Comprehensive Big Data Learning Path and Interview Knowledge Map

In September 2019 the author published a popular article "Big Data Learning and Interview Knowledge Map", which became one of the most read posts on the platform.

Two years later, with industry changes and national policies, the author updates the knowledge map to reflect the new era of big‑data professional development.

The guide is divided into five parts: learning path overview, detailed learning path, video & book recommendations, interview preparation, and advice on building a knowledge system.

Key technical foundations include programming languages (Java, Scala, Python), Linux basics, MySQL, computer networking, operating systems, data structures, algorithms, and core Java concepts such as locks, multithreading, JUC containers, JVM, NIO, and RPC frameworks.

Distributed system theory covers concepts like cluster, load balancing, consistency, 2PC/3PC, CAP, Paxos, Raft, ZAB, distributed locks, transactions, and ID generators.

Network communication focuses on Netty architecture (Reactor, Pipeline, Handler) and its threading model, serialization, flow control, graceful shutdown, and SSL/TLS support.

Offline computing sections detail Hadoop components (MapReduce, HDFS, YARN, Hive, HBase) and practical skills such as writing MapReduce jobs, configuring HDFS, and Hive optimization.

Message‑queue knowledge covers Kafka fundamentals, architecture, topics, partitions, ISR, leader election, reliability, exactly‑once semantics, and comparison with other MQs.

Real‑time computing explores Spark (Core, Streaming, SQL, Structured Streaming, MLlib) and Flink (core, APIs, state management, connectors), including their execution models and performance tuning.

Data‑warehouse and data‑lake concepts, including schema design, governance, and frameworks like Hudi and Iceberg, are also discussed.

Algorithmic topics include inverted indexes, Top‑N, Bloom filters, trie structures, and basic machine‑learning algorithms.

Backend engineering skills such as Spring, MyBatis, SpringBoot, DDD, and MVC are recommended for solid engineering practice.

The interview section points to additional articles and a CSDN outline for systematic interview preparation.

Finally, the author advises building a personal knowledge system, regularly reviewing and organizing resources, and maintaining a holistic view of past, present, and future technologies.

Netty 的 Buffer
Netty 的 Reactor
Netty 的 Pipeline
Netty 的 Handler
Netty 的 ChannelHandler
Netty 的 LoggingHandler
Netty 的 TimeoutHandler
Netty 的 CodecHandler
Netty 的 MessageToByteEncoder