Data Engineering Podcast
Data Engineering Podcast

Data Engineering Podcast

Tobias Macey

Overview
Episodes

Details

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.

Recent Episodes

Streaming Data Into The Lakehouse With Iceberg And Trino At Going
NOV 18, 2024
Streaming Data Into The Lakehouse With Iceberg And Trino At Going
<p>In this episode, I had the pleasure of speaking with Ken Pickering, VP of Engineering at Going, about the intricacies of streaming data into a Trino and Iceberg lakehouse. Ken shared his journey from product engineering to becoming deeply involved in data-centric roles, highlighting his experiences in ecommerce and InsurTech. At Going, Ken leads the data platform team, focusing on finding travel deals for consumers, a task that involves handling massive volumes of flight data and event stream information.<br /><br />Ken explained the dual approach of passive and active search strategies used by Going to manage the vast data landscape. Passive search involves aggregating data from global distribution systems, while active search is more transactional, querying specific flight prices. This approach helps Going sift through approximately 50 petabytes of data annually to identify the best travel deals.<br /><br />We delved into the technical architecture supporting these operations, including the use of Confluent for data streaming, Starburst Galaxy for transformation, and Databricks for modeling. Ken emphasized the importance of an open lakehouse architecture, which allows for flexibility and scalability as the business grows.<br /><br />Ken also discussed the composition of Going's engineering and data teams, highlighting the collaborative nature of their work and the reliance on vendor tooling to streamline operations. He shared insights into the challenges and strategies of managing data life cycles, ensuring data quality, and maintaining uptime for consumer-facing applications.<br /><br />Throughout our conversation, Ken provided a glimpse into the future of Going's data architecture, including potential expansions into other travel modes and the integration of large language models for enhanced customer interaction. This episode offers a comprehensive look at the complexities and innovations in building a data-driven travel advisory service.</p>
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39 MIN
An Opinionated Look At End-to-end Code Only Analytical Workflows With Bruin
NOV 11, 2024
An Opinionated Look At End-to-end Code Only Analytical Workflows With Bruin
Summary<br />The challenges of integrating all of the tools in the modern data stack has led to a new generation of tools that focus on a fully integrated workflow. At the same time, there have been many approaches to how much of the workflow is driven by code vs. not. Burak Karakan is of the opinion that a fully integrated workflow that is driven entirely by code offers a beneficial and productive means of generating useful analytical outcomes. In this episode he shares how Bruin builds on those opinions and how you can use it to build your own analytics without having to cobble together a suite of tools with conflicting abstractions.<br /><br /><br />Announcements<br /><ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>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 <a href="https://www.dataengineeringpodcast.com/datafold" target="_blank">dataengineeringpodcast.com/datafold</a> today!</li><li>Your host is Tobias Macey and today I'm interviewing Burak Karakan about the benefits of building code-only data systems</li></ul>Interview<br /><ul><li>Introduction</li><li>How did you get involved in the area of data management?</li><li>Can you describe what Bruin is and the story behind it?<ul><li>Who is your target audience?</li></ul></li><li>There are numerous tools that address the ETL workflow for analytical data. What are the pain points that you are focused on for your target users?</li><li>How does a code-only approach to data pipelines help in addressing the pain points of analytical workflows?<ul><li>How might it act as a limiting factor for organizational involvement?</li></ul></li><li>Can you describe how Bruin is designed?<ul><li>How have the design and scope of Bruin evolved since you first started working on it?</li></ul></li><li>You call out the ability to mix SQL and Python for transformation pipelines. What are the components that allow for that functionality?<ul><li>What are some of the ways that the combination of Python and SQL improves ergonomics of transformation workflows?</li></ul></li><li>What are the key features of Bruin that help to streamline the efforts of organizations building analytical systems?</li><li>Can you describe the workflow of someone going from source data to warehouse and dashboard using Bruin and Ingestr?</li><li>What are the opportunities for contributions to Bruin and Ingestr to expand their capabilities?</li><li>What are the most interesting, innovative, or unexpected ways that you have seen Bruin and Ingestr used?