A technology that has developed as the number one for handling Big Data processing is Hadoop. This efficient platform aids in storing, handling and retrieving enormous amounts of data in a variety of applications while also helping in deep analytics. As more and more companies are embracing Hadoop, the demand for Hadoop Developers is growing. Evanta Technologies online training for Apache Hadoop will help you understand its critical aspects and the tools and techniques to harness its power.
The Big Data Hadoop online course at Evanta Technologies is aimed to give you in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and Map Reduce. You will learn to use Pig, Hive, and Impala to process and analyse large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion with our big data training.
You will control present data processing using Spark, as well as functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimization techniques. With our big data course, you will also learn the various interactive algorithms in Spark and use Spark SQL for creating, transforming, and querying data forms.
As a portion of the big data course, you will be essential to execute real-life industry-based projects using Cloud Lab in the domains of banking, telecommunication, social media, insurance, and e-commerce. This Big Data Hadoop online training course will prepare you for the Cloud era CCA175 big data certification.
What you will learn
- Know what Big Data is and how Hadoop is used to control its power
- Learn about Map Reduce, Hadoop Distributed File System (HDFS), YARN, and how to write Map Reduce code
- Learn best carry out and attentions for Hadoop development, debugging techniques and implementation of workflows and common algorithms
- Learn how to use Hadoop frameworks like ApachePig™, ApacheHive™, Sqoop, Flume, Oozie and other projects from the Apache Hadoop Ecosystem
- Accomplish actual analytics by getting trained on advanced Hadoop API topics
- Learn about the hardware considerations that go into maintaining the Hadoop cluster
- Comprehensive e-courseware will be provided.
Introduction to Big data and Hadoop
- Understanding Big Data
- Challenges in processing Big Data
- 3V Characteristics (Volume, Variety and Velocity)
- Brief history of Hadoop
- How Hadoop addresses Big Data?
- HDFS and MR
- Hadoop echo system
HDFS (Hadoop Distributed File System)
- HDFS Overview and Architecture
- HDFS Keywords like Name Node, Data Node, Heart Beat etc
- Configuring HDFS
- Data Flows (Read and Write)
- HDFS Permissions and Security
- HDFS commands
- Rack Awareness
- 5 Daemons processes
- Map Reduce Basics
- Map Reduce Data Flow
- Word count Example solving
- Algorithms for simple and complex problems
- Hadoop Streaming
Developing a Map Reduce Application
- Setting up working environment
- Custom Data types (Writable and Custom Key types)
- Input and Output file formats
- Driver, Mapper and Reducer Code Wal thru
- Configuring IDE Eclipse
- Writing Unit test and running locally
- Map Reduce Web UI
- Hands -on
How Map Reduce works?
- Classic Map Reduce (Map Reduce I)
- YARN (Map Reduce II)
- Job Scheduling
- Shuffle and Sort
- Oozie Workflows
- Hands-on Excercises
How Map Reduce works?
- Map Reduce Types
- Input formats – Input splits & records, text input, binary input, multiple inputs and database input.
- Output formats - text output, binary output, multiple outputs, Lazy output and database output.
Hadoop Echo Systems
- Overview of PIG
- Installation and running PIG
- PIG Latin
- Loading and storing data
- Overview of HIVE
- Installation and running HIVE
- Overview of HBASE
- CLinets (avro, REST, Thrift)
Solving Case studies