Latest Episodes

Maintaining Your Data Lake At Scale With Spark - Episode 85

Building and maintaining a data lake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that data lakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics. Delta Lake is an open source, opinionated framework built on top of Spark for interacting with and maintaining data lake platforms that incorporates the lessons learned at DataBricks from countless customer use cases. In this episode Michael Armbrust, the lead architect of Delta Lake,...

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Managing The Machine Learning Lifecycle - Episode 84

Building a machine learning model can be difficult, but that is only half of the battle. Having a perfect model is only useful if you are able to get it into production. In this episode Stepan Pushkarev, founder of Hydrosphere, explains why deploying and maintaining machine learning projects in production is different from regular software projects and the challenges that they bring. He also describes the Hydrosphere platform, and how the different components work together to manage the full lifecycle of model deployment and retraining. This was a useful...

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Evolving An ETL Pipeline For Better Productivity - Episode 83

Building an ETL pipeline can be a significant undertaking, and sometimes it needs to be rebuilt when a better option becomes available. In this episode Aaron Gibralter, director of engineering at Greenhouse, joins Raghu Murthy, founder and CEO of DataCoral, to discuss the journey that he and his team took from an in-house ETL pipeline built out of open source components onto a paid service. He explains how their original implementation was built, why they decided to migrate to a paid service, and how they made that transition. He...

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Data Lineage For Your Pipelines - Episode 82

Some problems in data are well defined and benefit from a ready-made set of tools. For everything else, there's Pachyderm, the platform for data science that is built to scale. In this episode Joe Doliner, CEO and co-founder, explains how Pachyderm started as an attempt to make data provenance easier to track, how the platform is architected and used today, and examples of how the underlying principles manifest in the workflows of data engineers and data scientists as they collaborate on data projects. In addition to all of that...

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Build Your Data Analytics Like An Engineer - Episode 81

In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can...

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Using FoundationDB As The Bedrock For Your Distributed Systems - Episode 80

The database market continues to expand, offering systems that are suited to virtually every use case. But what happens if you need something customized to your application? FoundationDB is a distributed key-value store that provides the primitives that you need to build a custom database platform. In this episode Ryan Worl explains how it is architected, how to use it for your applications, and provides examples of system design patterns that can be built on top of it. If you need a foundation for your distributed systems, then FoundationDB...

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Running Your Database On Kubernetes With KubeDB - Episode 79

Kubernetes is a driving force in the renaissance around deploying and running applications. However, managing the database layer is still a separate concern. The KubeDB project was created as a way of providing a simple mechanism for running your storage system in the same platform as your application. In this episode Tamal Saha explains how the KubeDB project got started, why you might want to run your database with Kubernetes, and how to get started. He also covers some of the challenges of managing stateful services in Kubernetes and...

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Unpacking Fauna: A Global Scale Cloud Native Database - Episode 78

One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter's infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns....

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Index Your Big Data With Pilosa For Faster Analytics - Episode 77

Database indexes are critical to ensure fast lookups of your data, but they are inherently tied to the database engine. Pilosa is rewriting that equation by providing a flexible, scalable, performant engine for building an index of your data to enable high-speed aggregate analysis. In this episode Seebs explains how Pilosa fits in the broader data landscape, how it is architected, and how you can start using it for your own analysis. This was an interesting exploration of a different way to look at what a database can be.

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Serverless Data Pipelines On DataCoral - Episode 76

How much time do you spend maintaining your data pipeline? How much end user value does that provide? Raghu Murthy founded DataCoral as a way to abstract the low level details of ETL so that you can focus on the actual problem that you are trying to solve. In this episode he explains his motivation for building the DataCoral platform, how it is leveraging serverless computing, the challenges of delivering software as a service to customer environments, and the architecture that he has designed to make batch data management...

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