Podd: Data Engineering Podcast

Build Your Data Transformations Faster And Safer With SDF

6 oktober 2024 | 43 min

Summary
In this episode of the Data Engineering Podcast Lukas Schulte, co-founder and CEO of SDF, explores the development and capabilities of this fast and expressive SQL transformation tool. From its origins as a solution for addressing data privacy, governance, and quality concerns in modern data management, to its unique features like static analysis and type correctness, Lucas dives into what sets SDF apart from other tools like DBT and SQL Mesh. Tune in for insights on building a business around a developer tool, the importance of community and user experience in the data engineering ecosystem, and plans for future development, including supporting Python models and enhancing execution capabilities.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
Your host is Tobias Macey and today I'm interviewing Lukas Schulte about SDF, a fast and expressive SQL transformation tool that understands your schema

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what SDF is and the story behind it?
- What's the story behind the name?
What problem are you solving with SDF?
- dbt has been the dominant player for SQL-based transformations for several years, with other notable competition in the form of SQLMesh. Can you give an overview of the venn diagram for features and functionality across SDF, dbt and SQLMesh?
Can you describe the design and implementation of SDF?
- How have the scope and goals of the project changed since you first started working on it?
What does the development experience look like for a team working with SDF?
- How does that differ between the open and paid versions of the product?
What are the features and functionality that SDF offers to address intra- and inter-team collaboration?
One of the challenges for any second-mover technology with an established competitor is the adoption/migration path for teams who have already invested in the incumbent (dbt in this case). How are you addressing that barrier for SDF?
- Beyond the core migration path of the direct functionality of the incumbent product is the amount of tooling and communal knowledge that grows up around that product. How are you thinking about that aspect of the current landscape?
What is your governing principle for what capabilities are in the open core and which go in the paid product?
What are the most interesting, innovative, or unexpected ways that you have seen SDF used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on SDF?
When is SDF the wrong choice?
What do you have planned for the future of SDF?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Scaling Airbyte: Challenges and Milestones on the Road to 1.0

23 september 2024 | 57 min

Summary
Airbyte is one of the most prominent platforms for data movement. Over the past 4 years they have invested heavily in solutions for scaling the self-hosted and cloud operations, as well as the quality and stability of their connectors. As a result of that hard work, they have declared their commitment to the future of the platform with a 1.0 release. In this episode Michel Tricot shares the highlights of their journey and the exciting new capabilities that are coming next.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Your host is Tobias Macey and today I'm interviewing Michel Tricot about the journey to the 1.0 launch of Airbyte and what that means for the project

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Airbyte is and the story behind it?
What are some of the notable milestones that you have traversed on your path to the 1.0 release?
The ecosystem has gone through some significant shifts since you first launched Airbyte. How have trends such as generative AI, the rise and fall of the "modern data stack", and the shifts in investment impacted your overall product and business strategies?
What are some of the hard-won lessons that you have learned about the realities of data movement and integration?
- What are some of the most interesting/challenging/surprising edge cases or performance bottlenecks that you have had to address?
What are the core architectural decisions that have proven to be effective?
- How has the architecture had to change as you progressed to the 1.0 release?
A 1.0 version signals a degree of stability and commitment. Can you describe the decision process that you went through in committing to a 1.0 version?
What are the most interesting, innovative, or unexpected ways that you have seen Airbyte used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airbyte?
When is Airbyte the wrong choice?
What do you have planned for the future of Airbyte after the 1.0 launch?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Enhancing Data Accessibility and Governance with Gravitino

1 september 2024 | 39 min

Summary
As data architectures become more elaborate and the number of applications of data increases, it becomes increasingly challenging to locate and access the underlying data. Gravitino was created to provide a single interface to locate and query your data. In this episode Junping Du explains how Gravitino works, the capabilities that it unlocks, and how it fits into your data platform.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Your host is Tobias Macey and today I'm interviewing Junping Du about Gravitino, an open source metadata service for a unified view of all of your schemas

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Gravitino is and the story behind it?
What problems are you solving with Gravitino?
- What are the methods that teams have relied on in the absence of Gravitino to address those use cases?
What led to the Hive Metastore being the default for so long?
- What are the opportunities for innovation and new functionality in the metadata service?
The documentation suggests that Gravitino has overlap with a number of tool categories such as table schema (Hive metastore), metadata repository (Open Metadata), data federation (Trino/Alluxio). What are the capabilities that it can completely replace, and which will require other systems for more comprehensive functionality?
What are the capabilities that you are explicitly keeping out of scope for Gravitino?
Can you describe the technical architecture of Gravitino?
- How have the design and scope evolved from when you first started working on it?
Can you describe how Gravitino integrates into an overall data platform?
- In a typical day, what are the different ways that a data engineer or data analyst might interact with Gravitino?
One of the features that you highlight is centralized permissions management. Can you describe the access control model that you use for unifying across underlying sources?
What are the most interesting, innovative, or unexpected ways that you have seen Gravitino used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gravitino?
When is Gravitino the wrong choice?
What do you have planned for the future of Gravitino?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

The Evolution of DataOps: Insights from DataKitchen's CEO

4 augusti 2024 | 54 min

Summary
In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Chris Berg, CEO of DataKitchen, to discuss his ongoing mission to simplify the lives of data engineers. Chris explains the challenges faced by data engineers, such as constant system failures, the need for rapid changes, and high customer demands. Chris delves into the concept of DataOps, its evolution, and the misappropriation of related terms like data mesh and data observability. He emphasizes the importance of focusing on processes and systems rather than just tools to improve data engineering workflows. Chris also introduces DataKitchen's open-source tools, DataOps TestGen and DataOps Observability, designed to automate data quality validation and monitor data journeys in production.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Chris Bergh about his tireless quest to simplify the lives of data engineers

