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Learn Sqoop 2024 – Best Sqoop Courses & Best Sqoop Tutorials

Best Sqoop Courses 2021


Best Sqoop Tutorials 2021

Mastering Apache SQOOP with Hadoop,Hive, MySQL (Mac & Win)

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 huge amounts of data. This is the industry standard these days. In real-life scenarios, using sqoop, you can move data from relational tables to hadoop, and then take advantage of hadoop’s parallel processing capabilities to process huge amounts of data and generate meaningful information about the data. Hadoop processing results can again be stored in relational tables using the sqoop export feature.

Big data analytics starts with data ingestion and that’s where apache sqoop comes in. This is the first step in preparing the data.

About this course

In this course, you will learn step by step everything you need to know about apache sqoop and how to integrate it into the hadoop ecosystem. With each concept explained with real world examples, you will learn how to create data pipelines to move data from hadoop in / out. In this course, you will learn in detail the following main concepts:

Apache sqoop – subject import << mysql to hadoop / hive >>

Default hadoop storage

Specific target on hadoop storage

Parallelism check

Overwrite existing data

Add data

Load specific columns from mysql table

Control the data splitting logic

Defaults to a single mapper when needed

Sqoop options files

Debugging sqoop operations

Import data in various file formats – text, sequence, avro, parquet & orc

Data compression during import

Custom query execution

Handling null strings and non-string values

Setting delimiters for imported data files

Definition of escape characters

Incremental data loading

Write directly in the hive table

Use of hcatalog parameters

Import of all tables from the mysql database

Import the whole mysql database into the hive database

Apache sqoop – export topics << hadoop / hive to mysql >>

Move data from hadoop to the mysql table

Move specific columns from hadoop to a mysql table

Avoid partial export problems

Update operation when exporting

Apache sqoop – job topics << automation >>

Create a sqoop job

List existing sqoop jobs

Check metadata on sqoop tasks

Run sqoop job

Delete sqoop job

Enable password storage for easy execution in production

What you will expect after completing this course

After completing this course, you will tackle one of the most requested topics in the certifications below. You will also need to take other lessons to fully prepare for the test. We will be launching more courses soon.

1. Cca spark and hadoop developer exam (cca175)

2. Hortonworks data platform (hdp) certified developer exam (hdpcd)

You will also get step-by-step instructions to install all the required tools and components on your machine in order to run all the examples provided in this course. Each video will explain the whole process in detail and in an easy to understand way.

You will have access to the working code so that you can play with and develop it. All code examples work and will be shown in video lessons.

Windows users will need to install a virtual machine on their device to set up a single node hadoop cluster, while macbook or linux users can directly install the hadoop and sqoop components on their machines. The step-by-step process is illustrated in the course.

Big Data Analytics Using Sqoop and Hive

Data is the new oil in this digital age.

Are you planning to start your career in Big Data Analytics? Then you have landed in the right place.

In this course, you will learn how to organize, analyze, and interpret large sources of information.

We cover all the essential fundamental knowledge of Big Data Analytics and provide end-to-end real-world project practices.

The course contains

Understanding of Big Data and MapReduce
How to transfer data from different sources using Sqoop
Manipulate Data in HDFS Using HIVE
Real Hands Experience Project

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