Master HDFS, MapReduce, and YARN. Learn Hive, Pig, and ETL workflows to build high-performance Big Data clusters.
π₯ 420 students
π January 2026 update
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- Course Overview
- Embark on a comprehensive journey into the core of Big Data Engineering, moving beyond simple data storage to master the art of distributed systems architecture.
- Explore the historical evolution of the Hadoop Ecosystem and understand why it remains the bedrock of modern Data Lake implementations in 2026.
- Analyze the Master-Slave topology that governs high-performance clusters, focusing on how horizontal scaling outperforms traditional vertical scaling models.
- Deep dive into the Theory of Distributed Computing, addressing the CAP Theorem and how Hadoop balances consistency, availability, and partition tolerance.
- Investigate the 2026 Industry Standards for Data Governance and security, ensuring your Big Data pipelines are compliant with modern enterprise regulations.
- Learn the “Data-Locality” principle, discovering how to minimize network congestion by processing information exactly where it resides on the DataNodes.
- Gain unique insights into Cluster Resource Planning, including how to calculate hardware requirements and overhead for multi-petabyte environments.
- Understand the role of the Hadoop Architect in selecting the right mix of tools for specific ETL, analytical, and storage use cases.
- Requirements / Prerequisites
- Proficiency in Basic Linux Shell Scripting and command-line navigation is required to manage the Hadoop Distributed File System effectively.
- A foundational understanding of Java Programming (specifically Core Java) is necessary to customize MapReduce jobs and develop User Defined Functions (UDFs).
- Hardware prerequisites include a 64-bit computer with a minimum of 8GB RAM (16GB recommended) to run Cloudera Quickstart or localized Docker containers.
- Familiarity with Relational Database Management Systems (RDBMS) and SQL syntax will significantly flatten the learning curve for HiveQL.
- An introductory awareness of Distributed Networking concepts, such as IP addressing, SSH protocols, and firewall management, for cluster communication.
- Skills Covered / Tools Used
- HDFS Management: Master the intricacies of Block Storage, Replication Factors, and the NameNode Federation to ensure Fault Tolerance.
- YARN Resource Negotiation: Configure the Capacity Scheduler and Fair Scheduler to optimize Multi-tenant Cluster performance and job prioritization.
- Advanced MapReduce: Build sophisticated algorithms using Custom Partitioners, Writable Comparables, and Distributed Cache for complex data joins.
- Apache Hive 3.x: Architect Data Warehousing solutions using External Tables, Managed Tables, and Partitioning/Bucketing strategies for query optimization.
- Apache Pig Latin: Simplify Unstructured Data processing through multi-stage Data Flow Scripts that abstract away the complexity of raw MapReduce.
- Sqoop Ingestion: Perform seamless Data Transfer between traditional SQL Databases and Hadoop, including Incremental Imports and Export operations.
- Flume Log Collection: Configure Sources, Channels, and Sinks to ingest Real-time Streaming Data from web servers into the cluster.
- Apache Oozie: Design and automate Directed Acyclic Graphs (DAGs) to orchestrate complex Workflow Scheduling and operational monitoring.
- Zookeeper Coordination: Implement High Availability (HA) clusters to eliminate Single Points of Failure using distributed synchronization.
- Data Serialization: Explore advanced file formats like Avro, Parquet, and ORC to enhance compression ratios and query speed.
- Benefits / Outcomes
- Achieve the status of a Hadoop Certified Professional, opening doors to high-paying roles in Data Engineering and System Architecture.
- Develop the ability to build Production-Grade Data Pipelines that can ingest, transform, and store massive datasets with 99.9% reliability.
- Bridge the gap between Data Science and IT Operations by providing a stable, scalable platform for Machine Learning and Predictive Analytics.
- Optimize Operational Expenses (OPEX) by migrating legacy data workloads from expensive proprietary hardware to Open Source Hadoop clusters.
- Master the art of Performance Tuning, enabling you to identify bottlenecks in Shuffling and Sorting phases to slash job execution times.
- Create a robust Professional Portfolio featuring end-to-end ETL workflows that solve real-world business challenges at scale.
- Secure a competitive edge in the Big Data market with an Architecture-first mindset, allowing you to adapt to any distributed framework.
- PROS
- Features a Hands-on Laboratory Approach with real-world datasets that mirror actual enterprise Big Data challenges.
- Includes exclusive January 2026 Updates, ensuring compatibility with the latest Apache Hadoop 3.x versions and security patches.
- Provides In-depth Q&A Support from industry veterans to help you troubleshoot complex cluster configuration errors.
- Balances Theoretical Foundations with Practical Implementation, making it suitable for both developers and system administrators.
- CONS
- The Technical Intensity of the course requires a substantial time investment, as mastering distributed architecture involves a steep and rigorous learning curve.
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