Course Syllabus

  Module 1: Big Data Fundamentals & Hadoop Overview (4 hours). Introduction to Big Data concepts (the 7 Vs of data), trends and challenges. Overview of the Hadoop ecosystem and its components (HDFS, YARN, MapReduce, Spark, Hive, Pig, HBase, Sqoop, Flume). Course logistics (lab setup, cloud sandbox). Labs: Start Hadoop single-node cluster; explore HDFS commands and web UIs.

  Module 2: Hadoop Installation and HDFS (4 hours). Deep dive into Hadoop architecture and cluster components (NameNode, DataNodes, YARN ResourceManager, NodeManagers). Configure a Hadoop cluster (virtual machines), understand HDFS structure (blocks, replication, rack awareness). Use HDFS shell commands to create directories, put/get files, and manage permissions. Lab: Ingest sample large files into HDFS; run basic file operations and monitoring via Ambari.

  Module 3: MapReduce Programming (6 hours). Core MapReduce framework on YARN. Explain the Map, Shuffle, and Reduce phases and how data flows in a job Develop and run Java (or Python) MapReduce programs. Hands-on examples: WordCount and a simple sales/analytics job. Lab: Implement and debug MapReduce code on the cluster (monitor jobs, troubleshoot).

  Module 4: Data Warehousing with Hive and Scripting with Pig (6 hours). Introduction to Apache Hive: data warehousing on Hadoop, HiveQL query language, table types (managed vs external), partitions and buckets, Hive UDFs. Apache Pig: Pig Latin language for data transformation, Pig execution modes (local/MapReduce), Pig Latin commands (load, filter, group, join). Compare Pig vs Hive vs MapReduce. Lab: Create Hive tables, load large datasets, and run HiveQL queries; write Pig scripts to process and analyze the data.

  Module 5: Apache Spark Basics (6 hours). Overview of Apache Spark and its advantages over MapReduce for iterative analytics. Spark architecture (driver, executors) and RDD fundamentals. SparkCore programming (transformations, actions), Spark SQL (DataFrames, SparkSession) and DataFrame API (schema, joins, aggregations). (Optional: intro to Spark streaming concepts.) Lab: Use Spark shell and Spark application code to perform transformations on sample datasets.

  Module 6: NoSQL with HBase (3 hours). Introduction to Apache HBase (Hadoop’s columnar NoSQL database). HBase data model (tables, column families, rows, cells), architecture (region servers, HMaster), and use cases. Interacting with HBase via the shell: creating tables, inserting and retrieving rows, basic scan filters. Lab: Load a large dataset into HBase, perform read/write and filter operations.

  Module 7: Data Ingestion (Sqoop & Flume) (3 hours). Apache Sqoop: Importing/exporting data between Hadoop and relational databases. Sqoop commands for import to HDFS/Hive/HBase and export to RDBMS. Apache Flume: Collecting and streaming log data into Hadoop. Flume architecture (agents, sources, sinks, channels) and configuration files. Lab: Use Sqoop to import sample MySQL tables into HDFS/Hive; use Flume to stream log data into HDFS.

  Module 8: Real-World Projects and Capstone Lab (8 hours). In-depth, scenario-driven projects that combine the above tools to solve practical problems. Example projects: analyzing a large retail dataset (ETL with Sqoop/Flume, processing with Spark/Hive, visualizing results), or social media sentiment analysis pipeline. These capstone exercises reinforce end-to-end development of Hadoop solutions. Lab: Work on assigned big data project in teams (data ingestion, processing, and querying), with guidance and review by the instructor.

Key Features

• Certification-Aligned Curriculum: Covers core topics required for industry certifications. For example, Hortonworks’ HCA (Hortonworks Certified Associate) focuses on Hadoop basics including HDFS, YARN, Pig, and Hive, and Cloudera’s Data Engineer exam tests skills in HDFS, MapReduce, Hive, and Pig We also touch on AWS’s Big Data Specialty (which covers services like EMR, Redshift, and data visualization) The course content is regularly updated to match Cloudera, Hortonworks, and AWS Big Data certification objectives.
• Hands-On, Project-Based Learning: Every topic is reinforced with lab exercises and real datasets. Learners will engage in end-to-end projects using Hadoop tools – for example, loading data into HDFS, running MapReduce/Pig scripts, and querying via Hive. The training includes real-world case studies (telecom, finance, social media, etc.) and capstone projects. This practical approach helps students apply concepts immediately; as one course notes, exposure to “industry use-cases and scenarios will help learners ... perform real-time projects with best practices”.
• Flexible Delivery Modes: The 40-hour curriculum can be delivered as live instructor-led training (either in-classroom or virtually via Zoom) or as self-paced online learning. For example, MindMajix offers 40 hrs of remote instructor-led classes in Zoom/Meet, and the course includes self-paced videos and materials for on-demand learning. Corporate training options allow on-site instruction or blended modes. All formats provide support and guidance on certification preparation and real-time projects.
• Industry-Expert Instructors: Instructors are seasoned Hadoop professionals with years of field experience. They share real-world insights and best practices, bridging the gap between theory and practice. The learning experience includes sample interview questions and resume preparation to aid job readiness.
• Comprehensive Resources and Support: Participants receive complete training materials, including slide decks, code samples, and lab guides. Many programs provide a lifetime access to LMS content (videos and forums) and a community for Q&A. Certification guidance is also provided through practice tests and exam outlines
• Industry-Relevant Content: The curriculum features up-to-date Hadoop 3.x and Spark 3.x examples. Case studies reflect current big data scenarios (e.g. IoT data pipelines, real-time tweet processing). We demonstrate not only Hadoop batch analytics but also streaming concepts (Spark Streaming, Kafka) where appropriate, making the training relevant to today’s data landscape