Accelerated Computing in Modern Data Centers With Datapelago

Data Engineering Podcast

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You Can Visit All Seven Continents. But Should You? 26:46

14 days ago26:46

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26:46

For many travelers, Antarctica is a bucket-list destination, a once-in-a-lifetime opportunity to touch all seven continents. In 2023, a record-breaking 100,000 tourists made the trip. But the journey begs a fundamental question: What do we risk by traveling to a place that is supposed to be uninhabited by humans? And as the climate warms, should we really be going to Antarctica in the first place? SHOW NOTES: Kara Weller: The Impossible Dilemma of a Polar Guide Marilyn Raphael: A twenty-first century structural change in Antarctica’s sea ice system Karl Watson: First Time in Antarctica Jeb Brooks : 7 Days in Antarctica (Journey to the South Pole) Metallica - Freeze 'Em All: Live in Antarctica Learn about your ad choices: dovetail.prx.org/ad-choices…

about a year ago 55:36

MP3•Laman utama episod

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.
Your host is Tobias Macey and today I'm interviewing Rajan Goyal about how to drastically improve efficiencies in data processing by re-imagining the system architecture

Interview

Introduction
How did you get involved in the area of data management?
Can you start by outlining the main factors that contribute to performance challenges in data lake environments?
The different components of open data processing systems have evolved from different starting points with different objectives. In your experience, how has that un-planned and un-synchronized evolution of the ecosystem hindered the capabilities and adoption of open technologies?
The introduction of a new cross-cutting capability (e.g. Iceberg) has typically taken a substantial amount of time to gain support across different engines and ecosystems. What do you see as the point of highest leverage to improve the capabilities of the entire stack with the least amount of co-ordination?
What was the motivating insight that led you to invest in the technology that powers Datapelago?
Can you describe the system design of Datapelago and how it integrates with existing data engines?
The growth in the generation and application of unstructured data is a notable shift in the work being done by data teams. What are the areas of overlap in the fundamental nature of data (whether structured, semi-structured, or unstructured) that you are able to exploit to bridge the processing gap?
What are the most interesting, innovative, or unexpected ways that you have seen Datapelago used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datapelago?
When is Datapelago the wrong choice?
What do you have planned for the future of Datapelago?

Contact Info

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

462 episod

#Tech #Tobias Macey #Data Engineering #Big Data #Database #Data Pipeline #Podcasting Education

Accelerated Computing in Modern Data Centers With Datapelago

Data Engineering Podcast

68 subscribers

published about a year ago

Kongsi

MP3•Laman utama episod

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.
Your host is Tobias Macey and today I'm interviewing Rajan Goyal about how to drastically improve efficiencies in data processing by re-imagining the system architecture

Interview

Introduction
How did you get involved in the area of data management?
Can you start by outlining the main factors that contribute to performance challenges in data lake environments?
The different components of open data processing systems have evolved from different starting points with different objectives. In your experience, how has that un-planned and un-synchronized evolution of the ecosystem hindered the capabilities and adoption of open technologies?
The introduction of a new cross-cutting capability (e.g. Iceberg) has typically taken a substantial amount of time to gain support across different engines and ecosystems. What do you see as the point of highest leverage to improve the capabilities of the entire stack with the least amount of co-ordination?
What was the motivating insight that led you to invest in the technology that powers Datapelago?
Can you describe the system design of Datapelago and how it integrates with existing data engines?
The growth in the generation and application of unstructured data is a notable shift in the work being done by data teams. What are the areas of overlap in the fundamental nature of data (whether structured, semi-structured, or unstructured) that you are able to exploit to bridge the processing gap?
What are the most interesting, innovative, or unexpected ways that you have seen Datapelago used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datapelago?
When is Datapelago the wrong choice?
What do you have planned for the future of Datapelago?

