Core Technologies and Architectures for Big Data 🚧

Intermediate

Handling Big Data requires specialized architectures and tools designed for distributed processing. The most prominent framework is Apache Hadoop, which utilizes the MapReduce programming model to process large datasets across clusters of commodity hardware. Complementing Hadoop is Apache Spark, known for in-memory processing speed, making it suitable for iterative algorithms and real-time analytics.

Hadoop Ecosystem Components:

  • HDFS (Hadoop Distributed File System): Storage layer
  • MapReduce: Batch processing paradigm
  • YARN: Resource management
  • Hive & Pig: High-level query and scripting interfaces

Apache Spark:

  • In-memory computation engine
  • Supports SQL, streaming, machine learning, and graph processing
  • Significantly faster than traditional MapReduce

Diagram:

+-------------------+
| Data Sources      |
+--------+----------+
         |
+--------v----------+
| Hadoop/HDFS       |
+--------+----------+
         |
+--------v----------+
| Spark Engine      |
+-------------------+