Learning route of Java big data processing framework

Java Big Data Processing Framework Learning Route: Master the basic knowledge of Hadoop ecosystem Spark Proficient in core concepts, use SQL to query data, learn real-time data processing and machine learning Flink In-depth understanding of stream processing, event time processing and fault tolerance Practical case: MapReduce Process log data, analyze social media data with Spark, and monitor IoT devices with Flink. Advanced learning: distributed systems, cloud computing, big data analysis technology

Java big data processing framework Learning route

Prerequisite knowledge:

• Java basics
• Data structures and algorithms
• Hadoop basics

Route planning:

1. Hadoop ecosystem (master)

• Hadoop distributed file system ( HDFS)
• MapReduce programming model
• YARN resource management
• Apache Hive data warehouse
• Apache HBase database

2. Spark (Mastery)

• Core concepts (RDD, transformations and operations)
• Using Spark SQL for data query
• Apache Spark Streaming real-time Data processing
• Apache Spark ML machine learning library

3. Flink (in-depth understanding)

• Stream processing engine and State calculation
• Event time and window processing
• Fault tolerance and high availability
• Apache Flink Table API

Practical case:

• Use Hadoop MapReduce to process massive log data
• Use Spark to analyze social media data
• Use Flink to monitor IoT devices in real time

Learning resources:

• Apache official documentation
• Online courses (Coursera, edX)
• Books (Hadoop: The Definitive Guide, Spark in Action)
• Blog and community discussion

Advanced learning:

• Distributed systems
• CloudComputing
• Big data analysis technology (machine learning, artificial intelligence)

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