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Big data framework comparison

Big data frameworks are designed to address two primary challenges: data storage and data computation.^[600-developer__big-data__big-data.md] In the ecosystem of big data processing, two major frameworks have emerged as standard solutions: Hadoop and Spark.

Hadoop

Hadoop is a comprehensive ecosystem for storing and processing large data sets. Its core components include:

  • Storage & Core: HDFS (storage), MapReduce (processing), and HBase (a NoSQL database).^[600-developer__big-data__big-data.md]
  • Analysis Engines: Tools like Hive and Pig are used for data analysis.^[600-developer__big-data__big-data.md]
  • Data Ingestion: Sqoop and Flume serve as engines for data collection and acquisition.^[600-developer__big-data__big-data.md]
  • Management & Workflow: The ecosystem includes HUE for web-based management and Oozie for workflow scheduling.^[600-developer__big-data__big-data.md]

Spark

Spark is a unified analytics engine for large-scale data processing. Key aspects of the Spark framework include:

  • Language: It is natively built on Scala.^[600-developer__big-data__big-data.md]
  • Core Processing: Spark code is utilized for data computation.^[600-developer__big-data__big-data.md]
  • Modules:
    • Spark SQL: For structured data processing.^[600-developer__big-data__big-data.md]
    • Spark Streaming: Specifically designed for stream computing.^[600-developer__big-data__big-data.md]

Sources

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