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bruin?</li><li>When is Bruin the wrong choice?</li><li>What do you have planned for the future of Bruin?</li></ul>Contact Info<br /><ul><li><a href="https://www.linkedin.com/in/burakkarakan/?originalSubdomain=de" target="_blank">LinkedIn</a></li></ul>Parting Question<br /><ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul>Closing Announcements<br /><ul><li>Thank you for listening! Don't forget to check out our other shows. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used. The <a href="https://www.aiengineeringpodcast.com" target="_blank">AI Engineering Podcast</a> is your guide to the fast-moving world of building AI systems.</li><li>Visit the <a href="https://www.dataengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.</li></ul>Links<br /><ul><li><a href="https://getbruin.com/" target="_blank">Bruin</a></li><li><a href="https://www.fivetran.com/" target="_blank">Fivetran</a></li><li><a href="https://www.stitchdata.com/" target="_blank">Stitch</a></li><li><a href="https://github.com/bruin-data/ingestr" target="_blank">Ingestr</a></li><li><a href="https://github.com/bruin-data/bruin" target="_blank">Bruin CLI</a></li><li><a href="https://meltano.com/" target="_blank">Meltano</a></li><li><a href="https://github.com/tobymao/sqlglot" target="_blank">SQLGlot</a></li><li><a href="https://www.getdbt.com/" target="_blank">dbt</a></li><li><a href="https://sqlmesh.readthedocs.io/en/stable/" target="_blank">SQLMesh</a><ul><li><a href="https://www.dataengineeringpodcast.com/sqlmesh-open-source-dataops-episode-380" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://www.sdf.com/" target="_blank">SDF</a><ul><li><a href="https://www.dataengineeringpodcast.com/sdf-fast-and-expressive-sql-transformation-episode-440" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://airflow.apache.org/" target="_blank">Airflow</a></li><li><a href="https://dagster.io/" target="_blank">Dagster</a></li><li><a href="https://www.snowflake.com/en/data-cloud/snowpark/" target="_blank">Snowpark</a></li><li><a href="https://atlan.com/" target="_blank">Atlan</a></li><li><a href="https://evidence.dev/" target="_blank">Evidence</a></li></ul>The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>
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56 MIN
Feldera: Bridging Batch and Streaming with Incremental Computation
NOV 4, 2024
Feldera: Bridging Batch and Streaming with Incremental Computation
Summary<br />In this episode of the Data Engineering Podcast, the creators of Feldera talk about their incremental compute engine designed for continuous computation of data, machine learning, and AI workloads. The discussion covers the concept of incremental computation, the origins of Feldera, and its unique ability to handle both streaming and batch data seamlessly. The guests explore Feldera's architecture, applications in real-time machine learning and AI, and challenges in educating users about incremental computation. They also discuss the balance between open-source and enterprise offerings, and the broader implications of incremental computation for the future of data management, predicting a shift towards unified systems that handle both batch and streaming data efficiently.<br /><br />Announcements<br /><ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>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 <a href="https://www.dataengineeringpodcast.com/datafold" target="_blank">dataengineeringpodcast.com/datafold</a> today!</li><li>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 you should listen to Data Citizens® Dialogues, the forward-thinking podcast from the folks at 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. They address questions around AI governance, data sharing, and working at global scale. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. While data is shaping our world, Data Citizens Dialogues is shaping the conversation. Subscribe to <a href="https://www.collibra.com/podcasts" target="_blank">Data Citizens Dialogues</a> on Apple, Spotify, Youtube, or wherever you get your podcasts.</li><li>Your host is Tobias Macey and today I'm interviewing Leonid Ryzhyk, Lalith Suresh, and Mihai Budiu about Feldera, an incremental compute engine for continous computation of data, ML, and AI workloads</li></ul>Interview<br /><ul><li>Introduction</li><li>Can you describe what Feldera is and the story behind it?</li><li>DBSP (the theory behind Feldera) has won multiple awards from the database research community. Can you explain what it is and how it solves the incremental computation problem?</li><li>Depending on which angle you look at it, Feldera has attributes of data warehouses, federated query engines, and stream processors. What are the unique use cases that Feldera is designed to address?<ul><li>In what situations would you replace another technology with Feldera?</li><li>When is it an additive technology?</li></ul></li><li>Can you describe the architecture of Feldera?