Big Data & Distributed Computing
Process datasets too large for one machine using Spark, Databricks and the Hadoop ecosystem.
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When data outgrows a single machine, the tools and the mental model both change. These courses teach distributed storage and processing: Apache Spark with the DataFrame API and Spark SQL, partitioning and shuffle behaviour, and the performance tuning that separates a job running in minutes from the same job running for hours. You will meet the Databricks platform, the Delta Lake, Iceberg and Hudi table formats, and the Hadoop and Hive components still running in many enterprises. Relevant when you are working at terabyte scale or joining a team whose existing platform is built on Spark.