Apache Spark GraphFrames articles

4-day workshop Β· In-person or online

What would it take for you to trust your Databricks pipelines in production?

A 3-day bug hunt on a 3-person team costs up to €7,200 in lost engineering time. This workshop teaches you to prevent that β€” unit tests, data tests, and integration tests for PySpark and Databricks Lakeflow, including Spark Declarative Pipelines.

Unit, data & integration tests
Medallion architecture & Lakeflow SDP
Max 10 participants Β· production-ready templates
See the full curriculum β†’ €7,000 flat fee Β· cohort of up to 10
Bartosz Konieczny
Bartosz
Konieczny

Motifs finding in GraphFrames

In the previous post in GraphFrames category I mentioned the motifs finding feature and promised to write a separate post about it. After several weeks dedicated to the Apache Spark 2.4.0 features, I finally managed to find some time to explore the motifs finding in GraphFrames.

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Creating graphs in GraphFrames

The Project Tungsten revolutionized Apache Spark ecosystem. Thanks to the new row-based data structure the jobs became more performant and easier to create. This revolution first affected the batch processing and later the streaming one. As of writing the following article, the graph processing is still not impacted but hopefully GraphFrames project can change this.

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