Contact Info

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

462 episod

#Tech #Tobias Macey #Data Engineering #Big Data #Database #Data Pipeline #Podcasting Education

Semua episod

1
Advanced Lakehouse Management With The LakeKeeper Iceberg REST Catalog 57:13

1 day ago57:13

57:13

Summary In this episode of the Data Engineering Podcast Victor Kessler, co-founder of Vakama, talks about the architectural patterns in the lake house enabled by a fast and feature-rich Iceberg catalog. Victor shares his journey from data warehouses to developing the open-source project, Lakekeeper, an Apache Iceberg REST catalog written in Rust that facilitates building lake houses with essential components like storage, compute, and catalog management. He discusses the importance of metadata in making data actionable, the evolution of data catalogs, and the challenges and innovations in the space, including integration with OpenFGA for fine-grained access control and managing data across formats and compute engines. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Viktor Kessler about architectural patterns in the lakehouse that are unlocked by a fast and feature-rich Iceberg catalog Interview Introduction How did you get involved in the area of data management? Can you describe what LakeKeeper is and the story behind it? What is the core of the problem that you are addressing? There has been a lot of activity in the catalog space recently. What are the driving forces that have highlighted the need for a better metadata catalog in the data lake/distributed data ecosystem? How would you characterize the feature sets/problem spaces that different entrants are focused on addressing? Iceberg as a table format has gained a lot of attention and adoption across the data ecosystem. The REST catalog format has opened the door for numerous implementations. What are the opportunities for innovation and improving user experience in that space? What is the role of the catalog in managing security and governance? (AuthZ, auditing, etc.) What are the channels for propagating identity and permissions to compute engines? (how do you avoid head-scratching about permission denied situations) Can you describe how LakeKeeper is implemented? How have the design and goals of the project changed since you first started working on it? For someone who has an existing set of Iceberg tables and catalog, what does the migration process look like? What new workflows or capabilities does LakeKeeper enable for data teams using Iceberg tables across one or more compute frameworks? What are the most interesting, innovative, or unexpected ways that you have seen LakeKeeper used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on LakeKeeper? When is LakeKeeper the wrong choice? What do you have planned for the future of LakeKeeper? 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 hosts@dataengineeringpodcast.com with your story. Links LakeKeeper SAP Microsoft Access Microsoft Excel Apache Iceberg Podcast Episode Iceberg REST Catalog PyIceberg Spark Trino Dremio Hive Metastore Hadoop NATS Polars DuckDB Podcast Episode DataFusion Atlan Podcast Episode Open Metadata Podcast Episode Apache Atlas OpenFGA Hudi Podcast Episode Delta Lake Podcast Episode Lance Table Format Podcast Episode Unity Catalog Polaris Catalog Apache Gravitino Podcast Episode Keycloak Open Policy Agent (OPA) Apache Ranger Apache NiFi The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Simplifying Data Pipelines with Durable Execution 39:49

9 days ago39:49

39:49

Summary In this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Jeremy Edberg about durable execution and how it influences the design and implementation of business logic Interview Introduction How did you get involved in the area of data management? Can you describe what DBOS is and the story behind it? What is durable execution? What are some of the notable ways that inclusion of durable execution in an application architecture changes the ways that the rest of the application is implemented? (e.g. error handling, logic flow, etc.) Many data pipelines involve complex, multi-step workflows. How does DBOS simplify the creation and management of resilient data pipelines? How does durable execution impact the operational complexity of data management systems? One of the complexities in durable execution is managing code/data changes to workflows while existing executions are still processing. What are some of the useful patterns for addressing that challenge and how does DBOS help? Can you describe how DBOS is architected? How have the design and goals of the system changed since you first started working on it? What are the characteristics of Postgres that make it suitable for the persistence mechanism of DBOS? What are the guiding principles that you rely on to determine the boundaries between the open source and commercial elements of DBOS? What are the most interesting, innovative, or unexpected ways that you have seen DBOS used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on DBOS? When is DBOS the wrong choice? What do you have planned for the future of DBOS? 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 hosts@dataengineeringpodcast.com with your story. Links DBOS Exactly Once Semantics Temporal Sempahore Postgres DBOS Transact Python Typescript Idempotency Keys Agentic AI State Machine YugabyteDB Podcast Episode CockroachDB Supabase Neon Podcast Episode Airflow The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Overcoming Redis Limitations: The Dragonfly DB Approach 43:58

