Apache Spark review
A free, self-hosted engine for distributed batch, streaming, SQL, and machine learning workloads.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. It is designed for developers, data engineers, analysts, and organizations running workloads across clusters. Spark handles distributed batch processing, incremental stream processing through Structured Streaming, SQL queries, DataFrames and Datasets, machine learning with MLlib, and custom transformations through user-defined functions. It runs on Windows, macOS, and Linux, either locally or on clusters managed by Spark Standalone, Hadoop YARN, or Kubernetes.
The main appeal is breadth within a single processing engine. Teams can work through Python, SQL, Scala, Java, or R interfaces, while connecting to Hive, Avro, Parquet, ORC, JSON, and JDBC sources. Its broader ecosystem includes Apache Kafka, Apache Airflow, Delta Lake, Apache Iceberg, Cassandra, SQL Server, MongoDB, and Elasticsearch. Support for incremental loading, change data capture, custom code, and mixed transformation modes makes Spark suitable for pipelines that extend beyond straightforward batch extraction and loading. MLlib also supports machine learning workloads alongside data preparation and analytics.
Spark’s open-source, free model keeps software licensing separate from the infrastructure needed to operate it. The product is self-hosted rather than a hosted ETL SaaS service, so organizations must choose and manage their deployment environment, including standalone clusters, YARN, or Kubernetes. That model fits teams with engineering capacity and requirements for distributed processing, flexible programming interfaces, or control over execution environments. Organizations seeking a managed interface, guided onboarding, or a packaged subscription service should consider a hosted ETL alternative instead. Spark is the stronger fit when scale, custom transformations, and ecosystem compatibility matter more than low-maintenance operation.
Apache Spark pros and cons
- Where it wins
- Distributed batch and streaming processing with Structured Streaming
- Python, SQL, Scala, Java, and R interfaces
- Broad connectivity across files, databases, warehouses, and data platforms
- Where it doesn't
- Self-hosted deployment requires cluster and infrastructure management
- More technical than hosted ETL tools for non-developer teams
- No subscription tiers or packaged hosted SaaS experience
Apache Spark fact sheet, pricing and score →
Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.
Last updated · How we research and update