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Edureka's Big Data Hadoop training course is curated by Hadoop industry experts, and covers in-depth knowledge of Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this instructor-led online Hadoop training, you'll work on real-life industry use cases in retail, social media, aviation, tourism, and finance using Edureka's cloud lab.
ABOUT HADOOP TRAINING
Hadoop is an Apache project (ie open source software) for storing and processing Big Data. Hadoop stores Big Data in a distributed and fault-tolerant manner on commodity hardware. Hadoop tools are then used to perform data processing in parallel via HDFS (Hadoop Distributed File System). As organizations have realized the benefits of Big Data Analytics, there is a high demand for Big Data and Hadoop professionals. Companies are looking for Big data and Hadoop experts with knowledge of the Hadoop ecosystem and best practices on HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume. Edureka Hadoop Training is designed to make you a certified Big Data professional by providing you with rich hands-on training in the Hadoop Ecosystem.
WHAT ARE THE OBJECTIVES OF OUR BIG DATA HADOOP ONLINE COURSE?
Big Data Hadoop Certification Training is designed by industry experts to make you a certified Big Data professional. The Big Data Hadoop course offers: In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce Comprehensive knowledge of various tools found in the Hadoop Ecosystem such as Pig, Hive, Sqoop, Flume, Oozie, and HBase The ability to ingest data into HDFS using Sqoop & Flume, and analyze those large data sets stored in HDFS Exposure to many real world industry based projects to be run on Edureka's CloudLab Projects that are diverse in nature spanning various datasets from multiple domains such as banking, telecommunications, social media, insurance,
UNDERSTANDING BIG DATA AND HADOOP. Learning Objectives: In this module, you will understand what Big Data is, the limitations of traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write and how MapReduce works. Topics: Introduction to Big Data Challenges and Big Data Preview Limitations and Workarounds of Big Data Hadoop Architecture and Characteristics Hadoop Ecosystem Hadoop 2.x Main Components Preview Hadoop Storage: Hadoop Distributed File System (HDFS) Hadoop Processing: MapReduce Framework Different Hadoop Distributions Get detailed syllabus delivered to your inbox Download Curriculum
HADOOP AND HDFS ARCHITECTURE. Learning Objectives: In this module, you will learn Hadoop Cluster Architecture, important Hadoop Cluster configuration files, Data Loading Techniques using Sqoop & Flume, and how to configure Single Node and Multi-Node Hadoop Cluster. Topics: Hadoop 2.x Cluster Architecture Preview Federation and High Availability Architecture Preview Typical Production Hadoop Cluster Hadoop Cluster Modes Hadoop Common Shell Commands Hadoop 2.x Preview Configuration Files Single Node Cluster and Multi-Node Cluster Configuration Basic Hadoop Administration. Click on the "go to course" button to learn more details at edureka!
HADOOP MAPREDUCE FRAMEWORK. Learning Objectives: In this module, you will comprehensively understand the Hadoop MapReduce framework, how MapReduce works on data stored in HDFS. You will also learn advanced MapReduce concepts like Input Splits, Combiner & Partitioner. Topics: Traditional Way vs MapReduce Way Why MapReduce Preview YARN Components YARN Architecture YARN MapReduce Architecture YARN Application Execution Flow Workflow MapReduce Program Preview Anatomy Input Splits, relationship between input splits and blocks HDFS MapReduce: Merge and Partition Healthcare Dataset Demo Weather Dataset Demo . Click on the "go to course" button to learn more details at edureka!
ADVANCED HADOOP MAP. Learning Objectives: In this module, you will learn advanced MapReduce concepts such as counters, distributed cache, MRunit, union reduce, custom input format, stream input format, and XML parsing. Topics: Counters Distributed Cache MRunit Reduce Join Preview Custom Input Format Preview Sequence Input Format XML File Parsing Using MapReduce . Click on the "go to course" button to learn more details at edureka!
Instructor-led sessions will address all your concerns in real time.
Unlimited access to the course's online learning repository.
Develop a project with live accompaniment, based on any of the cases seen
In each class you will have practical tasks that will help you apply the concepts taught.
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