22 days ago43:58

43:58

Summary In this episode of the Data Engineering Podcast Roman Gershman, CTO and founder of Dragonfly DB, explores the development and impact of high-speed in-memory databases. Roman shares his experience creating a more efficient alternative to Redis, focusing on performance gains, scalability, and cost efficiency, while addressing limitations such as high throughput and low latency scenarios. He explains how Dragonfly DB solves operational complexities for users and delves into its technical aspects, including maintaining compatibility with Redis while innovating on memory efficiency. Roman discusses the importance of cost efficiency and operational simplicity in driving adoption and shares insights on the broader ecosystem of in-memory data stores, future directions like SSD tiering and vector search capabilities, and the lessons learned from building a new database engine. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Roman Gershman about building a high-speed in-memory database and the impact of the performance gains on data applications Interview Introduction How did you get involved in the area of data management? Can you describe what DragonflyDB is and the story behind it? What is the core problem/use case that is solved by making a "faster Redis"? The other major player in the high performance key/value database space is Aerospike. What are the heuristics that an engineer should use to determine whether to use that vs. Dragonfly/Redis? Common use cases for Redis involve application caches and queueing (e.g. Celery/RQ). What are some of the other applications that you have seen Redis/Dragonfly used for, particularly in data engineering use cases? There is a piece of tribal wisdom that it takes 10 years for a database to iron out all of the kinks. At the same time, there have been substantial investments in commoditizing the underlying components of database engines. Can you describe how you approached the implementation of DragonflyDB to arive at a functional and reliable implementation? What are the architectural elements that contribute to the performance and scalability benefits of Dragonfly? How have the design and goals of the system changed since you first started working on it? For teams who migrate from Redis to Dragonfly, beyond the cost savings what are some of the ways that it changes the ways that they think about their overall system design? What are the most interesting, innovative, or unexpected ways that you have seen Dragonfly used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on DragonflyDB? When is DragonflyDB the wrong choice? What do you have planned for the future of DragonflyDB? Contact Info GitHub 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 hosts@dataengineeringpodcast.com with your story. Links DragonflyDB Redis Elasticache ValKey Aerospike Laravel Sidekiq Celery Seastar Framework Shared-Nothing Architecture io_uring midi-redis Dunning-Kruger Effect Rust The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Bringing AI Into The Inner Loop of Data Engineering With Ascend 52:47

29 days ago52:47

52:47

Summary In this episode of the Data Engineering Podcast Sean Knapp, CEO of Ascend.io, explores the intersection of AI and data engineering. He discusses the evolution of data engineering and the role of AI in automating processes, alleviating burdens on data engineers, and enabling them to focus on complex tasks and innovation. The conversation covers the challenges and opportunities presented by AI, including the need for intelligent tooling and its potential to streamline data engineering processes. Sean and Tobias also delve into the impact of generative AI on data engineering, highlighting its ability to accelerate development, improve governance, and enhance productivity, while also noting the current limitations and future potential of AI in the field. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Sean Knapp about how Ascend is incorporating AI into their platform to help you keep up with the rapid rate of change Interview Introduction How did you get involved in the area of data management? Can you describe what Ascend is and the story behind it? The last time we spoke was August of 2022 . What are the most notable or interesting evolutions in your platform since then? In that same time "AI" has taken up all of the oxygen in the data ecosystem. How has that impacted the ways that you and your customers think about their priorities? The introduction of AI as an API has caused many organizations to try and leap-frog their data maturity journey and jump straight to building with advanced capabilities. How is that impacting the pressures and priorities felt by data teams? At the same time that AI-focused product goals are straining data teams capacities, AI also has the potential to act as an accelerator to their work. What are the roadblocks/speedbumps that are in the way of that capability? Many data teams are incorporating AI tools into parts of their workflow, but it can be clunky and cumbersome. How are you thinking about the fundamental changes in how your platform works with AI at its center? Can you describe the technical architecture that you have evolved toward that allows for AI to drive the experience rather than being a bolt-on? What are the concrete impacts that these new capabilities have on teams who are using Ascend? What are the most interesting, innovative, or unexpected ways that you have seen Ascend + AI used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on incorporating AI into the core of Ascend? When is Ascend the wrong choice? What do you have planned for the future of AI in Ascend? 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 hosts@dataengineeringpodcast.com with your story. Links Ascend Cursor AI Code Editor Devin GitHub Copilot OpenAI DeepResearch S3 Tables AWS Glue AWS Bedrock Snowpark Co-Intelligence : Living and Working with AI by Ethan Mollick (affiliate link) OpenAI o3 The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Astronomer's Role in the Airflow Ecosystem: A Deep Dive with Pete DeJoy 51:41