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what DataKitchen is and the story behind it?
You helped to define and popularize "DataOps", which then went through a journey of misappropriation similar to "DevOps", and has since faded in use. What is your view on the realities of "DataOps" today?
Out of the popularized wave of "DataOps" tools came subsequent trends in data observability, data reliability engineering, etc. How have those cycles influenced the way that you think about the work that you are doing at DataKitchen?
The data ecosystem went through a massive growth period over the past ~7 years, and we are now entering a cycle of consolidation. What are the fundamental shifts that we have gone through as an industry in the management and application of data?
What are the challenges that never went away?
You recently open sourced the dataops-testgen and dataops-observability tools. What are the outcomes that you are trying to produce with those projects?
What are the areas of overlap with existing tools and what are the unique capabilities that you are offering?
Can you talk through the technical implementation of your new obserability and quality testing platform?
What does the onboarding and integration process look like?
Once a team has one or both tools set up, what are the typical points of interaction that they will have over the course of their workday?
What are the most interesting, innovative, or unexpected ways that you have seen dataops-observability/testgen used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on promoting DataOps?
What do you have planned for the future of your work at DataKitchen?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Achieving Data Reliability: The Role of Data Contracts in Modern Data Management

28 juli 2024 | 49 min

Summary
Data contracts are both an enforcement mechanism for data quality, and a promise to downstream consumers. In this episode Tom Baeyens returns to discuss the purpose and scope of data contracts, emphasizing their importance in achieving reliable analytical data and preventing issues before they arise. He explains how data contracts can be used to enforce guarantees and requirements, and how they fit into the broader context of data observability and quality monitoring. The discussion also covers the challenges and benefits of implementing data contracts, the organizational impact, and the potential for standardization in the field.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
At Outshift, the incubation engine from Cisco, they are driving innovation in AI, cloud, and quantum technologies with the powerful combination of enterprise strength and startup agility. Their latest innovation for the AI ecosystem is Motific, addressing a critical gap in going from prototype to production with generative AI. Motific is your vendor and model-agnostic platform for building safe, trustworthy, and cost-effective generative AI solutions in days instead of months. Motific provides easy integration with your organizational data, combined with advanced, customizable policy controls and observability to help ensure compliance throughout the entire process. Move beyond the constraints of traditional AI implementation and ensure your projects are launched quickly and with a firm foundation of trust and efficiency. Go to motific.ai today to learn more!
Your host is Tobias Macey and today I'm interviewing Tom Baeyens about using data contracts to build a clearer API for your data

Interview

Introduction
How did you get involved in the area of data management?
Can you describe the scope and purpose of data contracts in the context of this conversation?
In what way(s) do they differ from data quality/data observability?
Data contracts are also known as the API for data, can you elaborate on this?
What are the types of guarantees and requirements that you can enforce with these data contracts?
What are some examples of constraints or guarantees that cannot be represented in these contracts?
Are data contracts related to the shift-left?
Data contracts are also known as the API for data, can you elaborate on this?
The obvious application of data contracts are in the context of pipeline execution flows to prevent failing checks from propagating further in the data flow. What are some of the other ways that these contracts can be integrated into an organization's data ecosystem?
How did you approach the design of the syntax and implementation for Soda's data contracts?
Guarantees and constraints around data in different contexts have been implemented in numerous tools and systems. What are the areas of overlap in e.g. dbt, great expectations?
Are there any emerging standards or design patterns around data contracts/guarantees that will help encourage portability and integration across tooling/platform contexts?
What are the most interesting, innovative, or unexpected ways that you have seen data contracts used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data contracts at Soda?
When are data contracts the wrong choice?
What do you have planned for the future of data contracts?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

How Generative AI Is Impacting Data Engineering Teams

21 juli 2024 | 55 min

Summary
Generative AI has rapidly gained adoption for numerous use cases. To support those applications, organizational data platforms need to add new features and data teams have increased responsibility. In this episode Lior Gavish, co-founder of Monte Carlo, discusses the various ways that data teams are evolving to support AI powered features and how they are incorporating AI into their work.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Lior Gavish about the impact of AI on data engineers

Interview

Introduction
How did you get involved in the area of data management?
Can you start by clarifying what we are discussing when we say "AI"?
Previous generations of machine learning (e.g. deep learning, reinforcement learning, etc.) required new features in the data platform. What new demands is the current generation of AI introducing?
Generative AI also has the potential to be incorporated in the creation/execution of data pipelines. What are the risk/reward tradeoffs that you have seen in practice?
- What are the areas where LLMs have proven useful/effective in data engineering?
Vector embeddings have rapidly become a ubiquitous data format as a result of the growth in retrieval augmented generation (RAG) for AI applications. What are the end-to-end operational requirements to support this use case effectively?
- As with all data, the reliability and quality of the vectors will impact the viability of the AI application. What are the different failure modes/quality metrics/error conditions that they are subject to?
As much as vectors, vector databases, RAG, etc. seem exotic and new, it is all ultimately shades of the same work that we have been doing for years. What are the areas of overlap in the work required for running the current generation of AI, and what are the areas where it diverges?
- What new skills do data teams need to acquire to be effective in supporting AI applications?
What are the most interesting, innovative, or unexpected ways that you have seen AI impact data engineering teams?
What are the most interesting, unexpected, or challenging lessons that you have learned while working with the current generation of AI?
When is AI the wrong choice?
What are your predictions for the future impact of AI on data engineering teams?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

The Role of Product Managers in Data-Centric Organizations

13 juli 2024 | 53 min

Summary
In this episode Praveen Gujar, Director of Product at LinkedIn, talks about the intricacies of product management for data and analytical platforms. Praveen shares his journey from Amazon to Twitter and now LinkedIn, highlighting his extensive experience in building data products and platforms, digital advertising, AI, and cloud services. He discusses the evolving role of product managers in data-centric environments, emphasizing the importance of clean, reliable, and compliant data. Praveen also delves into the challenges of building scalable data platforms, the need for organizational and cultural alignment, and the critical role of product managers in bridging the gap between engineering and business teams. He provides insights into the complexities of platformization, the significance of long-term planning, and the necessity of having a strong relationship with engineering teams. The episode concludes with Praveen offering advice for aspiring product managers and discussing the future of data management in the context of AI and regulatory compliance.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Praveen Gujar about product management for data and analytical platforms

