Big Data Hadoop and Spark Developer - eLearning

Big Data Hadoop and Spark Developer - eLearning

The Big Data Hadoop and Spark Developer Course is designed to provide you with an in-depth understanding of Apache Spark fundamentals and the Hadoop framework, equipping you with the skills needed to excel as a Big Data Developer. Through this program, you will gain hands-on knowledge of the Hadoop ecosystem and its integration with Spark, enabling you to process and analyze massive datasets efficiently. Learn how the multiple components of Hadoop, such as HDFS and MapReduce, fit seamlessly into the big data processing cycle, preparing you for success in today's data-driven world.

WHAT IS INCLUDED?

• Course and material are in English
• Intermediate for aspiring data engineer
• 1 year access to the self-paced study eLearning platform 24/7
• 11 hours of video content
• 50 hours study time recommended
• Simulation test, Virtual lab and Course-end Project
• No exam for the course but student will get certification of training completion

COURSE OBJECTIVES

• Learn how to navigate the Hadoop ecosystem and understand how to optimize its use
• Ingest data using Sqoop, Flume, and Kafka.
• Implement partitioning, bucketing, and indexing in Hive
• Work with RDD in Apache Spark
• Process real-time streaming data and Perform DataFrame operations in Spark using SQL queries
• Implement User-Defined Functions (UDF) and User-Defined Attribute Functions (UDAF) in Spark

Target Audience

Ideal for a wide range of professionals and individuals who want to advance their careers in big data analytics, data engineering, and data science.

• Analytics professionals
• Senior IT professionals
• Testing and mainframe professionals
• Data management professionals
• Business intelligence professionals
• Project managers
• Graduates looking to begin a career in big data analytics

Prerequisites: It is recommended that you have knowledge of Core Java and SQL

Course content

1. Introduction to Big Data and Hadoop
2. Hadoop Distributed File System (HDFS) and YARN
3. Data entry in Big Data and ETL systems
4. Distributed processing MapReduce and Pig
5. Lesson 05 - Apache Hive
6. NoSQL databases HBase
7. The basics of functional programming and Scala
8. Apache Spark, the new generation of Big Data
9. Spark Core Processing RDD
10. Spark SQL Processing of Data Frames
11. Spark MLib Modelling BigData with Spark
12. Stream Processing Frameworks and Spark Streaming
13. Spark GraphX