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Course Outline
Section 1: Introduction to Hadoop
- History and core concepts of Hadoop
- Ecosystem overview
- Available distributions
- High-level architecture
- Common myths about Hadoop
- Key challenges in Hadoop implementation
- Hardware and software considerations
- Lab: Initial exploration of Hadoop
Section 2: HDFS
- Design principles and architecture
- Core concepts (horizontal scaling, replication, data locality, rack awareness)
- Daemons: Namenode, Secondary Namenode, DataNode
- Communication mechanisms and heartbeats
- Data integrity management
- Read and write paths
- Namenode High Availability (HA) and Federation
- Labs: Interacting with HDFS
Section 3 : Map Reduce
- Concepts and architecture
- Daemons (MRV1): JobTracker and TaskTracker
- Processing phases: driver, mapper, shuffle/sort, reducer
- MapReduce Version 1 and Version 2 (YARN)
- Deep dive into MapReduce internals
- Introduction to Java-based MapReduce programming
- Labs: Executing a sample MapReduce program
Section 4 : Pig
- Pig compared to Java MapReduce
- Pig job workflow
- Pig Latin language fundamentals
- Data processing with Pig
- Transformations and joins
- User Defined Functions (UDF)
- Labs: Writing Pig scripts for data analysis
Section 5: Hive
- Architecture and design
- Data types
- SQL capabilities in Hive
- Creating Hive tables and executing queries
- Data partitioning
- Joins
- Text processing techniques
- Labs: Various hands-on exercises on data processing with Hive
Section 6: HBase
- Concepts and architecture
- Comparison of HBase, RDBMS, and Cassandra
- HBase Java API
- Handling time series data in HBase
- Schema design strategies
- Labs: Interacting with HBase via the shell; programming with the HBase Java API; Schema design exercises
Requirements
- Proficiency in the Java programming language is required, as most practical exercises will be conducted in Java.
- Familiarity with the Linux environment is essential, including the ability to navigate the command line and edit files using tools like vi or nano.
Lab environment
Zero Install : Participants do not need to install Hadoop software on their own devices. A fully functional Hadoop cluster will be provided for use during the course.
Students will need the following tools
- An SSH client (Linux and Mac systems come with built-in SSH clients; for Windows, PuTTY is recommended)
- A web browser to access the cluster, with Firefox being the recommended option.
28 Hours
Testimonials (1)
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
