Welcome to the ultimate, production-grade Big Data Learning Repository. This repository is rebuilt to serve as a comprehensive, step-by-step masterclass for software engineers, data engineers, and architects looking to master distributed systems, large-scale storage, stream processing, and data warehousing.
Each module in this guide is designed to go 100x deeper than basic command sheets, combining core theoretical architecture, system configuration math, interactive CLI command lists, troubleshooting tips, and interview traps.
Here is the step-by-step learning progression designed to take you from a system administrator to a Big Data Solution Architect.
flowchart TD
M1[Module 1: Linux & Bash Mastery] --> M2[Module 2: HDFS Storage]
M2 --> M3[Module 3: MapReduce Compute]
M2 --> M4[Module 4: YARN Resource Manager]
M3 & M4 --> M5[Module 5: Apache Hive Data Warehouse]
M5 --> M6[Module 6: Apache Spark Batch & Stream]
M6 --> M7[Module 7: Apache Kafka Event Streaming]
M7 --> M8[Module 8: NoSQL & Distributed Internals]
M8 --> M9[Module 9: Cluster Setup & Deployment]
style M1 fill:#f9f,stroke:#333,stroke-width:2px
style M2 fill:#bbf,stroke:#333,stroke-width:2px
style M5 fill:#dfd,stroke:#333,stroke-width:2px
style M6 fill:#fdd,stroke:#333,stroke-width:2px
style M7 fill:#ff9,stroke:#333,stroke-width:2px
style M8 fill:#f9d,stroke:#333,stroke-width:2px
style M9 fill:#dff,stroke:#333,stroke-width:2px
Follow this step-by-step path to master the ecosystem.
| Stage | Module | Key Focus Areas | Link to Guide |
|---|---|---|---|
| 0 | Roadmap & Study Guide | Study workflow, certifications, and project roadmap | Study Roadmap |
| 1 | Linux & Bash Mastery | Shell programming, processes, networking, resource limit tuning | Linux & Bash Guide |
| 2 | HDFS Storage Mastery | Storage internals, block allocation, high availability (HA), federation | HDFS Storage Guide |
| 3 | MapReduce Compute | Execution lifecycle, joins (map-side vs reduce-side), cache | MapReduce Guide |
| 4 | YARN Resource Management | Capacity & Fair schedulers, resource calculation, container limits | YARN Guide |
| 5 | Apache Hive Data Warehouse | Partitions, Buckets, ACID tables, ORC/Parquet optimization, CBO | Apache Hive Guide |
| 6 | Apache Spark (Core & SQL) | Catalyst, Tungsten, RDD/DataFrames, memory allocation, Join strategies | Spark Core & SQL |
| 6 | Apache Spark (Streaming) | Structured Streaming, Watermarks, sliding windows, stateful operations | Spark Streaming |
| 7 | Apache Kafka Streaming | Broker internals, partition sizing, replicas, idempotent producers | Apache Kafka Guide |
| 8 | NoSQL & Internals | LSM-trees, HBase, Cassandra, Paxos/Raft consensus, CAP Theorem | NoSQL & Internals |
| 9 | Cluster Setup & Deployment | core-site.xml, hdfs-site.xml, XML properties, Docker deployments |
Cluster Deployment |
If you wish to view your original cheat sheets and commands, they are safely preserved inside the /archive/ directory.