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, and R, making it accessible to a wide range of developers and data scientists. Its Python API, PySpark, also integrates well with popular libraries like Pandas for data manipulation. On Google Cloud, Apache Spark is taken to the next level with serverless options, breakthrough performance enhancements like the Lightning Engine (in Preview), and deep integrations into a unified data and AI platform.

One common question is when do you use Apache Spark versus Apache Hadoop? They are both among the most prominent distributed systems on the market today. Both are similar Apache top-level projects that are often used together. 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. Understanding the features of each will guide your decisions on which to implement when.

Learn how Google Cloud empowers you to run Apache Spark workloads in simpler, integrated, and more cost-effective ways. You can leverage Google Cloud Serverless for Apache Spark for zero-ops development or use Dataproc for managed Spark clusters.

Apache Spark overview

The Spark ecosystem includes five key components:

Spark Core is a general-purpose, distributed data processing engine. It's the foundational execution engine, managing distributed task dispatching, scheduling, and basic I/O. Spark Core introduced the concept of Resilient Distributed Datasets (RDDs), immutable distributed collections of objects that can be processed in parallel with fault tolerance. On top of it, sit libraries for SQL, stream processing, machine learning, and graph computation — all of which can be used together in an application.
Spark SQL is the Spark module for working with structured data and introduced DataFrames, which provide a more optimized and developer-friendly API over RDDs for structured data manipulation. It lets you query structured data inside Spark programs, using either SQL, or a familiar DataFrame API. Spark SQL supports the HiveQL syntax and allows access to existing Apache Hive warehouses. Google Cloud further accelerates Spark job performance, especially for SQL, and DataFrame operations, with innovations like the Lightning Engine, delivering significant speedups for your queries and data processing tasks when running Spark on Google Cloud.
Spark Streaming makes it easy to build scalable, fault-tolerant streaming solutions. It brings the Spark language-integrated API to stream processing, so you can write streaming jobs in the same way as batch jobs using either DStreams or the newer Structured Streaming API built on DataFrames. Spark Streaming supports Java, Scala, and Python, and features stateful, exactly-once semantics out of the box.
MLlib is the Spark scalable machine learning library with tools that make practical ML scalable and easy. MLlib contains many common learning algorithms, such as classification, regression, recommendation, and clustering. It also contains workflow and other utilities, including feature transformations, ML pipeline construction, model evaluation, distributed linear algebra, and statistics. When combined with Google Cloud's Vertex AI, Spark MLlib workflows can be seamlessly integrated into MLOps pipelines, and development can be enhanced with Gemini for coding and troubleshooting.
GraphX is the Spark API for graphs and graph-parallel computation. It's flexible and works seamlessly with both graphs and collections — unifying extract, transform, load; exploratory analysis; and iterative graph computation within one system.

Across these components, Google Cloud provides an optimized environment. For instance, the Lightning Engine boosts Spark and DataFrame performance, while Google Cloud Serverless for Apache Spark simplifies deployment and management, and Gemini enhances developer productivity in notebook environments like BigQuery Studio and Vertex AI Workbench.

How Apache Spark works

Apache Spark's power comes from a few core architectural principles:

In-memory processing: Spark loads data into memory significantly speeding up iterative algorithms and interactive queries compared to disk-based systems.
Distributed execution: It operates on a cluster of machines. A driver program coordinates executors (worker processes) that run tasks in parallel on different data partitions.
RDDs and DataFrames: Resilient Distributed Datasets (RDDs) are the basic fault-tolerant data abstraction. DataFrames, built on RDDs, provide a richer, schema-aware API for structured data, enabling optimizations through the Catalyst optimizer.
Lazy evaluation and DAGs: Spark builds a Directed Acyclic Graph (DAG) of operations. Transformations are "lazy" (not computed immediately), allowing Spark to optimize the entire workflow before an "action" triggers execution.

What are the benefits of Apache Spark?

Speed

Spark's in-memory processing and DAG scheduler enable faster workloads than Hadoop MapReduce, especially for iterative tasks. Google Cloud boosts this speed with optimized infrastructure and the Lightning Engine.

