A practitioner’s guide to Apache Spark: Mastering the borderless lakehouse, data science, and real-time pipelines.

Get your report

First name

Last name

Business email

Calling Code

Business phone

Job title

Company name

Sign me up to receive news, product updates, event information, and special offers about Google Cloud from Google.

I understand my personal data will be processed in accordance with Google's Privacy Policy.

Modernize and scale your data platform with Google Cloud’s Managed Service for Apache Spark. Written specifically for data engineers, scientists, and architects, this guide delivers low-level architectures, step-by-step blueprints, and runnable code templates to build cost-effective pipelines using serverless execution, managed clusters, and vectorized native C++ processing.

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