5 weeks ago51:41

51:41

Summary In this episode of the Data Engineering Podcast Pete DeJoy, co-founder and product lead at Astronomer, talks about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3. Pete shares his journey into data engineering, discusses Astronomer's contributions to the Airflow project, and highlights the critical role of Airflow in powering operational data products. He covers the evolution of Airflow, its position in the data ecosystem, and the challenges faced by data engineers, including infrastructure management and observability. The conversation also touches on the upcoming Airflow 3 release, which introduces data awareness, architectural improvements, and multi-language support, and Astronomer's observability suite, Astro Observe, which provides insights and proactive recommendations for Airflow users. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Pete DeJoy about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3 Interview Introduction Can you describe what Astronomer is and the story behind it? How would you characterize the relationship between Airflow and Astronomer? Astronomer just released your State of Airflow 2025 Report yesterday and it is the largest data engineering survey ever with over 5,000 respondents. Can you talk a bit about top level findings in the report? What about the overall growth of the Airflow project over time? How have the focus and features of Astronomer changed since it was last featured on the show in 2017? Astro Observe GA’d in early February, what does the addition of pipeline observability mean for your customers? What are other capabilities similar in scope to observability that Astronomer is looking at adding to the platform? Why is Airflow so critical in providing an elevated Observability–or cataloging, or something simlar - experience in a DataOps platform? What are the notable evolutions in the Airflow project and ecosystem in that time? What are the core improvements that are planned for Airflow 3.0? What are the most interesting, innovative, or unexpected ways that you have seen Astro used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airflow and Astro? What do you have planned for the future of Astro/Astronomer/Airflow? 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 hosts@dataengineeringpodcast.com with your story. Links Astronomer Airflow Maxime Beauchemin MongoDB Databricks Confluent Spark Kafka Dagster Podcast Episode Prefect Airflow 3 The Rise of the Data Engineer blog post dbt Jupyter Notebook Zapier cosmos library for dbt in Airflow Ruff Airflow Custom Operator Snowflake The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Accelerated Computing in Modern Data Centers With Datapelago 55:36