<ul><li>How have the design and scope evolved since you first started working on it?</li></ul></li><li>What are the state storage interfaces available in Feldera?<ul><li>What are the opportunities for integrating with or building on top of open table formats like Iceberg, Lance, Hudi, etc.?</li></ul></li><li>Can you describe a typical workflow for an engineer building with Feldera?</li><li>You advertise Feldera's utility in ML and AI use cases in addition to data management. What are the features that make it conducive to those applications?</li><li>What is your philosophy toward the community growth and engagement with the open source aspects of Feldera and how you're balancing that with sustainability of the project and business?</li><li>What are the most interesting, innovative, or unexpected ways that you have seen Feldera used?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on Feldera?</li><li>When is Feldera the wrong choice?</li><li>What do you have planned for the future of Feldera?</li></ul>Contact Info<br /><ul><li>Leonid<ul><li><a href="https://ryzhyk.net/" target="_blank">Website</a></li><li><a href="https://github.com/ryzhyk" target="_blank">GitHub</a></li><li><a href="https://www.linkedin.com/in/leonid-ryzhyk-0ba031b9/" target="_blank">LinkedIn</a></li></ul></li><li>Lalith<ul><li><a href="https://www.linkedin.com/in/lalith-suresh-34bb8911/" target="_blank">LinkedIn</a></li><li><a href="https://lalith.in/research/" target="_blank">Website</a></li></ul></li><li>Mihai<ul><li><a href="https://mihaibudiu.github.io/work/index.html" target="_blank">Website</a></li><li><a href="https://github.com/mihaibudiu" target="_blank">GitHub</a></li></ul></li></ul>Parting Question<br /><ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul>Closing Announcements<br /><ul><li>Thank you for listening! Don't forget to check out our other shows. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used. The <a href="https://www.aiengineeringpodcast.com" target="_blank">AI Engineering Podcast</a> is your guide to the fast-moving world of building AI systems.</li><li>Visit the <a href="https://www.dataengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.</li></ul>Links<br /><ul><li><a href="https://www.feldera.com/" target="_blank">Feldera</a><ul><li><a href="https://github.com/feldera/feldera" target="_blank">GitHub</a></li></ul></li><li><a href="https://arxiv.org/abs/2203.16684" target="_blank">DBSP</a> paper<ul><li><a href="https://docs.rs/dbsp/latest/dbsp/" target="_blank">Rust Crate</a></li></ul></li><li><a href="https://timelydataflow.github.io/differential-dataflow/" target="_blank">Differential Dataflow</a></li><li><a href="https://trino.io/" target="_blank">Trino</a></li><li><a href="https://flink.apache.org/" target="_blank">Flink</a></li><li><a href="https://spark.apache.org/" target="_blank">Spark</a></li><li><a href="https://materialize.com/" target="_blank">Materialize</a></li><li><a href="https://clickhouse.com/" target="_blank">Clickhouse</a><ul><li><a href="https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://duckdb.org/" target="_blank">DuckDB</a><ul><li><a href="https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://www.snowflake.com" target="_blank">Snowflake</a></li><li><a href="https://arrow.apache.org/" target="_blank">Arrow</a></li><li><a href="https://substrait.io/" target="_blank">Substrait</a></li><li><a href="https://datafusion.apache.org/" target="_blank">DataFusion</a></li><li><a href="https://en.wikipedia.org/wiki/Digital_signal_processing" target="_blank">DSP == Digital Signal Processing</a></li><li><a href="https://en.wikipedia.org/wiki/Change_data_capture" target="_blank">CDC == Change Data Capture</a></li><li><a href="https://prql-lang.org/" target="_blank">PRQL</a></li><li><a href="https://en.wikipedia.org/wiki/Log-structured_merge-tree" target="_blank">LSM (Log-Structured Merge) Tree</a></li><li><a href="https://iceberg.apache.org/" target="_blank">Iceberg</a><ul><li><a href="https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://delta.io/" target="_blank">Delta Lake</a><ul><li><a href="https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://www.openvswitch.org/" target="_blank">Open VSwitch</a></li><li><a href="https://en.wikipedia.org/wiki/Feature_engineering" target="_blank">Feature Engineering</a></li><li><a href="https://calcite.apache.org/" target="_blank">Calcite</a></li></ul>The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>
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47 MIN
Accelerate Migration Of Your Data Warehouse with Datafold's AI Powered Migration Agent
OCT 27, 2024
Accelerate Migration Of Your Data Warehouse with Datafold's AI Powered Migration Agent