Interview

Introduction
How did you get involved in the area of data management?
Product management is typically thought of as being oriented toward customer facing functionality and features. What is involved in being a product manager for data systems?
Many data-oriented products that are customer facing require substantial technical capacity to serve those use cases. How does that influence the process of determining what features to provide/create?
investment in technical capacity/platforms
identifying groupings of features that can be served by a common platform investment
managing organizational pressures between engineering, product, business, finance, etc.
What are the most interesting, innovative, or unexpected ways that you have seen "Data Products & Platforms @ Big-tech" used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on "Building Data Products & Platforms for Big-tech"?
When is "Data Products & Platforms @ Big-tech" the wrong choice?
What do you have planned for the future of "Data Products & Platforms @ Big-tech"?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Neon: A Serverless And Developer Friendly Postgres

8 juli 2024 | 58 min

Summary
Postgres is one of the most widely respected and liked database engines ever. To make it even easier to use for developers to use, Nikita Shamgunov decided to makee it serverless, so that it can scale from zero to infinity. In this episode he explains the engineering involved to make that possible, as well as the numerous details that he and his team are packing into the Neon service to make it even more attractive for anyone who wants to build on top of Postgres.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Nikita Shamgunov about his work on making Postgres a serverless database at Neon.

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Neon is and the story behind it?
- The ecosystem around Postgres is large and varied. What are the pain points that you are trying to address with Neon?
What does it mean for a database to be serverless?
- What kinds of products and services are unlocked by making Postgres a serverless database?
How does your vision for Neon compare/contrast with what you know of PlanetScale?
Postgres is known for having a large ecosystem of plugins that add a lot of interesting and useful features, but the storage layer has not been as easily extensible historically. How have architectural changes in recent Postgres releases enabled your work on Neon?
What are the core pieces of engineering that you have had to complete to make Neon possible?
- How have the design and goals of the project evolved since you first started working on it?
The separation of storage and compute is one of the most fundamental promises of the cloud. What new capabilities does that enable in Postgres?
- How does the branching functionality change the ways that development teams are able to deliver and debug features?
Because the storage is now a networked system, what new performance/latency challenges does that introduce? How have you addressed them in Neon?
Anyone who has ever operated a Postgres instance has had to tackle the upgrade process. How does Neon address that process for end users?
The rampant growth of AI has touched almost every aspect of computing, and Postgres is no exception. How does the introduction of pgvector and semantic/similarity search functionality impact the adoption and usage patterns of Postgres/Neon?
- What new challenges does that introduce for you as an operator and business owner?
What are the lessons that you learned from MemSQL/SingleStore that have been most helpful in your work at Neon?
What are the most interesting, innovative, or unexpected ways that you have seen Neon used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Neon?
When is Neon the wrong choice? Postgres?
What do you have planned for the future of Neon?

Contact Info

@nikitabase on Twitter
LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Improve Data Quality Through Engineering Rigor And Business Engagement With Synq

30 juni 2024 | 60 min

Summary
This episode features an insightful conversation with Petr Janda, the CEO and founder of Synq. Petr shares his journey from being an engineer to founding Synq, emphasizing the importance of treating data systems with the same rigor as engineering systems. He discusses the challenges and solutions in data reliability, including the need for transparency and ownership in data systems. Synq's platform helps data teams manage incidents, understand data dependencies, and ensure data quality by providing insights and automation capabilities. Petr emphasizes the need for a holistic approach to data reliability, integrating data systems into broader business processes. He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Petr Janda about Synq, a data reliability platform focused on leveling up data teams by supporting a culture of engineering rigor

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Synq is and the story behind it?
- Data observability/reliability is a category that grew rapidly over the past ~5 years and has several vendors focused on different elements of the problem. What are the capabilities that you saw as lacking in the ecosystem which you are looking to address?
Operational/infrastructure engineers have spent the past decade honing their approach to incident management and uptime commitments. How do those concepts map to the responsibilities and workflows of data teams?
- Tooling only plays a small part in SLAs and incident management. How does Synq help to support the cultural transformation that is necessary?
What does an on-call rotation for a data engineer/data platform engineer look like as compared with an application-focused team?
How does the focus on data assets/data products shift your approach to observability as compared to a table/pipeline centric approach?
With the focus on sharing ownership beyond the boundaries on the data team there is a strong correlation with data governance principles. How do you see organizations incorporating Synq into their approach to data governance/compliance?
Can you describe how Synq is designed/implemented?
- How have the scope and goals of the product changed since you first started working on it?
For a team who is onboarding onto Synq, what are the steps required to get it integrated into their technology stack and workflows?
What are the types of incidents/errors that you are able to identify and alert on?
- What does a typical incident/error resolution process look like with Synq?
What are the most interesting, innovative, or unexpected ways that you have seen Synq used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Synq?
When is Synq the wrong choice?
What do you have planned for the future of Synq?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Stitching Together Enterprise Analytics With Microsoft Fabric

23 juni 2024 | 53 min

Being Data Driven At Stripe With Trino And Iceberg

16 juni 2024 | 53 min

X-Ray Vision For Your Flink Stream Processing With Datorios

9 juni 2024 | 42 min

Practical First Steps In Data Governance For Long Term Success

2 juni 2024 | 61 min

Data Migration Strategies For Large Scale Systems

27 maj 2024 | 60 min

Summary

Any software system that survives long enough will require some form of migration or evolution. When that system is responsible for the data layer the process becomes more challenging. Sriram Panyam has been involved in several projects that required migration of large volumes of data in high traffic environments. In this episode he shares some of the valuable lessons that he learned about how to make those projects successful.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support.
Your host is Tobias Macey and today I'm interviewing Sriram Panyam about his experiences conducting large scale data migrations and the useful strategies that he learned in the process

