A practitioner’s guide to Apache Spark

Keynote:

  • Runnable blueprints and GitHub repositories: Deploy production-ready PySpark notebooks, Airflow DAGs, and Terraform templates to automate secure network setup, Google Cloud Storage (GCS) storage provisioning, and metadata federation.
  • Easier, smarter, and faster Spark operations: Master zero-ops serverless or managed cluster execution, history-based autotuning that dynamically prevents out-of-memory errors, and Lightning Engine’s native C++ vectorized execution for up to 4.9x faster performance than standard open-source Apache Spark.
  • Zero-copy lakehouse interoperability: Build an open storage plane standardizing on Apache Iceberg and the serverless Lakehouse runtime catalog to enable transactional multi-engine consistency across Spark, BigQuery, Flink, and Trino without format lock-in.

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