graph partitioning 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

Graph partitioning

As told many times in previous posts, one of the most challenging tasks in distributed graph processing is the partitioning. Connected nature of the graph components makes the partitioning hard. Hopefully, the researchers continue to propose the solutions.

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Edge partitioning strategies

Previously we've learned about the vertices and edges representations in Apache Spark GraphX. At this moment to not introduce too many new concepts at once, we deliberately omitted the discovery of edges partitioning. Luckily, a new week comes and it lets us discuss that.

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