Summary<br />Gleb Mezhanskiy, CEO and co-founder of DataFold, joins Tobias Macey to discuss the challenges and innovations in data migrations. Gleb shares his experiences building and scaling data platforms at companies like Autodesk and Lyft, and how these experiences inspired the creation of DataFold to address data quality issues across teams. He outlines the complexities of data migrations, including common pitfalls such as technical debt and the importance of achieving parity between old and new systems. Gleb also discusses DataFold's innovative use of AI and large language models (LLMs) to automate translation and reconciliation processes in data migrations, reducing time and effort required for migrations.<br />Announcements<br /><ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>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 <a href="https://www.dataengineeringpodcast.com/datafold" target="_blank">dataengineeringpodcast.com/datafold</a> today!</li><li>Your host is Tobias Macey and today I'm welcoming back Gleb Mezhanskiy to talk about Datafold's experience bringing AI to bear on the problem of migrating your data stack</li></ul>Interview<br /><ul><li>Introduction</li><li>How did you get involved in the area of data management?</li><li>Can you describe what the Data Migration Agent is and the story behind it?<ul><li>What is the core problem that you are targeting with the agent?</li></ul></li><li>What are the biggest time sinks in the process of database and tooling migration that teams run into?</li><li>Can you describe the architecture of your agent?<ul><li>What was your selection and evaluation process for the LLM that you are using?</li></ul></li><li>What were some of the main unknowns that you had to discover going into the project?<ul><li>What are some of the evolutions in the ecosystem that occurred either during the development process or since your initial launch that have caused you to second-guess elements of the design?</li></ul></li><li>In terms of SQL translation there are libraries such as SQLGlot and the work being done with SDF that aim to address that through AST parsing and subsequent dialect generation. What are the ways that approach is insufficient in the context of a platform migration?</li><li>How does the approach you are taking with the combination of data-diffing and automated translation help build confidence in the migration target?</li><li>What are the most interesting, innovative, or unexpected ways that you have seen the Data Migration Agent used?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI powered migration assistant?</li><li>When is the data migration agent the wrong choice?</li><li>What do you have planned for the future of applications of AI at Datafold?</li></ul>Contact Info<br /><ul><li><a href="https://www.linkedin.com/in/glebmezh/" target="_blank">LinkedIn</a></li></ul>Parting Question<br /><ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul>Closing Announcements<br /><ul><li>Thank you for listening! Don't forget to check out our other shows. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used. The <a href="https://www.aiengineeringpodcast.com" target="_blank">AI Engineering Podcast</a> is your guide to the fast-moving world of building AI systems.</li><li>Visit the <a href="https://www.dataengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email <a target="_blank">[email protected]</a> with your story.</li></ul>Links<br /><ul><li><a href="https://www.datafold.com/" target="_blank">Datafold</a></li><li><a href="https://www.datafold.com/data-migration" target="_blank">Datafold Migration Agent</a></li><li><a href="https://www.datafold.com/data-diff" target="_blank">Datafold data-diff</a></li><li><a href="https://www.dataengineeringpodcast.com/datafold-database-reconciliation-episode-417" target="_blank">Datafold Reconciliation Podcast Episode</a></li><li><a href="https://github.com/tobymao/sqlglot" target="_blank">SQLGlot</a></li><li><a href="https://github.com/lark-parser/lark" target="_blank">Lark</a> parser</li><li><a href="https://www.anthropic.com/news/claude-3-5-sonnet" target="_blank">Claude 3.5 Sonnet</a></li><li><a href="https://cloud.google.com/looker/?hl=en" target="_blank">Looker</a><ul><li><a href="https://www.dataengineeringpodcast.com/looker-with-daniel-mintz-episode-55" target="_blank">Podcast Episode</a></li></ul></li></ul>The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>
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48 MIN
Bring Vector Search And Storage To The Data Lake With Lance
OCT 20, 2024
Bring Vector Search And Storage To The Data Lake With Lance
