What Is Apache Spark?
Apache Spark is an open-source engine for processing large volumes of data quickly across a cluster of machines. Where a single computer would be overwhelmed, Spark splits the work across many machines and coordinates the results, which lets it process datasets far too big for one server. It handles a wide range of workloads, batch processing, streaming, SQL queries, and machine learning, on one engine, which is part of why it became a standard in big data and the lakehouse.
How Spark Works
Spark’s speed comes largely from working in memory. Earlier big-data engines wrote intermediate results to disk between steps; Spark keeps data in memory across steps where it can, which is far faster for the multi-step processing analytics requires. It distributes a job across a cluster, with a driver coordinating and executors doing the work in parallel, and it handles the failures and retries that are inevitable at scale.
What Spark Is Used For
Spark is the processing engine behind many data platforms. It runs the heavy transformations that turn raw data into analytics-ready tables, powers large-scale ETL and ELT, processes streaming data, and trains machine learning models on large datasets. In a lakehouse, Spark is often the engine that reads and writes the open data files and applies the transformation logic.
Spark and the Lakehouse
Spark and the lakehouse grew up together. The lakehouse stores data in open formats on cloud storage; Spark is one of the main engines that processes that data in place, applying transformations and serving large-scale analytics. Platforms like Databricks are built around Spark, and Microsoft Fabric includes Spark-based engineering alongside its other workloads.
Spark in an ERP Data Foundation
Turning large volumes of ERP data into a governed model takes a processing engine that can handle the transformations at scale. Spark is often that engine under the hood, running the jobs that clean, join, and reshape source data into analytics-ready form. QuickLaunch builds the governed foundation on top, so teams get the result, reliable, modeled data ready for Power BI, without having to write and tune the distributed processing themselves.
Frequently Asked Questions
What is Apache Spark used for?
Large-scale data processing across a cluster of machines. It runs batch transformations, streaming, SQL queries, and machine learning on big datasets, and is a common engine behind data platforms and lakehouses.
Why is Spark fast?
Largely because it works in memory, keeping data in memory across processing steps rather than writing to disk between them, and because it distributes work across many machines in parallel.
What is the difference between Spark and Airflow?
Spark is a processing engine that does the data work. Airflow is an orchestrator that schedules and coordinates jobs. They often work together: Airflow triggers and monitors Spark jobs.
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