6 weeks ago55:36

55:36

Summary In this episode of the Data Engineering Podcast Rajan Goyal, CEO and co-founder of Datapelago, talks about improving efficiencies in data processing by reimagining system architecture. Rajan explains the shift from hyperconverged to disaggregated and composable infrastructure, highlighting the importance of accelerated computing in modern data centers. He discusses the evolution from proprietary to open, composable stacks, emphasizing the role of open table formats and the need for a universal data processing engine, and outlines Datapelago's strategy to leverage existing frameworks like Spark and Trino while providing accelerated computing benefits. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Rajan Goyal about how to drastically improve efficiencies in data processing by re-imagining the system architecture Interview Introduction How did you get involved in the area of data management? Can you start by outlining the main factors that contribute to performance challenges in data lake environments? The different components of open data processing systems have evolved from different starting points with different objectives. In your experience, how has that un-planned and un-synchronized evolution of the ecosystem hindered the capabilities and adoption of open technologies? The introduction of a new cross-cutting capability (e.g. Iceberg) has typically taken a substantial amount of time to gain support across different engines and ecosystems. What do you see as the point of highest leverage to improve the capabilities of the entire stack with the least amount of co-ordination? What was the motivating insight that led you to invest in the technology that powers Datapelago? Can you describe the system design of Datapelago and how it integrates with existing data engines? The growth in the generation and application of unstructured data is a notable shift in the work being done by data teams. What are the areas of overlap in the fundamental nature of data (whether structured, semi-structured, or unstructured) that you are able to exploit to bridge the processing gap? What are the most interesting, innovative, or unexpected ways that you have seen Datapelago used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datapelago? When is Datapelago the wrong choice? What do you have planned for the future of Datapelago? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Datapelago MIPS Architecture ARM Architecture AWS Nitro Mellanox Nvidia Von Neumann Architecture TPU == Tensor Processing Unit FPGA == Field-Programmable Gate Array Spark Trino Iceberg Podcast Episode Delta Lake Podcast Episode Hudi Podcast Episode Apache Gluten Intermediate Representation Turing Completeness LLVM Amdahl's Law LSTM == Long Short-Term Memory The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
The Future of Data Engineering: AI, LLMs, and Automation 59:39

8 weeks ago59:39

59:39

Summary In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large language models (LLMs) to enhance productivity and reduce manual toil. The conversation covers the potential of AI to transform data engineering tasks, such as text-to-SQL interfaces and creating semantic graphs to improve data accessibility, and explores practical applications of LLMs in automating code reviews, testing, and understanding data lineage. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy about Interview Introduction How did you get involved in the area of data management? modern data stack is dead where is AI in the data stack? "buy our tool to ship AI" opportunities for LLM in DE workflow 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 hosts@dataengineeringpodcast.com with your story. Links Datafold Copilot Cursor IDE AI Agents DataChat AI Engineering Podcast Episode Metrics Layer Emacs LangChain LangGraph CrewAI The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Evolving Responsibilities in AI Data Management 38:57

9 weeks ago38:57

38:57

Summary In this episode of the Data Engineering Podcast Bartosz Mikulski talks about preparing data for AI applications. Bartosz shares his journey from data engineering to MLOps and emphasizes the importance of data testing over software development in AI contexts. He discusses the types of data assets required for AI applications, including extensive test datasets, especially in generative AI, and explains the differences in data requirements for various AI application styles. The conversation also explores the skills data engineers need to transition into AI, such as familiarity with vector databases and new data modeling strategies, and highlights the challenges of evolving AI applications, including frequent reprocessing of data when changing chunking strategies or embedding models. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Bartosz Mikulski about how to prepare data for use in AI applications Interview Introduction How did you get involved in the area of data management? Can you start by outlining some of the main categories of data assets that are needed for AI applications? How does the nature of the application change those requirements? (e.g. RAG app vs. agent, etc.) How do the different assets map to the stages of the application lifecycle? What are some of the common roles and divisions of responsibility that you see in the construction and operation of a "typical" AI application? For data engineers who are used to data warehousing/BI, what are the skills that map to AI apps? What are some of the data modeling patterns that are needed to support AI apps? chunking strategies metadata management What are the new categories of data that data engineers need to manage in the context of AI applications? agent memory generation/evolution conversation history management data collection for fine tuning What are some of the notable evolutions in the space of AI applications and their patterns that have happened in the past ~1-2 years that relate to the responsibilities of data engineers? What are some of the skills gaps that teams should be aware of and identify training opportunities for? What are the most interesting, innovative, or unexpected ways that you have seen data teams address the needs of AI applications? What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI applications and their reliance on data? What are some of the emerging trends that you are paying particular attention to? Contact Info Website 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 hosts@dataengineeringpodcast.com with your story. Links Spark Ray Chunking Strategies Hypothetical document embeddings Model Fine Tuning Prompt Compression The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
CSVs Will Never Die And OneSchema Is Counting On It 54:40