Interview

Introduction
How did you get involved in the area of data management?
Can you start by sharing some of your experiences with data migration projects?
- As you have gone through successive migration projects, how has that influenced the ways that you think about architecting data systems?
How would you categorize the different types and motivations of migrations?
- How does the motivation for a migration influence the ways that you plan for and execute that work?
Can you talk us through one or two specific projects that you have taken part in?
Part 1: The Triggers
- Section 1: Technical Limitations triggering Data Migration
  - Scaling bottlenecks: Performance issues with databases, storage, or network infrastructure
  - Legacy compatibility: Difficulties integrating with modern tools and cloud platforms
  - System upgrades: The need to migrate data during major software changes (e.g., SQL Server version upgrade)
- Section 2: Types of Migrations for Infrastructure Focus
  - Storage migration: Moving data between systems (HDD to SSD, SAN to NAS, etc.)
  - Data center migration: Physical relocation or consolidation of data centers
  - Virtualization migration: Moving from physical servers to virtual machines (or vice versa)
- Section 3: Technical Decisions Driving Data Migrations
  - End-of-life support: Forced migration when older software or hardware is sunsetted
  - Security and compliance: Adopting new platforms with better security postures
  - Cost Optimization: Potential savings of cloud vs. on-premise data centers
Part 2: Challenges (and Anxieties)
- Section 1: Technical Challenges
  - Data transformation challenges: Schema changes, complex data mappings
  - Network bandwidth and latency: Transferring large datasets efficiently
  - Performance testing and load balancing: Ensuring new systems can handle the workload
  - Live data consistency: Maintaining data integrity while updates occur in the source system
  - Minimizing Lag: Techniques to reduce delays in replicating changes to the new system
  - Change data capture: Identifying and tracking changes to the source system during migration
- Section 2: Operational Challenges
  - Minimizing downtime: Strategies for service continuity during migration
  - Change management and rollback plans: Dealing with unexpected issues
  - Technical skills and resources: In-house expertise/data teams/external help
- Section 3: Security & Compliance Challenges
  - Data encryption and protection: Methods for both in-transit and at-rest data
  - Meeting audit requirements: Documenting data lineage & the chain of custody
  - Managing access controls: Adjusting identity and role-based access to the new systems
Part 3: Patterns
- Section 1: Infrastructure Migration Strategies
  - Lift and shift: Migrating as-is vs. modernization and re-architecting during the move
  - Phased vs. big bang approaches: Tradeoffs in risk vs. disruption
  - Tools and automation: Using specialized software to streamline the process
  - Dual writes: Managing updates to both old and new systems for a time
  - Change data capture (CDC) methods: Log-based vs. trigger-based approaches for tracking changes
  - Data validation & reconciliation: Ensuring consistency between source and target
- Section 2: Maintaining Performance and Reliability
  - Disaster recovery planning: Failover mechanisms for the new environment
  - Monitoring and alerting: Proactively identifying and addressing issues
  - Capacity planning and forecasting growth to scale the new infrastructure
- Section 3: Data Consistency and Replication
  - Replication tools - strategies and specialized tooling
  - Data synchronization techniques, eg Pros and cons of different methods (incremental vs. full)
  - Testing/Verification Strategies for validating data correctness in a live environment
  - Implication of large scale systems/environments
  - Comparison of interesting strategies:
    - DBLog, Debezium, Databus, Goldengate etc
What are the most interesting, innovative, or unexpected approaches to data migrations that you have seen or participated in?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data migrations?
When is a migration the wrong choice?
What are the characteristics or features of data technologies and the overall ecosystem that can reduce the burden of data migration in the future?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.

Links

DagKnows
Google Cloud Dataflow
Seinfeld Risk Management
ACL == Access Control List
LinkedIn Databus - Change Data Capture
Espresso Storage
HDFS
Kafka
Postgres Replication Slots
Queueing Theory
Apache Beam
Debezium
Airbyte
[Fivetran](fivetran.com)
Designing Data Intensive Applications by Martin Kleppman (affiliate link)
Vector Databases
Pinecone
Weaviate
LAMP Stack
Netflix DBLog

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Support Data Engineering Podcast

Zenlytic Is Building You A Better Coworker With AI Agents

19 maj 2024 | 54 min

Release Management For Data Platform Services And Logic

12 maj 2024 | 20 min

Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach

5 maj 2024 | 54 min

Summary
Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human"

Interview

Introduction
How did you get involved in machine learning?
Can you start by unpacking the idea of "human-like" AI?
- How does that contrast with the conception of "AGI"?
The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment?
The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models?
What are the opportunities and limitations of causal modeling techniques for generalized AI models?
As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability?
What are the practical/architectural methods necessary to build more cognitive AI systems?
- How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications?
What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems?
When is cognitive AI the wrong choice?
What do you have planned for the future of cognitive AI applications at Aigo?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.

Links

Aigo.ai
Artificial General Intelligence
Cognitive AI
Knowledge Graph
Causal Modeling
Bayesian Statistics
Thinking Fast & Slow by Daniel Kahneman (affiliate link)
Agent-Based Modeling
Reinforcement Learning
DARPA 3 Waves of AI presentation
Why Don't We Have AGI Yet? whitepaper
Concepts Is All You Need Whitepaper
Hellen Keller
Stephen Hawking

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Build Your Second Brain One Piece At A Time

28 april 2024 | 50 min

Summary
Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Tsavo Knott about Pieces, a personal AI toolkit to improve the efficiency of developers

Interview

Introduction
How did you get involved in machine learning?
Can you describe what Pieces is and the story behind it?
The past few months have seen an endless series of personalized AI tools launched. What are the features and focus of Pieces that might encourage someone to use it over the alternatives?
model selections
architecture of Pieces application
local vs. hybrid vs. online models
model update/delivery process
data preparation/serving for models in context of Pieces app
application of AI to developer workflows
types of workflows that people are building with pieces
What are the most interesting, innovative, or unexpected ways that you have seen Pieces used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pieces?
When is Pieces the wrong choice?
What do you have planned for the future of Pieces?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Making Email Better With AI At Shortwave

