What is Apache Spark?
Apache Spark is a unified analytics engine for large-scale data processing with built-in modules for SQL, streaming, machine learning, and graph processing. Spark can run on Apache Hadoop, Kubernetes, on its own, in the cloud—and against diverse data sources. It provides rich APIs in Java, Scala, Python (PySpark), and R, making it accessible to a wide range of developers and data scientists.
On Google Cloud, Apache Spark is is transformed into a "Data-to-AI" platform. By leveraging serverless options and breakthrough performance enhancements like the Lightning Engine, Google Cloud solves the "tuning tax" associated with traditional Spark deployments. Deep integrations into a unified data and AI platform allow users to move from raw data to AI-driven action faster than ever before.
Apache Spark versus Apache Hadoop
One common question is when do you use Apache Spark versus Apache Hadoop? While Hadoop is used primarily for disk-heavy operations with the MapReduce paradigm, Spark is a more flexible and often more costly in-memory processing architecture. Spark is a fast general-purpose cluster computation engine that can be deployed in a Hadoop cluster or stand-alone mode. Understanding the features of each will guide your decisions on which to implement based on your workload's latency and memory requirements.
Apache Spark ecosystem and components
The Spark ecosystem includes five key components, each enhanced by Google Cloud’s infrastructure:
- Spark Core: The foundational execution engine, managing distributed task dispatching and I/O. It introduced Resilient Distributed Datasets (RDDs), immutable distributed collections of objects processed in parallel with fault tolerance.
- Spark SQL: The module for working with structured data using DataFrames. Google Cloud further accelerates these operations with the Lightning Engine, delivering significant speedups without the need for manual tuning.
- Spark Streaming: Enables scalable, fault-tolerant streaming solutions for both batch and real-time jobs.
- MLlib: A scalable machine learning library. When combined with Vertex AI, MLlib workflows can be seamlessly integrated into MLOps pipelines, and development can be enhanced with Gemini for coding and troubleshooting.
- GraphX: The API for graphs and graph-parallel computation.
Unique value for Data Scientists and Engineers
Google Cloud provides a specialized environment that addresses the unique needs of data professionals:
- Integrated development in BigQuery Studio: Data scientists can author and execute Spark code directly in BigQuery Studio notebooks. This provides a unified experience across Spark and BigQuery using a single queryable metadata service.
- AI-assisted productivity with Gemini: Leverage Gemini to assist with the entire lifecycle—from development and deployment to monitoring and troubleshooting complex PySpark jobs.
- Zero-Ops serverless execution: Eliminate the operational burden of managing clusters. With Serverless Spark, you can submit a single command and let Google handle the rest—no clusters to create, configure, or manage.
- Unified governance: Use Dataplex Universal Catalog to manage data and AI governance, providing semantics for agents and ensuring a consistent data lifecycle from ingestion to AI-driven insights.
Related products and services
While Apache Spark is an open-source framework, Google Cloud offers a suite of tools that automate, accelerate, and govern your Spark workloads:
Severless Spark (Dataproc)The industry's first first-party serverless Spark service, ideal for zero-ops, ETL, data science, and interactive analytics.
DataprocRun managed Apache Spark clusters with full control over configurations. Enhanced with the Lightning Engine for faster Spark performance.
BigQueryA serverless, high performance autonomous data to ai platform that integrates Spark for unified analytics.
BigLakeProvides fully managed Apache Iceberg tables with interoperability across BigQuery and open-source engines like Spark to accelerate streaming and AI use cases.
Lightning EngineThe processing engine available in 'Serverless' and Dataproc to accelerate workloads.
Dataplex Universal CatalogA unified catalog for data and AI that provides governance and semantic consistency across your entire data lakehouse.
Additional resources
- Dataproc Serverless for Apache Spark documentation: The official guide for running Spark batches and interactive sessions without managing infrastructure.
- Running Spark in BigQuery Studio: A guide specifically for data scientists on how to author and execute PySpark code directly within the BigQuery workspace.
- Apache Iceberg on Google Cloud guide: Detailed instructions on how to configure and use the Apache Iceberg table format with Spark and BigLake.
- Spark BigQuery Connector (GitHub): Official, publicly accessible documentation and repository for the open-source connector developed by Google.
- Google Cloud Blog: Accelerating Spark with Lightning Engine: Technical articles and performance benchmarks that demonstrate the real-world impact of our performance enhancements.
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