In this episode, Nicolay Gerold interviews John Wessel, the founder of Agreeable Data, about data orchestration. They discuss the evolution of data orchestration tools, the popularity of Apache Airflow, the crowded market of orchestration tools, and the key problem that orchestrators solve. They also explore the components of a data orchestrator, the role of AI in data orchestration, and how to choose the right orchestrator for a project. They touch on the challenges of managing orchestrators, the importance of monitoring and optimization, and the need for product people to be more involved in the orchestration space. They also discuss data residency considerations and the future of orchestration tools.

Sound Bites

"The modern era, definitely airflow. Took the market share, a lot of people running it themselves." "It's like people are launching new orchestrators every day. This is a funny one. This was like two weeks ago, somebody launched an orchestrator that was like a meta-orchestrator." "The DAG introduced two other components. It's directed acyclic graph is what DAG means, but direct is like there's a start and there's a finish and the acyclic is there's no loops."

Key Topics

The evolution of data orchestration: From basic task scheduling to complex DAG-based solutions
What is a data orchestrator and when do you need one? Understanding the role of orchestrators in handling complex dependencies and scaling data pipelines.
The crowded market: A look at popular options like Airflow, Daxter, Prefect, and more.
Best practices: Choosing the right tool, prioritizing serverless solutions when possible, and focusing on solving the use case before implementing complex tools.
Data residency and GDPR: How regulations influence tool selection, especially in Europe.
Future of the field: The need for consolidation and finding the right balance between features and usability.

John Wessel:

Nicolay Gerold:

Data orchestration, data movement, Apache Airflow, orchestrator selection, DAG, AI in orchestration, serverless, Kubernetes, infrastructure as code, monitoring, optimization, data residency, product involvement, generative AI.

Chapters

00:00 Introduction and Overview

00:34 The Evolution of Data Orchestration Tools

04:54 Components and Flow of Data in Orchestrators

08:24 Deployment Options: Serverless vs. Kubernetes

11:14 Considerations for Data Residency and Security

13:02 The Need for a Clear Winner in the Orchestration Space

20:47 Optimization Techniques for Memory and Time-Limited Issues

23:09 Integrating Orchestrators with Infrastructure-as-Code

24:33 Bridging the Gap Between Data and Engineering Practices

27:2 2Exciting Technologies Outside of Data Orchestration

30:09 The Feature of Dagster

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