Apache Hadoop

Founded by Arun C Murthy in 2006

Store and process huge datasets across distributed computer clusters with automatic failure handling

Pricing model

Free

Rating

★ Mixed Reviews

Time to value

Long Setup (> 1 day)

You can use Apache Hadoop to manage massive amounts of data across clusters of standard computers without worrying about individual hardware failures. The system splits large files across multiple machines using its distributed file system and schedules computational workloads directly where the data lives. It coordinates cluster resources efficiently and processes heavy data analytics jobs in parallel. This setup lets you scale storage and processing capacity from a single server to thousands of machines simply by adding more commodity hardware to your network.

What Apache Hadoop does

Distribute large datasets across clustered storage nodesSchedule and balance parallel computation tasks across serversRecover automatically from individual node hardware failuresExecute MapReduce jobs across distributed data filesConnect cluster storage directly to cloud storage buckets like Amazon S3Monitor node availability and data replica consistency across the clusterDistributed file system for high-throughput data access across computer clustersAutomatic failure detection and recovery at the application layer without hardware redundancyCluster resource management and job scheduling through YARNParallel data processing using simple distributed MapReduce programming modelsNative connectors for cloud object storage including Amazon S3Scalable architecture expanding from single servers to thousands of machinesLean packaging options that omit bulky third-party SDK dependencies

Pricing breakdown

Pricing model: Free

Tutorials & Demos

Frequently asked

Apache Spark

Used by

Jacob Bourchier

Startup Founder @ Applied Industrial Technologies

In their stack

Meriç Doğan Sipahi

Software Engineer @ CyberFirenze

In their stack

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