Reviews onG2Capterra
55 reviews trackedThe Bottom Line
Entry price
Free, no paid tier
Biggest pro
Completely free and open-source under Apache License 2.0
Biggest con
Steep learning curve for cluster configuration and tuning
TL;DR - Apache Spark
- Open-source distributed engine for batch and streaming data processing
- Supports Python, SQL, Scala, Java, and R across single nodes or clusters
- Powers ML, ETL, and analytics for 80% of Fortune 500 companies
Pricing: Free forever
Best for: Individuals & startups
4.4/5 across review platforms
What is Apache Spark?
Apache Spark is an open-source unified analytics engine for large-scale data processing. It handles batch and real-time streaming workloads across Python, SQL, Scala, Java, and R, enabling distributed computing on single nodes or clusters. Used by 80% of Fortune 500 companies, Spark powers data engineering, data science, and machine learning pipelines at petabyte scale with adaptive query execution that delivers up to 8x faster performance on industry benchmarks.
Available on: Web, Linux
Pros & Cons
Pros
- Completely free and open-source under Apache License 2.0
- Massive community with 2,000+ contributors from industry and academia
- Handles both batch and streaming in a single engine
- Integrates with virtually every data tool in the modern stack
- Scales linearly from laptop to thousands of cluster nodes
- Mature ecosystem with extensive documentation and tutorials
Cons
- Steep learning curve for cluster configuration and tuning
- Requires significant infrastructure to run at scale
- Memory-intensive workloads can be expensive on cloud providers
- GraphX graph processing module is deprecated
- Debugging distributed jobs can be difficult
Ratings Across the Web
4.4(55 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Unified batch and real-time stream processingSQL analytics engine faster than most data warehousesMachine learning library (MLlib) for scalable model trainingStructured Streaming for continuous data pipelinesMulti-language support for Python, SQL, Scala, Java, and RAdaptive Query Execution for automatic performance tuningKubernetes-native deployment and cluster managementIntegration with pandas, scikit-learn, TensorFlow, and PyTorchPetabyte-scale exploratory data analysis without downsamplingDelta Lake and Apache Iceberg lakehouse support
Pricing
Free
Apache Spark is completely free to use with no hidden costs.
Reviews
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4.4/5
Across 55 verified user reviews on G2, Capterra
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Apache Spark FAQ
How does Apache Spark facilitate large-scale data processing?
Apache Spark is an open-source unified analytics engine designed for large-scale data processing. It handles both batch and real-time streaming workloads, enabling distributed computing across various programming languages. Spark powers data engineering, data science, and machine learning pipelines at petabyte scale.
Which teams benefit most from using Apache Spark?
Teams involved in big data analytics, ETL and data pipelines, and general analytics will find Apache Spark most beneficial. Its ability to process petabyte-scale data and integrate with numerous data tools makes it suitable for data engineers, data scientists, and machine learning practitioners. It is used by 80% of Fortune 500 companies for these purposes.
How is Apache Spark's pricing structured?
Apache Spark is completely free and open-source under the Apache License 2.0. There is no paid plan required to use the software. Users only incur costs related to the infrastructure needed to run Spark at scale.
What kind of challenges might users encounter when implementing Apache Spark?
Users might face a steep learning curve for cluster configuration and tuning due to its distributed nature. Running Spark at scale requires significant infrastructure, and memory-intensive workloads can become expensive on cloud providers. Debugging distributed jobs can also be a difficult process.
How does Apache Spark compare to Presto for data processing?
Apache Spark is a unified analytics engine that handles both batch and real-time streaming workloads, supporting data engineering, data science, and machine learning. It provides adaptive query execution for faster performance. Presto, while also a distributed query engine, focuses primarily on interactive SQL queries across various data sources.
Can Apache Spark integrate with existing data tools?
Yes, Apache Spark integrates with virtually every data tool in the modern stack. It has a mature ecosystem with extensive documentation and tutorials, supported by a massive community of over 2,000 contributors. This broad compatibility allows it to fit into diverse data environments.
Source: spark.apache.org