Big Data with Hadoop
Course Overview
The Big Data with Hadoop course by CourseDeal is designed to help learners understand and work with large-scale data processing frameworks. You’ll explore the Hadoop ecosystem, which allows organizations to store, process, and analyze massive volumes of structured and unstructured data efficiently. The course covers HDFS (Hadoop Distributed File System), MapReduce, YARN, and essential Hadoop tools such as Hive, Pig, and Spark for data processing and analytics. Through hands-on labs and projects, learners will gain practical experience in designing distributed data workflows, performing batch and real-time processing, and managing big data pipelines. By the end of the course, participants will be prepared for roles in big data engineering, analytics, and data management.
Key Highlights
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Master Hadoop ecosystem components like HDFS, MapReduce, YARN, Hive, and Pig
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Learn big data storage, processing, and analytics techniques
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Hands-on projects with real-world large datasets
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Practical experience with batch and real-time data processing
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Prepare for careers in Big Data, Data Engineering, and Analytics
Tools & Technologies Covered
- Hadoop
- HDFS
- MapReduce
- YARN
- Hive
- Pig
- Apache Spark
- Sqoop
- Flume
- Java
- Linux
Curriculum
- 6 Sections
- 0 Lessons
- 22 Hours
- Module 1: Introduction to Big Data and HadoopThis module introduces the concept of big data, its characteristics, and challenges in managing and analyzing massive datasets. You’ll learn why traditional databases are insufficient for big data and how Hadoop provides a scalable and fault-tolerant solution. Topics include the Hadoop ecosystem, architecture, and core components such as HDFS and YARN. Learners will understand distributed computing principles and gain hands-on experience with Hadoop setup and cluster management to process large-scale data efficiently.0
- Module 2: Hadoop Distributed File System (HDFS)In this module, you’ll explore HDFS in detail, learning how Hadoop stores and manages data across multiple nodes. Topics include block storage, replication, data nodes, name nodes, and fault tolerance mechanisms. Hands-on exercises cover file operations, reading and writing data, and understanding the storage architecture. By the end of this module, learners will be able to manage large datasets effectively and ensure high availability and reliability in distributed data storage.0
- Module 3: MapReduce ProgrammingThis module focuses on the MapReduce programming model, which enables distributed processing of large datasets. You’ll learn to write Map and Reduce functions to perform data transformations, aggregations, and analytics tasks. The module also covers job scheduling, monitoring, and optimization techniques to improve performance. Through practical exercises, learners gain experience in designing scalable data workflows that can process big data efficiently across multiple nodes in a Hadoop cluster.0
- Module 4: Hive and Pig for Data ProcessingHere, learners will work with Hive and Pig, two high-level tools for processing and querying big data. Hive allows SQL-like queries on large datasets stored in Hadoop, while Pig provides a scripting language for data transformation. You’ll learn to write Hive queries, create tables, and analyze data, as well as develop Pig scripts for complex data processing tasks. Hands-on labs reinforce understanding of data manipulation and analytical operations in the Hadoop ecosystem.0
- Module 5: Introduction to Apache SparkThis module introduces Apache Spark, a fast and flexible framework for big data processing. You’ll learn about Spark’s architecture, RDDs (Resilient Distributed Datasets), DataFrames, and Spark SQL for querying large datasets. The module covers batch and real-time data processing, performance optimization, and integration with Hadoop. Learners gain hands-on experience in building scalable data processing pipelines using Spark, enabling efficient analysis of large-scale data.0
- Module 6: Big Data Project and CapstoneIn the final module, learners will apply all concepts through a comprehensive project involving real-world big data. You’ll design a data pipeline, process datasets using Hadoop and Spark, and perform analytics using Hive and Pig. The module emphasizes end-to-end workflow, from data ingestion and storage to analysis and visualization. By completing this capstone project, learners will have practical experience and a portfolio demonstrating their ability to handle big data projects professionally.0
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