Exploring The TileDB Universal Data Engine - Episode 146

August 17, 2020 tmacey Comments Off on Exploring The TileDB Universal Data Engine - Episode 146

Most databases are designed to work with textual data, with some special purpose engines that support domain specific formats. TileDB is a data engine that was built to support every type of data by using multi-dimensional arrays as the foundational primitive. In this episode the creator and founder of TileDB shares how he first started working on the underlying technology and the benefits of using a single engine for efficiently storing and querying any form of data. He also discusses the shifts in database architectures from vertically integrated monoliths to separately deployed layers, and the approach he is taking with TileDB cloud to embed the authorization into the storage engine, while providing a flexible interface for compute. This was...

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Closing The Loop On Event Data Collection With Iteratively - Episode 145

August 10, 2020 tmacey Comments Off on Closing The Loop On Event Data Collection With Iteratively - Episode 145

Event based data is a rich source of information for analytics, unless none of the event structures are consistent. The team at Iteratively are building a platform to manage the end to end flow of collaboration around what events are needed, how to structure the attributes, and how they are captured. In this episode founders Patrick Thompson and Ondrej Hrebicek discuss the problems that they have experienced as a result of inconsistent event schemas, how the Iteratively platform integrates the definition, development, and delivery of event data, and the benefits of elevating the visibility of event data collection for improving the effectiveness of the resulting analytics. If you are struggling with inconsistent implementations of event data capture, lack of...

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A Practical Introduction To Graph Data Applications - Episode 144

August 4, 2020 tmacey Comments Off on A Practical Introduction To Graph Data Applications - Episode 144

Finding connections between data and the entities that they represent is a complex problem. Graph data models and the applications built on top of them are perfect for representing relationships and finding emergent structures in your information. In this episode Denise Gosnell and Matthias Broecheler discuss their recent book, the Practitioner's Guide To Graph Data, including the fundamental principles that you need to know about graph structures, the current state of graph support in database engines, tooling, and query languages, as well as useful tips on potential pitfalls when putting them into production. This was an informative and enlightening conversation with two experts on graph data applications that will help you start on the right track in your own...

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Build More Reliable Distributed Systems By Breaking Them With Jepsen - Episode 143

July 28, 2020 tmacey Comments Off on Build More Reliable Distributed Systems By Breaking Them With Jepsen - Episode 143

A majority of the scalable data processing platforms that we rely on are built as distributed systems. This brings with it a vast number of subtle ways that errors can creep in. Kyle Kingsbury created the Jepsen framework for testing the guarantees of distributed data processing systems and identifying when and why they break. In this episode he shares his approach to testing complex systems, the common challenges that are faced by engineers who build them, and why it is important to understand their limitations. This was a great look at some of the underlying principles that power your mission critical workloads.

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Making Wind Energy More Efficient With Data At Turbit Systems - Episode 142

July 21, 2020 tmacey Comments Off on Making Wind Energy More Efficient With Data At Turbit Systems - Episode 142

Wind energy is an important component of an ecologically friendly power system, but there are a number of variables that can affect the overall efficiency of the turbines. Michael Tegtmeier founded Turbit Systems to help operators of wind farms identify and correct problems that contribute to suboptimal power outputs. In this episode he shares the story of how he got started working with wind energy, the system that he has built to collect data from the individual turbines, and how he is using machine learning to provide valuable insights to produce higher energy outputs. This was a great conversation about using data to improve the way the world works.

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