DataBase Technologies

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Hadoop

A Hadoop course typically focuses on teaching the fundamentals of the Hadoop ecosystem, which is a framework used for distributed storage and processing of large data sets across clusters of computers. The course often covers topics such as Hadoop's architecture, key components (like HDFS, MapReduce, YARN), and various tools that complement Hadoop, including Pig, Hive, HBase, and others. Here’s an outline of what you might expect from a typical Hadoop course: 1. Introduction to Big Data Understanding Big Data Challenges of storing and processing large datasets Importance of distributed systems 2. Hadoop Overview What is Hadoop? Core components: HDFS, MapReduce, and YARN Hadoop ecosystem and its components (Hive, Pig, HBase, etc.) 3. Hadoop Distributed File System (HDFS) HDFS Architecture Block replication, data nodes, and namenodes File operations: creating, reading, and writing files Data storage and retrieval concepts 4. MapReduce Programming Model What is MapReduce? How MapReduce works: Map phase, Shuffle phase, Reduce phase Writing and running a MapReduce job Debugging and optimizing MapReduce programs 5. YARN (Yet Another Resource Negotiator) Overview of YARN architecture Resource management with YARN The role of ResourceManager and NodeManager Running applications on YARN 6. Hadoop Ecosystem Tools Hive: SQL-based querying on Hadoop Pig: Scripting language for data analysis HBase: NoSQL database for Hadoop Sqoop: Data transfer between Hadoop and relational databases Flume: Data ingestion tool Zookeeper: Coordination service for distributed systems 7. Hadoop Deployment and Management Setting up Hadoop clusters Configuring Hadoop Cluster management tools (Ambari, Cloudera Manager) Hadoop ecosystem and cloud integration 8. Data Processing with Hadoop Batch vs. Real-time processing Using Hive and Pig for data processing Running MapReduce jobs for analysis 9. Hadoop Security Authentication and authorization in Hadoop Kerberos setup in Hadoop clusters Securing HDFS and YARN 10. Advanced Topics (Optional) Performance tuning and optimization of Hadoop Hadoop with Spark for faster processing Advanced use-cases (e.g., machine learning, real-time analytics).