• 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.

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.

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 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 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.

• 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.