Big Data Analytics with Hadoop
Big Data Analytics with Hadoop is the process of examining large and complex datasets using the Apache Hadoop framework. Hadoop provides a scalable, reliable, and cost-effective solution for storing and processing big data across distributed systems.
Key Features of Big Data Analytics with Hadoop
- Distributed data storage (HDFS)
- Parallel processing with MapReduce
- Scalable and fault-tolerant
- Supports various data formats
- Open-source and cost-effective
- Integrates with analytics tools (Hive, Pig, Spark)
Skills Needed Before Learning Big Data Analytics with Hadoop
- Basic knowledge of Linux/Unix
- Understanding of programming (Java, Python)
- Familiarity with SQL and databases
- Concept of distributed systems
- Basic data structures and algorithms
- Big Data & Hadoop
- Hadoop Architecture & HDFS
- MapReduce Programming
- Data Processing with Hive and Pig
- HBase and NoSQL Concepts
- Real-time Analytics with Spark
- Data Ingestion Tools (Sqoop, Flume)
- Mini Project / Case Study
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