Apache Spark

Founded by Dongjoon Hyun in 2014

Process massive batch and streaming data across distributed clusters of computers

Pricing model

Free

Rating

★ People love it

Time to value

Moderate Setup (1-3 hours)

You can use Apache Spark to process large-scale datasets across distributed computer clusters. It lets you write data processing pipelines using Python, SQL, Scala, Java, or R. You can handle batch data processing and real-time data streaming within the same framework. The system includes built-in modules for SQL queries, streaming computation, machine learning algorithms, and graph processing. It executes queries quickly using an in-memory execution engine and adaptive query planning, making it practical to perform exploratory data analysis and train models across thousands of machines.

What Apache Spark does

Read and parse multi-terabyte JSON or CSV filesExecute distributed SQL queries on unstructured dataTrain random forest models across cluster nodesFilter and transform real-time event streamsJoin large datasets using adaptive query executionGenerate statistical summaries on massive data tablesDistributed data processing across multiple worker nodesUnified API supporting Python, SQL, Scala, Java, and RReal-time stream processing with structured streamingBuilt-in machine learning library for distributed model trainingAdaptive query execution to adjust plans at runtimeDirect querying of structured tables and unstructured filesIn-memory caching for rapid iterative computations

Pricing breakdown

Pricing model: Free

Frequently asked

Docker

Kubernetes

Used by

J__Navarro

SEO Specialist @ NavarroMD

In their stack

Jacob Bourchier

Startup Founder @ Applied Industrial Technologies

In their stack

Meriç Doğan Sipahi

Software Engineer @ CyberFirenze

In their stack

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