Apache Hadoop is an accumulation of open-source software utilities that make it easier to solve problems involving massive amounts of data and computing using a network of multiple computers. It offers an integrated framework for data storage and processing of large data using the MapReduce programming model.Hadoop was designed specifically for multiple computers built from commodity hardware, which would still be commonly used. Since then, use has also been found on higher-end hardware clusters. All components in Hadoop are engineered with the key principle that hardware failures are regular events and should be managed immediately by the framework.
HKR trainings is one of the most successful e-learning platforms that delivers comprehensive best hadoop training in India. This training benefits both the newbies and experienced learners as well. During the training you will gain hands on experience with the four important components of hadoop namely developer, administrator, testing and analyst. By the endo fht etraining you will be more proficient and can easily attain the cloudera hadoop certification. During the training period, you will experience good support and real time project assistance. Get enrolled to make the best out of your hadoop certification training course in India.
To apply for the Hadoop Training in India, you need to either:
The Hadoop Training Certification offered in India was designed for professionals with and without work experience in any of the profiles below:
Well, there is no special requirement to take up the Hadoop training in India. The aspirants who wish to learn or build their career in data analytics can take up this course.
To commence your hadoop training course in Hyderabad, people therefore need to check with the perfect institute which really provides information. Before moving ahead with any training, accept advice from the professionals that have already experienced the course. We at HKR with a squad of industry experts, are ready to fulfil your future career in order to get a job in the company you want.
HKR trainings delivers the most curated hadoop course content in India with a team of qualified and professional experts in hadoop technology. One can have quick access to the optimized hadoop course structure in handy.
1.1 Introduction to Big Data and Hadoop
1.2 Introduction to Big Data
1.3 Big Data Analytics
1.4 What is Big Data
1.5 Four Vs Of Big Data
1.6 Case Study Royal Bank of Scotland
1.7 Challenges of Traditional System
1.8 Distributed Systems
1.9 Introduction to Hadoop
1.10 Components of Hadoop Ecosystem
1.11 Commercial Hadoop Distributions
2.1 Introduction to Hadoop Architecture Distributed Storage (HDFS) and YARN
2.2 What Is HDFS
2.3 Need for HDFS
2.4 Regular File System vs HDFS
2.5 Characteristics of HDFS
2.6 HDFS Architecture and Components
2.7 High Availability Cluster Implementations
2.8 HDFS Component File System Namespace
2.9 Data Block Split
2.10 Data Replication Topology
2.11 HDFS Command Line
2.12 YARN Introduction
2.13 YARN Use Case
2.14 YARN and Its Architecture
2.15 Resource Manager
2.16 How Resource Manager Operates
2.17 Application Master
2.18 How YARN Runs an Application
2.19 Tools for YARN Developers
3.1 Introduction to Data Ingestion into Big Data Systems and ETL
3.2 Overview of Data Ingestion
3.3 Apache Sqoop
3.4 Sqoop and Its Uses
3.5 Sqoop Processing
3.6 Sqoop Import Process
3.7 Sqoop Connectors
3.8 Apache Flume
3.9 Flume Model
3.10 Scalability in Flume
3.11 Components in Flume’s Architecture
3.12 Configuring Flume Components
3.13 Apache Kafka
3.14 Aggregating User Activity Using Kafka
3.15 Kafka Data Model
3.16 Partitions
3.17 Apache Kafka Architecture
3.18 Producer Side API Example
3.19 Consumer Side API
3.20 Consumer Side API Example
3.21 Kafka Connect
4.1 Introduction to Distributed Processing MapReduce Framework and Pig
4.2 Distributed Processing in MapReduce
4.3 Word Count Example
4.4 Map Execution Phases
4.5 Map Execution Distributed Two Node Environment
4.6 MapReduce Jobs
4.7 Hadoop MapReduce Job Work Interaction
4.8 Setting Up the Environment for MapReduce Development
4.9 Set of Classes
4.10 Creating a New Project
4.11 Advanced MapReduce
4.12 Data Types in Hadoop
4.13 OutputFormats in MapReduce
4.14 Using Distributed Cache
4.15 Joins in MapReduce
4.16 Replicated Join
4.17 Introduction to Pig
4.18 Components of Pig
4.19 Pig Data Model
4.20 Pig Interactive Modes
4.21 Pig Operations
4.22 Various Relations Performed by Developers
5.1 Introduction to Apache Hive
5.2 Hive SQL over Hadoop MapReduce
5.3 Hive Architecture
5.4 Interfaces to Run Hive Queries
5.5 Running Beeline from Command Line
5.6 Hive Metastore
5.7 Hive DDL and DML
5.8 Creating New Table
5.9 Data Types
5.10 Validation of Data
5.11 File Format Types
5.12 Data Serialization
5.13 Hive Table and Avro Schema
5.14 Hive Optimization Partitioning Bucketing and Sampling
5.15 Non-Partitioned Table
5.16 Data Insertion
5.17 Dynamic Partitioning in Hive
5.18 Bucketing
5.19 What Do Buckets Do
5.20 Hive Analytics UDF and UDAF
5.21 Other Functions of Hive
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