21 april 2024 | 54 min

Designing A Non-Relational Database Engine

14 april 2024 | 76 min

Establish A Single Source Of Truth For Your Data Consumers With A Semantic Layer

7 april 2024 | 56 min

Adding Anomaly Detection And Observability To Your dbt Projects Is Elementary

31 mars 2024 | 51 min

Ship Smarter Not Harder With Declarative And Collaborative Data Orchestration On Dagster+

24 mars 2024 | 56 min

Reconciling The Data In Your Databases With Datafold

17 mars 2024 | 58 min

Version Your Data Lakehouse Like Your Software With Nessie

10 mars 2024 | 41 min

When And How To Conduct An AI Program

3 mars 2024 | 46 min

Find Out About The Technology Behind The Latest PFAD In Analytical Database Development

25 februari 2024 | 56 min

Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

18 februari 2024 | 59 min

Data Sharing Across Business And Platform Boundaries

11 februari 2024 | 60 min

Tackling Real Time Streaming Data With SQL Using RisingWave

4 februari 2024 | 57 min

Build A Data Lake For Your Security Logs With Scanner

29 januari 2024 | 63 min

Modern Customer Data Platform Principles

22 januari 2024 | 62 min

Pushing The Limits Of Scalability And User Experience For Data Processing WIth Jignesh Patel

7 januari 2024 | 50 min

Designing Data Platforms For Fintech Companies

1 januari 2024 | 48 min

Troubleshooting Kafka In Production

24 december 2023 | 75 min

Adding An Easy Mode For The Modern Data Stack With 5X

18 december 2023 | 56 min

Run Your Own Anomaly Detection For Your Critical Business Metrics With Anomstack

11 december 2023 | 51 min

Designing Data Transfer Systems That Scale

4 december 2023 | 64 min

Addressing The Challenges Of Component Integration In Data Platform Architectures

27 november 2023 | 30 min

Unlocking Your dbt Projects With Practical Advice For Practitioners

20 november 2023 | 76 min

Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine

13 november 2023 | 68 min

Shining Some Light In The Black Box Of PostgreSQL Performance

6 november 2023 | 55 min

Surveying The Market Of Database Products

30 oktober 2023 | 47 min

Defining A Strategy For Your Data Products

23 oktober 2023 | 64 min

Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

15 oktober 2023 | 68 min

Using Data To Illuminate The Intentionally Opaque Insurance Industry

9 oktober 2023 | 52 min

Building ETL Pipelines With Generative AI

1 oktober 2023 | 52 min

Powering Vector Search With Real Time And Incremental Vector Indexes

25 september 2023 | 59 min

Building Linked Data Products With JSON-LD

17 september 2023 | 62 min

An Overview Of The State Of Data Orchestration In An Increasingly Complex Data Ecosystem

10 september 2023 | 61 min

Eliminate The Overhead In Your Data Integration With The Open Source dlt Library

4 september 2023 | 42 min

Building An Internal Database As A Service Platform At Cloudflare

28 augusti 2023 | 61 min

Harnessing Generative AI For Creating Educational Content With Illumidesk

20 augusti 2023 | 55 min

Unpacking The Seven Principles Of Modern Data Pipelines

14 augusti 2023 | 47 min

Quantifying The Return On Investment For Your Data Team

6 augusti 2023 | 62 min

Summary

As businesses increasingly invest in technology and talent focused on data engineering and analytics, they want to know whether they are benefiting. So how do you calculate the return on investment for data? In this episode Barr Moses and Anna Filippova explore that question and provide useful exercises to start answering that in your company.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
Your host is Tobias Macey and today I'm interviewing Barr Moses and Anna Filippova about how and whether to measure the ROI of your data team

Interview

Introduction
How did you get involved in the area of data management?
What are the typical motivations for measuring and tracking the ROI for a data team?
- Who is responsible for collecting that information?
- How is that information used and by whom?
What are some of the downsides/risks of tracking this metric? (law of unintended consequences)
What are the inputs to the number that constitutes the "investment"? infrastructure, payroll of employees on team, time spent working with other teams?
What are the aspects of data work and its impact on the business that complicate a calculation of the "return" that is generated?
How should teams think about measuring data team ROI?
What are some concrete ROI metrics data teams can use?
- What level of detail is useful? What dimensions should be used for segmenting the calculations?
How can visibility into this ROI metric be best used to inform the priorities and project scopes of the team?
With so many tools in the modern data stack today, what is the role of technology in helping drive or measure this impact?
How do your respective solutions, Monte Carlo and dbt, help teams measure and scale data value?
With generative AI on the upswing of the hype cycle, what are the impacts that you see it having on data teams?
- What are the unrealistic expectations that it will produce?
- How can it speed up time to delivery?
What are the most interesting, innovative, or unexpected ways that you have seen data team ROI calculated and/or used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on measuring the ROI of data teams?
When is measuring ROI the wrong choice?

Contact Info

Barr
- LinkedIn
Anna
- LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Rudderstack: ![Rudderstack](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/CKNV8HZ6.png) Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)

Support Data Engineering Podcast

Strategies For A Successful Data Platform Migration

31 juli 2023 | 70 min

Build Real Time Applications With Operational Simplicity Using Dozer

24 juli 2023 | 41 min

Datapreneurs - How Todays Business Leaders Are Using Data To Define The Future

17 juli 2023 | 55 min

Reduce Friction In Your Business Analytics Through Entity Centric Data Modeling

9 juli 2023 | 73 min

How Data Engineering Teams Power Machine Learning With Feature Platforms

3 juli 2023 | 64 min

Summary

Feature engineering is a crucial aspect of the machine learning workflow. To make that possible, there are a number of technical and procedural capabilities that must be in place first. In this episode Razi Raziuddin shares how data engineering teams can support the machine learning workflow through the development and support of systems that empower data scientists and ML engineers to build and maintain their own features.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
Your host is Tobias Macey and today I'm interviewing Razi Raziuddin about how data engineers can empower data scientists to develop and deploy better ML models through feature engineering

