CABDE – Certified Associate Big Data Engineer


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

SKU: 5060-1-1 Categories: , Tag:


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