14 weeks ago54:40

54:40

Summary In this episode of the Data Engineering Podcast Andrew Luo, CEO of OneSchema, talks about handling CSV data in business operations. Andrew shares his background in data engineering and CRM migration, which led to the creation of OneSchema, a platform designed to automate CSV imports and improve data validation processes. He discusses the challenges of working with CSVs, including inconsistent type representation, lack of schema information, and technical complexities, and explains how OneSchema addresses these issues using multiple CSV parsers and AI for data type inference and validation. Andrew highlights the business case for OneSchema, emphasizing efficiency gains for companies dealing with large volumes of CSV data, and shares plans to expand support for other data formats and integrate AI-driven transformation packs for specific industries. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Andrew Luo about how OneSchema addresses the headaches of dealing with CSV data for your business Interview Introduction How did you get involved in the area of data management? Despite the years of evolution and improvement in data storage and interchange formats, CSVs are just as prevalent as ever. What are your opinions/theories on why they are so ubiquitous? What are some of the major sources of CSV data for teams that rely on them for business and analytical processes? The most obvious challenge with CSVs is their lack of type information, but they are notorious for having numerous other problems. What are some of the other major challenges involved with using CSVs for data interchange/ingestion? Can you describe what you are building at OneSchema and the story behind it? What are the core problems that you are solving, and for whom? Can you describe how you have architected your platform to be able to manage the variety, volume, and multi-tenancy of data that you process? How have the design and goals of the product changed since you first started working on it? What are some of the major performance issues that you have encountered while dealing with CSV data at scale? What are some of the most surprising things that you have learned about CSVs in the process of building OneSchema? What are the most interesting, innovative, or unexpected ways that you have seen OneSchema used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on OneSchema? When is OneSchema the wrong choice? What do you have planned for the future of OneSchema? 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 hosts@dataengineeringpodcast.com with your story. Links OneSchema EDI == Electronic Data Interchange UTF-8 BOM (Byte Order Mark) Characters SOAP CSV RFC Iceberg SSIS == SQL Server Integration Services MS Access Datafusion JSON Schema SFTP == Secure File Transfer Protocol The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Breaking Down Data Silos: AI and ML in Master Data Management 57:30

16 weeks ago57:30

57:30

Summary In this episode of the Data Engineering Podcast Dan Bruckner, co-founder and CTO of Tamr, talks about the application of machine learning (ML) and artificial intelligence (AI) in master data management (MDM). Dan shares his journey from working at CERN to becoming a data expert and discusses the challenges of reconciling large-scale organizational data. He explains how data silos arise from independent teams and highlights the importance of combining traditional techniques with modern AI to address the nuances of data reconciliation. Dan emphasizes the transformative potential of large language models (LLMs) in creating more natural user experiences, improving trust in AI-driven data solutions, and simplifying complex data management processes. He also discusses the balance between using AI for complex data problems and the necessity of human oversight to ensure accuracy and trust. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world; like in their episode “The Secret Sauce Behind McDonald’s Data Strategy”, which digs into how AI-driven tools can be used to support crew efficiency and customer interactions. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts. Your host is Tobias Macey and today I'm interviewing Dan Bruckner about the application of ML and AI techniques to the challenge of reconciling data at the scale of business Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the different ways that organizational data becomes unwieldy and needs to be consolidated and reconciled? How does that reconciliation relate to the practice of "master data management" What are the scaling challenges with the current set of practices for reconciling data? ML has been applied to data cleaning for a long time in the form of entity resolution, etc. How has the landscape evolved or matured in recent years? What (if any) transformative capabilities do LLMs introduce? What are the missing pieces/improvements that are necessary to make current AI systems usable out-of-the-box for data cleaning? What are the strategic decisions that need to be addressed when implementing ML/AI techniques in the data cleaning/reconciliation process? What are the risks involved in bringing ML to bear on data cleaning for inexperienced teams? What are the most interesting, innovative, or unexpected ways that you have seen ML techniques used in data resolution? What are the most interesting, unexpected, or challenging lessons that you have learned while working on using ML/AI in master data management? When is ML/AI the wrong choice for data cleaning/reconciliation? What are your hopes/predictions for the future of ML/AI applications in MDM and data cleaning? 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 hosts@dataengineeringpodcast.com with your story. Links Tamr Master Data Management CERN LHC Michael Stonebraker Conway's Law Expert Systems Information Retrieval Active Learning The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
Building a Data Vision Board: A Guide to Strategic Planning 49:59