Interview

Introduction
How did you get involved in the area of data management?
What is feature engineering is and why/to whom it matters?
- A topic that commonly comes up in relation to feature engineering is the importance of a feature store. What are the tradeoffs for that to be a separate infrastructure/architecture component?
What is the overall lifecycle of a feature, from definition to deployment and maintenance?
- How is this distinct from other forms of data pipeline development and delivery?
- Who are the participants in that workflow?
What are the sharp edges/roadblocks that typically manifest in that lifecycle?
What are the interfaces that are needed for data scientists/ML engineers to be able to self-serve their feature management?
- What is the role of the data engineer in supporting those interfaces?
- What are the communication/collaboration channels that are necessary to make the overall process a success?
From an implementation/architecture perspective, what are the patterns that you have seen teams build around for feature development/serving?
What are the most interesting, innovative, or unexpected ways that you have seen feature platforms used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on feature engineering?
What are the resources that you find most helpful in understanding and designing feature platforms?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Rudderstack: ![Rudderstack](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/CKNV8HZ6.png) Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)

Support Data Engineering Podcast

Seamless SQL And Python Transformations For Data Engineers And Analysts With SQLMesh

25 juni 2023 | 50 min

How Column-Aware Development Tooling Yields Better Data Models

18 juni 2023 | 46 min

Build Better Tests For Your dbt Projects With Datafold And data-diff

11 juni 2023 | 48 min

Reduce The Overhead In Your Pipelines With Agile Data Engine's DataOps Service

4 juni 2023 | 54 min

Summary

A significant portion of the time spent by data engineering teams is on managing the workflows and operations of their pipelines. DataOps has arisen as a parallel set of practices to that of DevOps teams as a means of reducing wasted effort. Agile Data Engine is a platform designed to handle the infrastructure side of the DataOps equation, as well as providing the insights that you need to manage the human side of the workflow. In this episode Tevje Olin explains how the platform is implemented, the features that it provides to reduce the amount of effort required to keep your pipelines running, and how you can start using it in your own team.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack
Your host is Tobias Macey and today I'm interviewing Tevje Olin about Agile Data Engine, a platform that combines data modeling, transformations, continuous delivery and workload orchestration to help you manage your data products and the whole lifecycle of your warehouse

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Agile Data Engine is and the story behind it?
What are some of the tools and architectures that an organization might be able to replace with Agile Data Engine?
- How does the unified experience of Agile Data Engine change the way that teams think about the lifecycle of their data?
- What are some of the types of experiments that are enabled by reduced operational overhead?
What does CI/CD look like for a data warehouse?
- How is it different from CI/CD for software applications?
Can you describe how Agile Data Engine is architected?
- How have the design and goals of the system changed since you first started working on it?
- What are the components that you needed to develop in-house to enable your platform goals?
What are the changes in the broader data ecosystem that have had the most influence on your product goals and customer adoption?
Can you describe the workflow for a team that is using Agile Data Engine to power their business analytics?
- What are some of the insights that you generate to help your customers understand how to improve their processes or identify new opportunities?
In your "about" page it mentions the unique approaches that you take for warehouse automation. How do your practices differ from the rest of the industry?
How have changes in the adoption/implementation of ML and AI impacted the ways that your customers exercise your platform?
What are the most interesting, innovative, or unexpected ways that you have seen the Agile Data Engine platform used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Agile Data Engine?
When is Agile Data Engine the wrong choice?
What do you have planned for the future of Agile Data Engine?

Guest Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

About Agile Data Engine

Agile Data Engine unlocks the potential of your data to drive business value - in a rapidly changing world.
Agile Data Engine is a DataOps Management platform for designing, deploying, operating and managing data products, and managing the whole lifecycle of a data warehouse. It combines data modeling, transformations, continuous delivery and workload orchestration into the same platform.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Rudderstack: ![Rudderstack](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/CKNV8HZ6.png) RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.

Support Data Engineering Podcast

A Roadmap To Bootstrapping The Data Team At Your Startup

29 maj 2023 | 43 min

Keep Your Data Lake Fresh With Real Time Streams Using Estuary

21 maj 2023 | 56 min

What Happens When The Abstractions Leak On Your Data

15 maj 2023 | 27 min

Use Consistent And Up To Date Customer Profiles To Power Your Business With Segment Unify

7 maj 2023 | 55 min

Summary

Every business has customers, and a critical element of success is understanding who they are and how they are using the companies products or services. The challenge is that most companies have a multitude of systems that contain fragments of the customer's interactions and stitching that together is complex and time consuming. Segment created the Unify product to reduce the burden of building a comprehensive view of customers and synchronizing it to all of the systems that need it. In this episode Kevin Niparko and Hanhan Wang share the details of how it is implemented and how you can use it to build and maintain rich customer profiles.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack
Your host is Tobias Macey and today I'm interviewing Kevin Niparko and Hanhan Wang about Segment's new Unify product for building and syncing comprehensive customer profiles across your data systems

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Segment Unify is and the story behind it?
What are the net-new capabilities that it brings to the Segment product suite?
What are some of the categories of attributes that need to be managed in a prototypical customer profile?
What are the different use cases that are enabled/simplified by the availability of a comprehensive customer profile?
- What is the potential impact of more detailed customer profiles on LTV?
How do you manage permissions/auditability of updating or amending profile data?
Can you describe how the Unify product is implemented?
- What are the technical challenges that you had to address while developing/launching this product?
What is the workflow for a team who is adopting the Unify product?
- What are the other Segment products that need to be in use to take advantage of Unify?
What are some of the most complex edge cases to address in identity resolution?
How does reverse ETL factor into the enrichment process for profile data?
What are some of the issues that you have to account for in synchronizing profiles across platforms/products?
- How do you mititgate the impact of "regression to the mean" for systems that don't support all of the attributes that you want to maintain in a profile record?
What are some of the data modeling considerations that you have had to account for to support e.g. historical changes (e.g. slowly changing dimensions)?
What are the most interesting, innovative, or unexpected ways that you have seen Segment Unify used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Segment Unify?
When is Segment Unify the wrong choice?
What do you have planned for the future of Segment Unify?

