Big Data Mastery Roadmap
This article outlines a comprehensive series of over 500 planned tutorials covering Java advanced features, distributed theory, Hadoop, Spark, Flink, and various big‑data storage and processing technologies, designed to guide engineers transitioning into big‑data development from fundamentals to expert level.
The series "Big Data Mastery Roadmap" aims to provide engineers who wish to enter big‑data development or transition from backend roles with a clear learning path, citing numerous references and correcting errors from existing resources.
The planned curriculum, expected to exceed 500 articles, currently includes more than 40 published topics such as Java advanced features (multithreading, concurrency, collections), JVM deep dive, distributed system fundamentals (Zookeeper Paxos, Netty, RPC), core big‑data frameworks (NIO, MapReduce, HDFS, YARN), OLAP engine tuning (HiveSQL), columnar storage (HBase), NoSQL (Redis), messaging (Kafka), Spark (three‑part series), Flink (multiple parts), and big‑data algorithms.
Additional sections cover Hadoop’s three core components, Spark Streaming, and detailed Flink source analysis, including JobManager, TaskManager, operators, networking, checkpointing, scheduling, load balancing, and error handling.
The author encourages readers to follow the public account "Big Data Technology and Architecture" for updates and concludes with a personal note emphasizing the challenges and rewards of the learning journey.
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Big Data Technology & Architecture
Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
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