17 weeks ago49:59

49:59

Summary In this episode of the Data Engineering Podcast Lior Barak shares his insights on developing a three-year strategic vision for data management. He discusses the importance of having a strategic plan for data, highlighting the need for data teams to focus on impact rather than just enablement. He introduces the concept of a "data vision board" and explains how it can help organizations outline their strategic vision by considering three key forces: regulation, stakeholders, and organizational goals. Lior emphasizes the importance of balancing short-term pressures with long-term strategic goals, quantifying the cost of data issues to prioritize effectively, and maintaining the strategic vision as a living document through regular reviews. He encourages data teams to shift from being enablers to impact creators and provides practical advice on implementing a data vision board, setting clear KPIs, and embracing a product mindset to create tangible business impacts through strategic data management. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management It’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. Your host is Tobias Macey and today I'm interviewing Lior Barak about how to develop your three year strategic vision for data Interview Introduction How did you get involved in the area of data management? Can you start by giving an outline of the types of problems that occur as a result of not developing a strategic plan for an organization's data systems? What is the format that you recommend for capturing that strategic vision? What are the types of decisions and details that you believe should be included in a vision statement? Why is a 3 year horizon beneficial? What does that scale of time encourage/discourage in the debate and decision-making process? Who are the personas that should be included in the process of developing this strategy document? Can you walk us through the steps and processes involved in developing the data vision board for an organization? What are the time-frames or milestones that should lead to revisiting and revising the strategic objectives? What are the most interesting, innovative, or unexpected ways that you have seen a data vision strategy used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data strategy development? When is a data vision board the wrong choice? What are some additional resources or practices that you recommend teams invest in as a supplement to this strategic vision exercise? Contact Info LinkedIn Substack 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 hosts@dataengineeringpodcast.com with your story. Links Vision Board Overview Episode 397: Defining A Strategy For Your Data Products Minto Pyramid Principle KPI == Key Performance Indicator OKR == Objectives and Key Results Phil Jackson: Eleven Rings (affiliate link) The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
How Orchestration Impacts Data Platform Architecture 59:39

18 weeks ago59:39

59:39

Summary The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management It’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world, from big picture questions like AI governance and data sharing to more nuanced questions like, how do we balance offense and defense in data management? In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts. Your host is Tobias Macey and today I'm interviewing Hugo Lu about the data platform and orchestration ecosystem and how to navigate the available options Interview Introduction How did you get involved in building data platforms? Can you describe what an orchestrator is in the context of data platforms? There are many other contexts in which orchestration is necessary. What are some examples of how orchestrators have adapted (or failed to adapt) to the times? What are the core features that are necessary for an orchestrator to have when dealing with data-oriented workflows? Beyond the bare necessities, what are some of the other features and design considerations that go into building a first-class dat platform or orchestration system? There have been several generations of orchestration engines over the past several years. How would you characterize the different coarse groupings of orchestration engines across those generational boundaries? How do the characteristics of a data orchestrator influence the overarching architecture of an organization's data platform/data operations? What about the reverse? How have the cycles of ML and AI workflow requirements impacted the design requirements for data orchestrators? What are the most interesting, innovative, or unexpected ways that you have seen data orchestrators used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration? When is an orchestrator the wrong choice? What are your predictions and/or hopes for the future of data orchestration? Contact Info Medium LinkedIn Parting Question From your perspective, what is the biggest thing data teams are missing in the technology 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 hosts@dataengineeringpodcast.com with your story. Links Orchestra Previous Episode: Overview Of The State Of Data Orchestration Cron ArgoCD DAG Kubernetes Data Mesh Airflow SSIS == SQL Server Integration Services Pentaho Kettle DataVolo NiFi Podcast Episode Dagster gRPC Coalesce Podcast Episode dbt DataHub Palantir The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

