Hands-On Data Engineering | DBDA.X424
Formerly: Data Engineering with Hadoop
Big Data platforms are distributed systems that can process large amounts of data across clusters of servers. They are being used across industries in internet startups and established enterprises. In this comprehensive course, you will get up to speed on the use of current Big Data platforms and gain insights into cloud-based Big Data architectures. We will cover Hadoop, Spark, Kafka and other Big Data platforms based on SQL, such as Hive.
The first half of the course includes an overview of the frameworks for MapReduce, Spark, Kafka, and Hive as well as some aspects of Python programming. You will learn how to write MapReduce/Spark jobs and how to optimize data processing applications and become familiar with SQL based tools for Big Data. We use Hive to build ETL jobs. The course also includes the fundamentals of NoSQL databases like HBase and Kafka.
The second half of the course covers stream processing capability and developing streaming applications with Apache Spark. You’ll learn how to process large amounts of data using DataFrame, Apache Spark’s structured data processing programming model that provides simple, powerful APIs. In addition to batch and iterative data processing, Apache Spark also supports stream processing, which enables companies to extract interesting and useful business insights at near real-time.
The course consists of interactive lectures, hands-on labs in class, and take home practice exercises. Upon completion of this course, you will possess a strong understanding of the tools used to build Big Data applications using MapReduce, Spark, and Hive.
At the conclusion of the course, you should be able to
- Describe the role Hadoop plays in the analysis of big data
- Discuss the inner workings of Hadoop's computing framework, including MapReduce processing and Hadoop's file system (HDFS)
- Develop programs/small applications in Spark and Hive
- Use Hive and NOSQL databases for data analysis
- Leverage the Hadoop ecosystem to become productive in analyzing data
- Big Data applications architecture
- Understanding Hadoop distributed file system (HDFS)
- How MapReduce framework works
- Introduction to HBase (Hadoop NoSQL database)
- Introduction to Apache Kafka
- Introduction to Spark and SparkSQL
- Developing Spark/SparkSQL and Hive applications
- Managing tables and query development in Hive
- Introduction to data pipelines
Basic SQL skills and the ability to create simple programs in a modern programming language, like Python are required. An understanding of database, parallel or distributed computing is helpful.
This course uses AWS EMR and Databricks for Spark, Hive and HDFS programming. Students are required to have accounts with AWS and Databricks.
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