Certified Associate Big Data Engineer
Prerequisite Requirements: Basic knowledge of programming, databases, and statistics.
Course Description:
The course is designed to help learners develop practical skills required for an entry-level Certified Associate Big Data Engineer in a modern big data ecosystem. The course focuses on industry-wide standards and best practices. All objectives are measurable, and learners are equipped with the knowledge and skills needed for a successful career in big data engineering.
Course Objectives:
- Understand the fundamentals of big data processing and data engineering principles
- Learn how to install, configure, and deploy a Hadoop cluster
- Understand the concepts and architecture of Hadoop Distributed File System (HDFS) and MapReduce
- Master the core concepts of Hadoop components and ecosystems, including YARN, ZooKeeper, and Spark
- Develop a good understanding of Hive and HiveQL, including data warehousing and data processing concepts
- Gain hands-on experience with Pig and PigLatin, including data transformation and data cleaning
- Learn how to use Spark and Resilient Distributed Datasets (RDDs) for big data processing
- Understand data ingestion techniques, including streaming data sources and log files
- Master data storage and retrieval techniques, including HBase, Cassandra, and NoSQL databases
- Comprehend data analysis and visualization techniques, including statistical analysis and visualization tools
Course Structure:
Unit 1: Introduction to Big Data and Hadoop Ecosystem
Unit 2: HDFS and MapReduce Concepts
Unit 3: Hadoop Components and Ecosystems
Unit 4: Apache Hive and HiveQL
Unit 5: Apache Pig and PigLatin
Unit 6: Apache Spark and RDD
Unit 7: Data Ingestion Techniques
Unit 8: Data Storage and Retrieval Techniques
Unit 9: Data Processing Techniques
Unit 10: Data Analysis and Visualization Techniques
Unit 11: Project Implementation
Unit 12: Capstone Project