1
An Exploration Of The Impediments To Reusable Data Pipelines 51:32

19 weeks ago51:32

51:32

Summary In this episode of the Data Engineering Podcast the inimitable Max Beauchemin talks about reusability in data pipelines. The conversation explores the "write everything twice" problem, where similar pipelines are built without code reuse, and discusses the challenges of managing different SQL dialects and relational databases. Max also touches on the evolving role of data engineers, drawing parallels with front-end engineering, and suggests that generative AI could facilitate knowledge capture and distribution in data engineering. He encourages the community to share reference implementations and templates to foster collaboration and innovation, and expresses hopes for a future where code reuse becomes more prevalent. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm joined again by Max Beauchemin to talk about the challenges of reusability in data pipelines Interview Introduction How did you get involved in the area of data management? Can you start by sharing your current thesis on the opportunities and shortcomings of code and component reusability in the data context? What are some ways that you think about what constitutes a "component" in this context? The data ecosystem has arguably grown more varied and nuanced in recent years. At the same time, the number and maturity of tools has grown. What is your view on the current trend in productivity for data teams and practitioners? What do you see as the core impediments to building more reusable and general-purpose solutions in data engineering? How can we balance the actual needs of data consumers against their requests (whether well- or un-informed) to help increase our ability to better design our workflows for reuse? In data engineering there are two broad approaches; code-focused or SQL-focused pipelines. In principle one would think that code-focused environments would have better composability. What are you seeing as the realities in your personal experience and what you hear from other teams? When it comes to SQL dialects, dbt offers the option of Jinja macros, whereas SDF and SQLMesh offer automatic translation. There are also tools like PRQL and Malloy that aim to abstract away the underlying SQL. What are the tradeoffs across those options that help or hinder the portability of transformation logic? Which layers of the data stack/steps in the data journey do you see the greatest opportunity for improving the creation of more broadly usable abstractions/reusable elements? low/no code systems for code reuse impact of LLMs on reusability/composition impact of background on industry practices (e.g. DBAs, sysadmins, analysts vs. SWE, etc.) polymorphic data models (e.g. activity schema) What are the most interesting, innovative, or unexpected ways that you have seen teams address composability and reusability of data components? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data-oriented tools and utilities? What are your hopes and predictions for sharing of code and logic in the future of data engineering? 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 hosts@dataengineeringpodcast.com with your story. Links Max's Blog Post Airflow Superset Tableau Looker PowerBI Cohort Analysis NextJS Airbyte Podcast Episode Fivetran Podcast Episode Segment dbt SQLMesh Podcast Episode Spark LAMP Stack PHP Relational Algebra Knowledge Graph Python Marshmallow Data Warehouse Lifecycle Toolkit (affiliate link) Entity Centric Data Modeling Blog Post Amplitude OSACon presentation ol-data-platform Tobias' team's data platform code The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

42 weeks ago59:48

59:48

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 LinkedIn Substack 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 hosts@dataengineeringpodcast.com with your story. Links Synq Incident Management SLA == Service Level Agreement Data Governance Podcast Episode PagerDuty OpsGenie Clickhouse Podcast Episode dbt Podcast Episode SQLMesh Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

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