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boto3 or aws cli for s3 … Python vs Bash and other thoughts.

For any Data Engineer working on aws for any length of time, there is one task that always seems to come up and never go away. Manipulating files on s3 a bucket on aws is something I’ve had to do for years, it just never goes away. It’s always something … listing files, moving files, copying files, checking for files, getting the last modified file, checking file sizes, downloading files … it pretty much never ends.

Luckily aws provides a few tools to make these easy, their handy cli for command-line work, or the trusty boto3 Python package. I want to give an introduction to the common commands Data Engineers have to run with both the aws cli and boto3 to perform various common tasks. We will then compare and contrast which tool to use in our pipelines and the pros and cons of each.

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Part 4 – Keys To Success – Idempotency and Partitioning.

As the road winds on we come to Part 4, of our 5 Part Series on Data Warehouses, Lakes, and Lake Houses. Finally, we are getting to some fun topics after all the boring stuff. Today I want to talk about the two keys to success in your Data Lakes … Idempotency and Partitioning. I firmly believe these two concepts are the cornerstones of the new exciting, or not-so-exciting world of Data Lakes and Lake Houses, without which your data and pipelines go the way of the dodo.

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Databricks + Delta Lake MERGE duplicates – Deterministic vs Non-Deterministic ETL.

Is there any problem more classic to the Data Lakes and Data Warehouses than duplicate records? You would think after doing the same ETL for over a decade I could avoid the issue, apparently not. It’s good never to think too highly of one’s self, the duplicates can get us all. Today I want to talk about a wonderful feature of Databricks + Delta Lake MERGE statements that are perfect for quietly and insidiously injecting duplicates into your Data Warehouse or Data Lake. This is a great trick to play on your unsuspecting coworkers.

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End-to-End Pipeline Integration Testing for Databricks + Delta Lake.

The testing never ends. Tests tests tests, and more tests. When it comes to data engineering and data pipelines it seems good practices are finally catching up after years. In the past, the data engineering community took a lot of heat, and rightly so, for not adopting good software engineering principles, especially in data pipelines.

In the defense of many data engineers, because of the varied backgrounds people come from, some were never taught or realized the importance of good software design and testing practices. Sure, it always “takes more time” upfront to design data pipelines with code that is functional and unit-testable, and worse, able to be integration tested from end to end. It requires some foresight and thought in both data architecture and pipeline design to enable complete testability.

Integration testing end-to-end in an automated manner is a tough nut to crack. How can you do such a thing on massive pipelines that crunch hundreds of TBs of data? With a little creativity.

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