data engineering

TechChat Tuesdays #65: Redshift Execution Plans with Keith Gregory

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In this week’s TechChat, we welcome Keith Gregory, our Cloud & Data Engineering Practice Lead here at Chariot. Keith is a prolific writer both on the Chariot blog as well as on his own, and is a wealth of knowledge on all things AWS. We touch on Redshift execution plans, how to appropriately size Redshift … Read More

Athena Performance Comparison: Avro, JSON, and Parquet

In my “Friends Don’t Let Friends Use JSON” post, I noted that I preferred the Avro file format to Parquet, because it was easier to write code to use it. I expected some pushback, and got it: Parquet is “much” more performant. So I decided to do some benchmarking.

Unbalanced Data in Redshift

Decision support databases have a number of quirks that are not obvious to the casual user, particularly someone coming from an OLTP background. In this post I look at how unbalanced distributions can impact your query performance, how you can identify imbalances, and what you can do to fix them.

Avro Three Ways

In my last post I recommended using Avro for file storage in a data lake. It has the benefits of compact storage and a schema in every file that tells you what data it holds. In this post I show three ways to generate Avro files: one in Java, and two in Python.

Friends Don’t Let Friends Use JSON (in their data lakes)

I’ve never been a JSON hater, but I’ve recently run into enough pain with JSON as a data serialization format that my feelings are edging toward dislike. However, JSON is a fact of life in most data pipelines, especially those that receive event-stream data from a third-party supplier. This post reflects on some of the problems that I’ve seen, and solutions that I’ve used

Twenty Years of Big Data

More, cheaper, faster: our own Keith Gregory recounts the changes in big data, data storage, and data engineering over the last two decades.