Hadoop Big Data

Index:

• Introduction to Big Data: Understand the concept of Big Data, its challenges, and how Hadoop addresses these challenges with distributed computing and storage.
• Hadoop Ecosystem Overview: Explore the Hadoop ecosystem components, including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce for batch processing.
• Hadoop Installation and Configuration: Learn how to set up a Hadoop cluster on both single-node and multi-node environments, configuring core services and ensuring high availability.
• Data Ingestion and Storage: Implement data ingestion techniques using tools like Apache Sqoop and Apache Flume to bring data into Hadoop from various sources.
• Data Processing with MapReduce: Master the MapReduce paradigm for distributed processing of large datasets, writing and optimizing MapReduce jobs in Java or other languages like Python.
• Querying Data with Hive and Impala: Use Apache Hive and Impala for querying and analyzing data stored in Hadoop using SQL-like syntax, optimizing queries for performance.
• Data Warehousing with Hadoop: Design and implement data warehouses on Hadoop using tools like Apache HBase and Apache Phoenix for NoSQL and real-time data access.
• Data Processing with Spark: Integrate Apache Spark with Hadoop for in-memory processing, leveraging Spark SQL, DataFrame API, and Spark Streaming for real-time analytics.
• Data Visualization: Visualize and interpret data insights using tools like Apache Zeppelin or integrating with BI tools like Tableau for effective data-driven decision-making.
• Security and Governance: Implement security measures with Kerberos authentication, Hadoop ACLs, and governance policies to ensure data integrity and compliance.
• Real-time Data Processing: Explore frameworks like Apache Kafka for real-time data streaming and integration with Hadoop for continuous data processing.

·  Scalability and Performance Optimization: Apply techniques to optimize Hadoop cluster performance, including tuning JVM parameters, data partitioning, and resource management.