Contact Info

Kevin
- LinkedIn
- Blog
Hanhan
- LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Rudderstack: ![Rudderstack](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/CKNV8HZ6.png) RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.

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Realtime Data Applications Made Easier With Meroxa

24 april 2023 | 45 min

Building Self Serve Business Intelligence With AI And Semantic Modeling At Zenlytic

16 april 2023 | 49 min

Summary

Business intellingence has been chasing the promise of self-serve data for decades. As the capabilities of these systems has improved and become more accessible, the target of what self-serve means changes. With the availability of AI powered by large language models combined with the evolution of semantic layers, the team at Zenlytic have taken aim at this problem again. In this episode Paul Blankley and Ryan Janssen explore the power of natural language driven data exploration combined with semantic modeling that enables an intuitive way for everyone in the business to access the data that they need to succeed in their work.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack
Your host is Tobias Macey and today I'm interviewing Paul Blankley and Ryan Janssen about Zenlytic, a no-code business intelligence tool focused on emerging commerce brands

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Zenlytic is and the story behind it?
Business intelligence is a crowded market. What was your process for defining the problem you are focused on solving and the method to achieve that outcome?
Self-serve data exploration has been attempted in myriad ways over successive generations of BI and data platforms. What are the barriers that have been the most challenging to overcome in that effort?
- What are the elements that are coming together now that give you confidence in being able to deliver on that?
Can you describe how Zenlytic is implemented?
- What are the evolutions in the understanding and implementation of semantic layers that provide a sufficient substrate for operating on?
- How have the recent breakthroughs in large language models (LLMs) improved your ability to build features in Zenlytic?
- What is your process for adding domain semantics to the operational aspect of your LLM?
For someone using Zenlytic, what is the process for getting it set up and integrated with their data?
Once it is operational, can you describe some typical workflows for using Zenlytic in a business context?
- Who are the target users?
- What are the collaboration options available?
What are the most complex engineering/data challenges that you have had to address in building Zenlytic?
What are the most interesting, innovative, or unexpected ways that you have seen Zenlytic used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Zenlytic?
When is Zenlytic the wrong choice?
What do you have planned for the future of Zenlytic?

Contact Info

Paul Blankley (LinkedIn)

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Rudderstack: ![Rudderstack](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/CKNV8HZ6.png) RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.

Support Data Engineering Podcast

An Exploration Of The Composable Customer Data Platform

10 april 2023 | 72 min

Mapping The Data Infrastructure Landscape As A Venture Capitalist

3 april 2023 | 62 min

Unlocking The Potential Of Streaming Data Applications Without The Operational Headache At Grainite

25 mars 2023 | 74 min

Aligning Data Security With Business Productivity To Deploy Analytics Safely And At Speed

19 mars 2023 | 52 min

Use Your Data Warehouse To Power Your Product Analytics With NetSpring

10 mars 2023 | 49 min

Exploring The Nuances Of Building An Intentional Data Culture

6 mars 2023 | 46 min

Summary

The ecosystem for data professionals has matured to the point that there are a large and growing number of distinct roles. With the scope and importance of data steadily increasing it is important for organizations to ensure that everyone is aligned and operating in a positive environment. To help facilitate the nascent conversation about what constitutes an effective and productive data culture, the team at Data Council have dedicated an entire conference track to the subject. In this episode Pete Soderling and Maggie Hays join the show to explore this topic and their experience preparing for the upcoming conference.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender where you can do two things: watch us build a data estate in 15 minutes and start for free today.
Your host is Tobias Macey and today I'm interviewing Pete Soderling and Maggie Hays about the growing importance of establishing and investing in an organization's data culture and their experience forming an entire conference track around this topic

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what your working definition of "Data Culture" is?
- In what ways is a data culture distinct from an organization's corporate culture? How are they interdependent?
- What are the elements that are most impactful in forming the data culture of an organization?
What are some of the motivations that teams/companies might have in fighting against the creation and support of an explicit data culture?
- Are there any strategies that you have found helpful in counteracting those tendencies?
In terms of the conference, what are the factors that you consider when deciding how to group the different presentations into tracks or themes?
- What are the experiences that you have had personally and in community interactions that led you to elevate data culture to be it's own track?
What are the broad challenges that practitioners are facing as they develop their own understanding of what constitutes a healthy and productive data culture?
What are some of the risks that you considered when forming this track and evaluating proposals?
What are your criteria for determining whether this track is successful?
What are the most interesting, innovative, or unexpected aspects of data culture that you have encountered through developing this track?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on selecting presentations for this year's event?
What do you have planned for the future of this topic at Data Council events?

Contact Info

Pete
- @petesoder on Twitter
- LinkedIn
Maggie
- LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Data Council
- Podcast Episode
Data Community Fund
DataHub
- Podcast Episode
Database Design For Mere Mortals by Michael J. Hernandez (affiliate link)
SOAP
REST
Econometrics
DBA == Database Administrator
Conway's Law
dbt
- Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

TimeXtender: ![TimeXtender Logo](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/35MYWp0I.png) TimeXtender is a holistic, metadata-driven solution for data integration, optimized for agility. TimeXtender provides all the features you need to build a future-proof infrastructure for ingesting, transforming, modelling, and delivering clean, reliable data in the fastest, most efficient way possible. You can't optimize for everything all at once. That's why we take a holistic approach to data integration that optimises for agility instead of fragmentation. By unifying each layer of the data stack, TimeXtender empowers you to build data solutions 10x faster while reducing costs by 70%-80%. We do this for one simple reason: because time matters. Go to [dataengineeringpodcast.com/timextender](https://www.dataengineeringpodcast.com/timextender) today to get started for free!

