Moving Data into Hadoop Course Overview
Hadoop is a framework that is very efficient for distributed storage and computing. Hadoop enables distributed processing of large data across clusters using a very simple programming model.
The Hadoop design allows scaling up from single servers to thousands of machines, and each of them can perform local computation and storage. Hadoop is Fault-tolerant, scalable, Efficient, reliable distributed storage system
Organizations can make decisions based on the analysis of data sets and variables that are made possible by Hadoop. The exposure to a large quantity of data using Hadoop, enables you to get a view of customers, opportunities, operations and risks.
Flume and sqoop are essential for moving data into Hadoop.
Flume is a highly distributed, reliable and configurable tool for efficiently collecting, aggregating and moving large amounts of data from various web servers to HDFS. Flume is a framework for populating Hadoop with data.
Sqoop efficiently transfers bulk data between Hadoop and structured data stores like relational databases. Sqoop allows users to point the target location inside Hadoop and move data from database to the target.
What will you learn from Moving Data into Hadoop course?
During this course, you will learn to:
- Understand the working of Hadoop
- Use Flume and Sqoop
- Move data into Hadoop
- Use Hadoop Architecture and the Hadoop Distributed File System
- Setup and learn the installation procedures
- Use HDFS Operations
- Modify configuration parameters
Why get enrolled in this Course?
Enroll in this course to
- Understand Hadoop
- Move data into Hadoop using Flume and Sqoop
- Understand the Core components like MapReduce
- Use the Hadoop Distributed File System
Course Offerings
- Live/Virtual Training in the presence of online instructors
- Quick look at Course Details, Contents, and Demo Videos
- Quality Training Manuals for easy understanding
- Anytime access to Reference materials
- Gain your Course Completion Certificate on the Topic
- Guaranteed high pay jobs after completing certification
Course Benefits
- Learn the basics of Hadoop
- Understand the process of moving data into Hadoop
- Explain the working methodology of Hadoop
- Learn to use the Hadoop Distributed File System (HDFS)
- Gain skills in using the Hadoop administration concepts
- Learn the components used like Flume and Sqoop
Audience
- Data Engineers
- Graduates willing to build a career in Big Data Analytics
Prerequisite to learn Moving Data into Hadoop
- Basic knowledge of Big Data
- Basic understanding of Linux Operating System
- Familiar with Scala, Python, or Java programming languages.
Moving Data into Hadoop Course Content
Lesson 1: Hadoop Load Scenarios
The primary challenge in handling the data is moving the data produced by different servers at different locations to the Hadoop environment. Hadoop File System Shell provides commands to insert data into Hadoop and read the data from it.
- Learn to load data at rest
- Learn to load data in motion
- Understand to load data from standard data sources like RDBMS
Lesson 2: Using Sqoop
Sqoop is designed to transfer data between Hadoop and relational database servers. Using Sqoop, you can import data from relational databases like RDBMS, MySQL to Hadoop HDFS and export from Hadoop to relational databases. The installation procedure of Sqoop is very straightforward.
Class 2.1:
- Installing Sqoop
- Importing data from a relational database table into HDFS
- Using Sqoop import and export command
Lesson 3: Flume Overview
Flume is a simple, standard, flexible, robust and extensible tool for data ingestion from various data producers or web servers into Hadoop
Class 3.1:
Flume and its uses
Working method of Flume
Lesson 4: Using Flume
This chapter shows the need for Flume for pushing data into HDFS and similar storage systems. Flume supports pushing data from a huge source of data into Hadoop ecosystem like HDFS and HBase where the stream of data is continuous and volume is large.
Class 4.1:
- Need for Flume
- Flume configuration components
- Start and configure a Flume agent
Lesson 5: Using Data Click
This lesson explains you about Data click. Data click allows you to integrate data with Hadoop. Data can be integrated from tables, multiple tables or entire schema into HDFS using Data click.
Class 5.1:
- Data Click for BigInsights
- Major components of Data Click
Moving Data into Hadoop FAQs
1. What is Flume?
Flume is a standard, simple, robust, flexible, and extensible tool for data ingestion from various data producers (webservers) into Hadoop.
2. Why should we enroll for this course?
Big Data and Hadoop are high demand in the market, and this increasing need opens many more job opportunities in different industries. Enroll in this course to grab those opportunities and gain an excellent piece of work opportunity.
3. What are the features of Hadoop?
Features of Hadoop:
- Scalable: This feature enables you to add new nodes without changing the existing data formats and loading process
- Cost effective: Hadoop helps in a sizeable decrease in the storage cost as it allows to store an enormous amount of data and operate on that data in parallel.
- Flexible: Hadoop combines and aggregates data in any format from multiple sources and performs in-depth analysis on that data.
- Fault tolerant: The system redirects work to another location even in case you lose a working node. It allows the process to continue without missing a beat.
4. What are the components in Hadoop?
Hadoop four core components are HDFS, MapReduce, YARN, and Common. However, for some of the commercially available framework solutions, there are three well-known components available namely Spark, Hive, and Pig. All the components in Hadoop help to create applications and process data.
5. How does Hadoop influence career growth?
Hadoop allows for ramping up your career and gives you the following advantages:
- Accelerated career growth.
- Increased pay package due to Hadoop skill.
6. Why do we need Hadoop?
Every organization holds a significant amount of unstructured data. The major challenge is to retrieve and analyze the big data and not storing data. Hadoop allows analyzing the data that is present on different machines at different locations in a very cost effective way. Hadoop uses the MapReduce that divides the query into small parts for parallel processing which is also called parallel computing.