Ease of use

Spark's high-level operators simplify parallel app building. Interactive use with Scala, Python, R, and SQL enables rapid development. Google Cloud offers serverless options and integrated notebooks with Gemini for enhanced ease of use.

Scalability

Spark offers horizontal scalability, processing vast data by distributing work across cluster nodes. Google Cloud simplifies scaling with serverless autoscaling and flexible Dataproc clusters.

Generality

Spark powers a stack of libraries, including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.

Open source framework innovation

Spark leverages the power of open source communities for rapid innovation and problem-solving, leading to faster development and time to market. Google Cloud embraces this open spirit, offering standard Apache Spark while enhancing its capabilities.

Why choose Spark over a SQL-only engine?

Apache Spark is a fast general-purpose cluster computation engine that can be deployed in a Hadoop cluster or stand-alone mode. With Spark, programmers can write applications quickly in Java, Scala, Python, R, and SQL which makes it accessible to developers, data scientists, and advanced business people with statistics experience. Using Spark SQL, users can connect to any data source and present it as tables to be consumed by SQL clients. In addition, interactive machine learning algorithms are easily implemented in Spark.

With a SQL-only engine like Apache Impala, Apache Hive, or Apache Drill, users can only use SQL or SQL-like languages to query data stored across multiple databases. That means that the frameworks are smaller compared to Spark. However, on Google Cloud, you don't have to make a strict choice; BigQuery provides powerful SQL capabilities, Google Cloud Serverless for Apache Spark and Dataproc for a Spark and Hadoop managed service allows you to use Spark's versatility, often on the same data through BigLake Metastore and open formats.

How are companies using Spark?

Many companies are using Spark to help simplify the challenging and computationally intensive task of processing and analyzing high volumes of real-time or archived data, both structured and unstructured. Spark also enables users to seamlessly integrate relevant complex capabilities like machine learning and graph algorithms. Common applications include:

Large-scale ETL/ELT
Real-time data processing
Machine learning
Interactive data exploration
Graph analytics

Data engineers

Data engineers use Spark for coding and building data processing jobs — with the option to program in an expanded language set. On Google Cloud, data engineers can leverage Google Cloud Serverless for Apache Spark for zero-ops ETL/ELT pipelines or use Dataproc for managed cluster control, all integrated with services like BigQuery and Dataplex Universal Catalog for governance.

Data scientists

Data scientists can have a richer experience with analytics and ML using Spark with GPUs. The ability to process larger volumes of data faster with a familiar language can help accelerate innovation. Google Cloud provides robust GPU support for Spark and seamless integration with Vertex AI, allowing data scientists to build and deploy models faster. They can leverage various notebook environments like BigQuery Studio, Vertex AI Workbench, or connect their preferred IDEs such as Jupyter and VS Code. This flexible development experience, combined with Gemini, helps accelerate their workflow from initial exploration to production deployment.

Running Apache Spark on Google Cloud

Optimize your Spark experience with Google Cloud

Google Cloud Serverless for Apache Spark: For a truly zero-ops experience, run your Spark jobs without managing any clusters. Benefit from near-instant startup, automatic scaling, the performance boost of the Lightning Engine and Gemini. Ideal for ETL, data science, and interactive analytics, especially when integrated with BigQuery.
Dataproc: When you need more control over your cluster environment or require specific Hadoop ecosystem components alongside Spark, Dataproc provides a fully managed service. Dataproc simplifies cluster creation and management and also benefits from Lightning Engine enhancements for Spark performance.
A unified and open ecosystem: Running Spark on Google Cloud means seamless integration with services like BigQuery for unified analytics, Vertex AI for MLOps, BigLake Metastore for open metadata sharing, and Dataplex Universal Catalog for comprehensive data governance, all supporting an open lakehouse architecture.

Related products and services

Google Cloud offers a suite of powerful tools that complement and integrate with Apache Spark. Key services like Google Cloud Serverless for Apache Spark, Dataproc, BigQuery, and integrations with technologies like Apache Kafka enable you to build comprehensive, context-rich applications and, new analytics solutions, turning data into actionable insights.