Support Data Engineering Podcast

Building A Data Mesh Platform At PayPal

27 februari 2023 | 47 min

The View Below The Waterline Of Apache Iceberg And How It Fits In Your Data Lakehouse

19 februari 2023 | 55 min

Summary

Cloud data warehouses have unlocked a massive amount of innovation and investment in data applications, but they are still inherently limiting. Because of their complete ownership of your data they constrain the possibilities of what data you can store and how it can be used. Projects like Apache Iceberg provide a viable alternative in the form of data lakehouses that provide the scalability and flexibility of data lakes, combined with the ease of use and performance of data warehouses. Ryan Blue helped create the Iceberg project, and in this episode he rejoins the show to discuss how it has evolved and what he is doing in his new business Tabular to make it even easier to implement and maintain.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to timextender.com/dataengineering where you can do two things: watch us build a data estate in 15 minutes and start for free today.
Your host is Tobias Macey and today I'm interviewing Ryan Blue about the evolution and applications of the Iceberg table format and how he is making it more accessible at Tabular

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Iceberg is and its position in the data lake/lakehouse ecosystem?
- Since it is a fundamentally a specification, how do you manage compatibility and consistency across implementations?
What are the notable changes in the Iceberg project and its role in the ecosystem since our last conversation October of 2018?
Around the time that Iceberg was first created at Netflix a number of alternative table formats were also being developed. What are the characteristics of Iceberg that lead teams to adopt it for their lakehouse projects?
- Given the constant evolution of the various table formats it can be difficult to determine an up-to-date comparison of their features, particularly earlier in their development. What are the aspects of this problem space that make it so challenging to establish unbiased and comprehensive comparisons?
For someone who wants to manage their data in Iceberg tables, what does the implementation look like?
- How does that change based on the type of query/processing engine being used?
Once a table has been created, what are the capabilities of Iceberg that help to support ongoing use and maintenance?
What are the most interesting, innovative, or unexpected ways that you have seen Iceberg used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Iceberg/Tabular?
When is Iceberg/Tabular the wrong choice?
What do you have planned for the future of Iceberg/Tabular?

Contact Info

LinkedIn
rdblue on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Acryl: ![Acryl](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/2E3zCRd4.png) The modern data stack needs a reimagined metadata management platform. Acryl Data’s vision is to bring clarity to your data through its next generation multi-cloud metadata management platform. Founded by the leaders that created projects like LinkedIn DataHub and Airbnb Dataportal, Acryl Data enables delightful search and discovery, data observability, and federated governance across data ecosystems. Signup for the SaaS product today at [dataengineeringpodcast.com/acryl](https://www.dataengineeringpodcast.com/acryl)

Support Data Engineering Podcast

Let The Whole Team Participate In Data With The Quilt Versioned Data Hub

11 februari 2023 | 52 min

Reflecting On The Past 6 Years Of Data Engineering

6 februari 2023 | 32 min

Let Your Business Intelligence Platform Build The Models Automatically With Omni Analytics

30 januari 2023 | 51 min

Safely Test Your Applications And Analytics With Production Quality Data Using Tonic AI

22 januari 2023 | 46 min

Building Applications With Data As Code On The DataOS

16 januari 2023 | 49 min

Automate Your Pipeline Creation For Streaming Data Transformations With SQLake

8 januari 2023 | 44 min

Summary

Managing end-to-end data flows becomes complex and unwieldy as the scale of data and its variety of applications in an organization grows. Part of this complexity is due to the transformation and orchestration of data living in disparate systems. The team at Upsolver is taking aim at this problem with the latest iteration of their platform in the form of SQLake. In this episode Ori Rafael explains how they are automating the creation and scheduling of orchestration flows and their related transforations in a unified SQL interface.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data and analytics leaders, 2023 is your year to sharpen your leadership skills, refine your strategies and lead with purpose. Join your peers at Gartner Data & Analytics Summit, March 20 – 22 in Orlando, FL for 3 days of expert guidance, peer networking and collaboration. Listeners can save $375 off standard rates with code GARTNERDA. Go to dataengineeringpodcast.com/gartnerda today to find out more.
Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions!
Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more.
Your host is Tobias Macey and today I'm interviewing Ori Rafael about the SQLake feature for the Upsolver platform that automatically generates pipelines from your queries

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what the SQLake product is and the story behind it?
- What is the core problem that you are trying to solve?
What are some of the anti-patterns that you have seen teams adopt when designing and implementing DAGs in a tool such as Airlow?
What are the benefits of merging the logic for transformation and orchestration into the same interface and dialect (SQL)?
Can you describe the technical implementation of the SQLake feature?
What does the workflow look like for designing and deploying pipelines in SQLake?
What are the opportunities for using utilities such as dbt for managing logical complexity as the number of pipelines scales?
- SQL has traditionally been challenging to compose. How did that factor into your design process for how to structure the dialect extensions for job scheduling?
What are some of the complexities that you have had to address in your orchestration system to be able to manage timeliness of operations as volume and complexity of the data scales?
What are some of the edge cases that you have had to provide escape hatches for?
What are the most interesting, innovative, or unexpected ways that you have seen SQLake used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLake?
When is SQLake the wrong choice?
What do you have planned for the future of SQLake?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering.

Om podden

Avsnitt

Build Your Data Transformations Faster And Safer With SDF

Scaling Airbyte: Challenges and Milestones on the Road to 1.0

Enhancing Data Accessibility and Governance with Gravitino

The Evolution of DataOps: Insights from DataKitchen's CEO

Achieving Data Reliability: The Role of Data Contracts in Modern Data Management

How Generative AI Is Impacting Data Engineering Teams

The Role of Product Managers in Data-Centric Organizations

Neon: A Serverless And Developer Friendly Postgres

Improve Data Quality Through Engineering Rigor And Business Engagement With Synq

Stitching Together Enterprise Analytics With Microsoft Fabric

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links

Being Data Driven At Stripe With Trino And Iceberg

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links

X-Ray Vision For Your Flink Stream Processing With Datorios

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links

Practical First Steps In Data Governance For Long Term Success

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links

Data Migration Strategies For Large Scale Systems

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links

Zenlytic Is Building You A Better Coworker With AI Agents

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links

Release Management For Data Platform Services And Logic

Summary

Announcements

Interview

Contact Info

Closing Announcements

Links

Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach

Build Your Second Brain One Piece At A Time

Making Email Better With AI At Shortwave

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links

Designing A Non-Relational Database Engine

Summary