315 episodes

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.

Data Engineering Podcast Tobias Macey

    • Technology
    • 4.2 • 5 Ratings

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.

    Useful Lessons And Repeatable Patterns Learned From Data Mesh Implementations At AgileLab

    Useful Lessons And Repeatable Patterns Learned From Data Mesh Implementations At AgileLab

    Data mesh is a frequent topic of conversation in the data community, with many debates about how and when to employ this architectural pattern. The team at AgileLab have first-hand experience helping large enterprise organizations evaluate and implement their own data mesh strategies. In this episode Paolo Platter shares the lessons they have learned in that process, the Data Mesh Boost platform that they have built to reduce some of the boilerplate required to make it successful, and some of the considerations to make when deciding if a data mesh is the right choice for you.

    • 48 min
    Optimize Your Machine Learning Development And Serving With The Open Source Vector Database Milvus

    Optimize Your Machine Learning Development And Serving With The Open Source Vector Database Milvus

    The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. For machine learning applications relational models require additional processing to be directly useful, which is why there has been a growth in the use of vector databases. These platforms store direct representations of the vector embeddings that machine learning models rely on for computing relevant predictions so that there is no additional processing required to go from input data to inference output. In this episode Frank Liu explains how the open source Milvus vector database is implemented to speed up machine learning development cycles, how to think about proper storage and scaling of these vectors, and how data engineering and machine learning teams can collaborate on the creation and maintenance of these data sets.

    • 58 min
    What "Data Lineage Done Right" Looks Like And How They're Doing It At Manta

    What "Data Lineage Done Right" Looks Like And How They're Doing It At Manta

    Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. Because of its centrality to your data systems it is valuable for debugging, governance, understanding context, and myriad other purposes. This means that it is important to have an accurate and complete lineage graph so that you don't have to perform your own detective work when time is in short supply. In this episode Ernie Ostic shares the approach that he and his team at Manta are taking to build a complete view of data lineage across the various data systems in your organization and the useful applications of that information in the work of every data stakeholder.

    • 1 hr 5 min
    Interactive Exploratory Data Analysis On Petabyte Scale Data Sets With Arkouda

    Interactive Exploratory Data Analysis On Petabyte Scale Data Sets With Arkouda

    Exploratory data analysis works best when the feedback loop is fast and iterative. This is easy to achieve when you are working on small datasets, but as they scale up beyond what can fit on a single machine those short iterations quickly become long and tedious. The Arkouda project is a Python interface built on top of the Chapel compiler to bring back those interactive speeds for exploratory analysis on horizontally scalable compute that parallelizes operations on large volumes of data. In this episode David Bader explains how the framework operates, the algorithms that are built into it to support complex analyses, and how you can start using it today.

    • 40 min
    Writing The Book That Offers A Single Reference For The Fundamentals Of Data Engineering

    Writing The Book That Offers A Single Reference For The Fundamentals Of Data Engineering

    Data engineering is a difficult job, requiring a large number of skills that often don't overlap. Any effort to understand how to start a career in the role has required stitching together information from a multitude of resources that might not all agree with each other. In order to provide a single reference for anyone tasked with data engineering responsibilities Joe Reis and Matt Housley took it upon themselves to write the book "Fundamentals of Data Engineering". In this episode they share their experiences researching and distilling the lessons that will be useful to data engineers now and into the future, without being tied to any specific technologies that may fade from fashion.

    • 1 hr 1 min
    Re-Bundling The Data Stack With Data Orchestration And Software Defined Assets Using Dagster

    Re-Bundling The Data Stack With Data Orchestration And Software Defined Assets Using Dagster

    The current stage of evolution in the data management ecosystem has resulted in domain and use case specific orchestration capabilities being incorporated into various tools. This complicates the work involved in making end-to-end workflows visible and integrated. Dagster has invested in bringing insights about external tools' dependency graphs into one place through its "software defined assets" functionality. In this episode Nick Schrock discusses the importance of orchestration and a central location for managing data systems, the road to Dagster's 1.0 release, and the new features coming with Dagster Cloud's general availability.

    • 58 min

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4.2 out of 5
5 Ratings

5 Ratings

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