In this apache sqoop tutorial, you will learn everything that you need to know about Apache Sqoop and how to integrate it within Big data hadoop systems. With every concept explained with real world like examples, you will learn how to create Data Pipelines to move in/out the data from Hadoop.
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This comprehensive Apache Sqoop tutorial focuses on building real world data pipelines to move data from RDBMS systems (such as Oracle, MySQL etc) to Hadoop systems and vice versa. This knowledge is very critical for any big data engineer today. It will also help you greatly with answering sqooop interview questions.

Why Apache SQOOP
Apache SQOOP is designed to import data from relational databases such as Oracle, MySQL, etc to Hadoop systems. Hadoop is ideal for batch processing of huge amounts of data. It is industry standard nowadays. In real world scenarios, using SQOOP you can transfer the data from relational tables into Hadoop and then leverage the parallel processing capabilities of Hadoop to process huge amounts of data and generate meaningful data insights. The results of Hadoop processing can again be stored back to relational tables using SQOOP export functionality.

You will learn
Section 1 – APACHE SQOOP IMPORT (MySQL to Hadoop/Hive)
In this section of the course, we will start with understanding of apache sqoop architecture. After that, you will learn how to move data from a MySQL database into Hadoop/Hive systems. In other words, we will learn about apache sqoop import process.

There are lots of key areas that we will cover in this section of the course and it’s very critical for any data engineer to complete it. We will also cover step by step the process of apache sqoop installation for windows and Mac/Linux users. Here are few of the key areas that we will cover in the course:

1. warehouse hadoop storage
2. specific target on hadoop storage
3. controlling parallelism
4. overwriting existing data
5. append data
6. load specific columns from MySQL table
7. control data splitting logic
8. default to single mapper when needed
9. Sqoop Option files
10. debugging Sqoop Operations
11. Importing data in various file formats – TEXT, SEQUENCE, AVRO, PARQUET & ORC
12. data compression while importing
13. custom query execution
14. handling null strings and non string values
15. setting delimiters for imported data files
16. setting escaped characters
17. incremental loading of data
18. write directly to hive table
19. using HCATALOG parameters
20. importing all tables from MySQL database
21. importing entire MySQL database into Hive database

Section 2 – APACHE SQOOP EXPORT (Hadoop/Hive to MySQL)
In this section of the course, we will learn opposite of sqoop import process which is called apache sqoop export. In other words, you will learn how to move data from a hadoop or hive system to MySQL (RDBMS) database. This is an important lesson for data engineers and data analysts who often need to store aggregated results of their data processing into relational databases.

23. Move data from Hadoop to MySQL table
24. Move specific columns from Hadoop to MySQL table
25. Avoid partial export issues
26. Update Operation while exporting

Section 3 – APACHE SQOOP JOBS (Automation)
In this section, you will learn how to automate the process of sqoop import or sqoop export using sqoop jobs feature. This is how a real process will be ran in production. So, this lesson is critical for your success at job.

27. create sqoop job
28. list existing sqoop jobs
29. check metadata about sqoop jobs
30. execute sqoop job
31. delete sqoop job
32. enable password storage for easy execution in production

In this sqoop tutorial, you will learn various sqoop commands that are necessary for anyone to answer sqoop interview questions or to work as a ETL data engineer today.

You will also get step by step instructions for installing all required tools and components on your machine in order to run all examples provided in this course. Each video will explain entire process in detail and easy to understand manner.

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