Summary<br />The rapid growth of generative AI applications has prompted a surge of investment in vector databases. While there are numerous engines available now, Lance is designed to integrate with data lake and lakehouse architectures. In this episode Weston Pace explains the inner workings of the Lance format for table definitions and file storage, and the optimizations that they have made to allow for fast random access and efficient schema evolution. In addition to integrating well with data lakes, Lance is also a first-class participant in the Arrow ecosystem, making it easy to use with your existing ML and AI toolchains. This is a fascinating conversation about a technology that is focused on expanding the range of options for working with vector data.<br />Announcements<br /><ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>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 <a href="https://www.dataengineeringpodcast.com/datafold" target="_blank">dataengineeringpodcast.com/datafold</a> today!</li><li>Your host is Tobias Macey and today I'm interviewing Weston Pace about the Lance file and table format for column-oriented vector storage</li></ul>Interview<br /><ul><li>Introduction</li><li>How did you get involved in the area of data management?</li><li>Can you describe what Lance is and the story behind it?<ul><li>What are the core problems that Lance is designed to solve?<ul><li>What is explicitly out of scope?</li></ul></li></ul></li><li>The README mentions that it is straightforward to convert to Lance from Parquet. What is the motivation for this compatibility/conversion support?<ul><li>What formats does Lance replace or obviate?</li></ul></li><li>In terms of data modeling Lance obviously adds a vector type, what are the features and constraints that engineers should be aware of when modeling their embeddings or arbitrary vectors?<ul><li>Are there any practical or hard limitations on vector dimensionality?</li></ul></li><li>When generating Lance files/datasets, what are some considerations to be aware of for balancing file/chunk sizes for I/O efficiency and random access in cloud storage?</li><li>I noticed that the file specification has space for feature flags. How has that aided in enabling experimentation in new capabilities and optimizations?</li><li>What are some of the engineering and design decisions that were most challenging and/or had the biggest impact on the performance and utility of Lance?</li><li>The most obvious interface for reading and writing Lance files is through LanceDB. Can you describe the use cases that it focuses on and its notable features?<ul><li>What are the other main integrations for Lance?</li><li>What are the opportunities or roadblocks in adding support for Lance and vector storage/indexes in e.g. Iceberg or Delta to enable its use in data lake environments?</li></ul></li><li>What are the most interesting, innovative, or unexpected ways that you have seen Lance used?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Lance format?</li><li>When is Lance the wrong choice?</li><li>What do you have planned for the future of Lance?</li></ul>Contact Info<br /><ul><li><a href="https://www.linkedin.com/in/weston-pace-cool-dude/" target="_blank">LinkedIn</a></li><li><a href="https://github.com/westonpace" target="_blank">GitHub</a></li></ul>Parting Question<br /><ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul>Links<br /><ul><li><a href="https://lancedb.github.io/lance/" target="_blank">Lance Format</a></li><li><a href="https://lancedb.github.io/lancedb/" target="_blank">LanceDB</a></li><li><a href="https://substrait.io/" target="_blank">Substrait</a></li><li><a href="https://arrow.apache.org/docs/python/index.html" target="_blank">PyArrow</a></li><li><a href="https://github.com/facebookresearch/faiss" target="_blank">FAISS</a></li><li><a href="https://www.pinecone.io/" target="_blank">Pinecone</a><ul><li><a href="https://www.dataengineeringpodcast.com/pinecone-vector-database-similarity-search-episode-189/" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://parquet.apache.org/" target="_blank">Parquet</a></li><li><a href="https://iceberg.apache.org/" target="_blank">Iceberg</a><ul><li><a href="https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://delta.io/" target="_blank">Delta Lake</a><ul><li><a href="https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://github.com/lancedb/lance/tree/main/python" target="_blank">PyLance</a></li><li><a href="https://en.wikipedia.org/wiki/Hilbert_curve" target="_blank">Hilbert Curves</a></li><li><a href="https://en.wikipedia.org/wiki/Scale-invariant_feature_transform" target="_blank">SIFT Vectors</a></li><li><a href="https://aws.amazon.com/s3/storage-classes/express-one-zone/" target="_blank">S3 Express</a></li><li><a href="https://www.weka.io/" target="_blank">Weka</a></li><li><a href="https://datafusion.apache.org/" target="_blank">DataFusion</a></li><li><a href="https://www.ray.io/" target="_blank">Ray Data</a></li><li><a href="https://pytorch.org/tutorials/beginner/basics/data_tutorial.html#preparing-your-data-for-training-with-dataloaders" target="_blank">Torch Data Loader</a></li><li><a href="https://lancedb.github.io/lancedb/concepts/index_hnsw/" target="_blank">HNSW == Hierarchical Navigable Small Worlds</a> vector index</li><li><a href="https://lancedb.github.io/lancedb/concepts/index_ivfpq/" target="_blank">IVFPQ</a> vector index</li><li><a href="https://geojson.org/" target="_blank">GeoJSON</a></li><li><a href="https://docs.pola.rs/" target="_blank">Polars</a></li></ul>The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>
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58 MIN