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This presentation about Sqoop will help you learn what is Sqoop, why is Sqoop important, the different features of Sqoop, the architecture of Sqoop, how Sqoop import and export works, how Sqoop processes data and finally you’ll see how to work with Sqoop commands. Sqoop is a tool used to transfer bulk data between Hadoop and external data stores such as relational databases. This tutorial will help you understand how Sqoop can load data from MySql database into HDFS and process that data using Sqoop commands. Finally, you will learn how to export the table imported in HDFS back to RDBMS. Now, let us get started and understand Sqoop in detail.
Below topics are explained in this Sqoop Hadoop presentation:
1. Need for Sqoop
2. What is Sqoop?
3. Sqoop features
4. Sqoop Architecture
5. Sqoop import
6. Sqoop export
7. Sqoop processing
8. Demo on Sqoop
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.