Data Engineering Podcast

Auteur(s): Tobias Macey
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  • Résumé

  • This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
    © 2024 Boundless Notions, LLC.
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Épisodes
  • Improve Data Quality Through Engineering Rigor And Business Engagement With Synq

    Improve Data Quality Through Engineering Rigor And Business Engagement With Synq
    Jun 30 2024
    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 (https://www.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 (https://www.linkedin.com/in/petr-janda/?originalSubdomain=dk) Substack (https://substack.com/@petrjanda) 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__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) 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 (mailto:hosts@dataengineeringpodcast.com) with your story. Links Synq (https://www.synq.io/) Incident Management (https://www.pagerduty.com/resources/learn/what-is-incident-management/) SLA == Service Level Agreement (https://en.wikipedia.org/wiki/Service-level_agreement) Data Governance (https://en.wikipedia.org/wiki/Data_governance) Podcast Episode (https://www.dataengineeringpodcast.com/nicola-askham-practical-data-governance-episode-428) ...
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    1 h
  • Stitching Together Enterprise Analytics With Microsoft Fabric

    Stitching Together Enterprise Analytics With Microsoft Fabric
    Jun 23 2024
    Summary Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service. 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 (https://www.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 Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou Interview Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution? What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges? How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine? What are the challenges in terms of safety and reliability? What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics? Contact Info LinkedIn (https://www.linkedin.com/in/diptiborkar/) 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__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) 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 (mailto:hosts@dataengineeringpodcast.com) with your story. Links Microsoft Fabric (https://www.microsoft.com/microsoft-fabric) Ahana episode (https://www.dataengineeringpodcast.com/ahana-presto-cloud-data-lake-episode-217) DB2 Distributed (https://www.ibm.com/docs/en/db2/11.5?topic=managers-designing-distributed-databases) Spark (https://spark.apache.org/) Presto (https://prestodb.io/) Azure Data (https://azure.microsoft.com/en-us/products#analytics) MAD Landscape (https://mattturck.com/mad2024/) Podcast Episode (https://www.dataengineeringpodcast.com/mad-landscape-2023-data-infrastructure-episode-369) ML Podcast Episode (https://www.themachinelearningpodcast.com/mad-landscape-2023-ml-ai-episode-21) Tableau (https://www.tableau.com/) dbt (https://www.getdbt.com/) Medallion Architecture (https://dataengineering.wiki/Concepts/Medallion+Architecture) Microsoft Onelake (https://learn.microsoft.com/fabric/onelake/onelake-overview) ORC (https://orc.apache.org/) Parquet (https://parquet.incubator.apache.org) Avro (https://avro.apache.org/) Delta Lake (https://delta.io/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209) Hadoop (https://hadoop.apache.org/) PowerBI (https://www.microsoft.com/power-platform/products/power-bi) Podcast Episode (https://www.dataengineeringpodcast.com/power-bi-business-intelligence-episode-154) Velox (https://velox-lib.io/) Gluten (https://gluten.apache.org/) Apache ...
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    53 min
  • Being Data Driven At Stripe With Trino And Iceberg

    Being Data Driven At Stripe With Trino And Iceberg
    Jun 16 2024
    Summary Stripe is a company that relies on data to power their products and business. To support that functionality they have invested in Trino and Iceberg for their analytical workloads. In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. 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 (https://www.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 Kevin Liu about his use of Trino and Iceberg for Stripe's data lakehouse Interview Introduction How did you get involved in the area of data management? Can you describe what role Trino and Iceberg play in Stripe's data architecture? What are the ways in which your job responsibilities intersect with Stripe's lakehouse infrastructure? What were the requirements and selection criteria that led to the selection of that combination of technologies? What are the other systems that feed into and rely on the Trino/Iceberg service? what kinds of questions are you answering with table metadata what use case/team does that support comparative utility of iceberg REST catalog What are the shortcomings of Trino and Iceberg? What are the most interesting, innovative, or unexpected ways that you have seen Iceberg/Trino used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Stripe's data infrastructure? When is a lakehouse on Trino/Iceberg the wrong choice? What do you have planned for the future of Trino and Iceberg at Stripe? Contact Info Substack (https://kevinjqliu.substack.com) LinkedIn (https://www.linkedin.com/in/kevinjqliu) 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__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) 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 (mailto:hosts@dataengineeringpodcast.com) with your story. Links Trino (https://trino.io/) Iceberg (https://iceberg.apache.org/) Stripe (https://stripe.com/) Spark (https://spark.apache.org/) Redshift (https://aws.amazon.com/redshift/) Hive Metastore (https://cwiki.apache.org/confluence/display/hive/design#Design-Metastore) Python Iceberg (https://py.iceberg.apache.org/) Python Iceberg REST Catalog (https://github.com/kevinjqliu/iceberg-rest-catalog) Trino Metadata Table (https://trino.io/docs/current/connector/iceberg.html#metadata-tables) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57) Tabular (https://tabular.io/) Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363) Delta Table (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) Databricks Unity Catalog (https://www.databricks.com/product/unity-catalog) Starburst (https://www.starburst.io/) AWS Athena (https://aws.amazon.com/athena/) Kevin Trinofest Presentation (https://trino.io/blog/2023/07/19/trino-fest-2023-stripe.html) Alluxio (https://www.alluxio.io/) Podcast Episode (https://www.dataengineeringpodcast.com/alluxio-distributed-storage-episode-70) Parquet (https://parquet.incubator.apache.org/) Hudi (https://hudi.apache.org/) Trino Project Tardigrade (https://trino.io/blog/2022/05/05/tardigrade-launch.html) Trino On Ice (https://www.starburst.io/blog/iceberg-table-partitioning/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://...
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    53 min

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