Workflows

Stick All Of Your Systems And Data Together With SaaSGlue As Your Workflow Manager - Episode 201

At the core of every data workflow is an orchestration engine (or several). Deploying, managing, and scaling that orchestration can consume a large fraction of a data team’s energy so it is important to pick something that provides the power and flexibility that you need. SaaSGlue is a managed service that lets you connect all of your systems, across clouds and physical infrastructure, and spanning all of your programming languages. In this episode Bart and Rich Wood explain how SaaSGlue is architected to allow for a high degree of flexibility in usage and deployment, their experience building a business with family, and how you can get started using it today. This is a fascinating platform with an endless set of use cases and a great team of people behind it.

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Building A Knowledge Graph From Public Data At Enigma With Chris Groskopf - Episode 50

There are countless sources of data that are publicly available for use. Unfortunately, combining those sources and making them useful in aggregate is a time consuming and challenging process. The team at Enigma builds a knowledge graph for use in your own data projects. In this episode Chris Groskopf explains the platform they have built to consume large varieties and volumes of public data for constructing a graph for serving to their customers. He discusses the challenges they are facing to scale the platform and engineering processes, as well as the workflow that they have established to enable testing of their ETL jobs. This is a great episode to listen to for ideas on how to organize a data engineering organization.

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Putting Airflow Into Production With James Meickle - Episode 43

The theory behind how a tool is supposed to work and the realities of putting it into practice are often at odds with each other. Learning the pitfalls and best practices from someone who has gained that knowledge the hard way can save you from wasted time and frustration. In this episode James Meickle discusses his recent experience building a new installation of Airflow. He points out the strengths, design flaws, and areas of improvement for the framework. He also describes the design patterns and workflows that his team has built to allow them to use Airflow as the basis of their data science platform.

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Dask with Matthew Rocklin - Episode 2

There is a vast constellation of tools and platforms for processing and analyzing your data. In this episode Matthew Rocklin talks about how Dask fills the gap between a task oriented workflow tool and an in memory processing framework, and how it brings the power of Python to bear on the problem of big data.

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