Best Big Data Training in Chennai
Learn from our Bigdata experts from the scratch to extreme level to do the analytics about the data. The syllabus designed to give you training more on practical way with set of scenario based question along with mini projects. It helps you to learn in expert level.
It is very difficult to process complex and large set of data With the help of traditional systems like RDBMS, Enterprise System, etc. Now a days any business like Banking, Finance, Telecom or social media like Twitter, Facebook generating more than 90% of data what they generated few years back. Hadoop is the system or frame work used for storing or handling those large volumn of data including different formats like structured, unstructured semi structured. This system is generally designed to scale up to thousands of servers. Also it is open source provided by Apache. Most of large giants like Google, Twitter, Facebook, Amazon, etc are moved their project into hadoop eco system.
This course used to analyze more on about data and you can play more on analytics part which will leads to become an data scientist. This course is best base of all data related technologies like Data Science, Machine Learning, IOT, AI, etc. In this digital world, Going forward every business developement cycle purely based on data and data analytics. So it helps everyone to get into analytics part more.
Bigdata Hadoop Development Course Content
Why we need Bigdata
Real time use cases of Bigdata Overview
Introduction to Apache Hadoop and the Hadoop Ecosystem
Apache Hadoop Overview
Data Ingestion and Storage
Data Locality
Data Analysis and Exploration
Other Ecosystem Tools
Why we need HDFS
Apache Hadoop Cluster Components
HDFS Architecture
Failures of HDFS 1.0
High Availability and Scaling
Pros and Cons of HDFS
Basics File system Operations
Hadoop FS or HDFS DFS - The Command-Line Interface
Decommission methods for Data Nodes
Exercise and small use case on HDFS
Overview and Architecture of Map Reduce
Components of MapReduce
How MapReduce works
Flow and Difference of MapReduce Version
YARN Architecture
Working with YARN
Types of Input formats & Output Formats
Examples of MapReduce Tasks
Reading Data from HDFS
Writing Data from HDFS
Replica Placement Strategy
Fault tolerance
Hive Installation on Ubuntu 14.04 With MySQL Database Metastore
Hive Overview and Architecture
Hive command execution in shell and HUE
Hive Data Loading methods
Hive Partition and Bucketing
External and Managed tables in Hive
File formats in Hive
Hive Joins
Serde in Hive
Functions in Hive
String Manipulation in Hive
Date Manipulation in Hive
Row level transformations in Hive
Indexes and Views in Hive
Hive Query Optimizers
Windowing Functions in Hive
Apache Sqoop Overview and Architecture
Apache Sqoop Import
Apache Sqoop Export
Sqoop Incremental load
Sqoop Eval
Managing Directories
File Formats
Compression Algorithm
Boundary Query and Split-by
Transformations and filtering
Delimiter and Handling Nulls
Sqoop import all tables
Column Mapping in Sqoop Export
Apache Pig Overview and Architecture
MapReduce Vs Pig
Data types of Pig
Pig Data loading methods
Pig Operators and execution modes
Load and Store Operators
Diagnostic Operators
Grouping and Joining
Combining and Splitting
Filtering and Sorting
Built-in Functions
Pig script execution in shell/HUE
Introduction to NoSQL/CAP theorem concepts
Apache HBase Overview and Architecture
Apache HBase Commands
HBase and Hive Integration module
Hbase execution in shell/HUE
Functional Programing Vs Object Orient Programing
Scala Overview
Configuring Apache Spark with Scala
Variable Declaration
Operations on variables
Conditional Expressions
Pattern Matching
Iteration
Scala Functions
Scala Oops Concept
Scala Abstract Class & Traits
Scala Access Modifier
Scala Array and String
Scala Exceptions
Scala Collections
Scala Tuples
Scala File handling
Scala Multithreading
Spark Ecosystem
What is Apache Spark?
Starting the Spark Shell
Using the Spark Shell
Getting Started with Datasets and Data Frames
Data Frame Operations
Apache Spark Overview and Architecture
RDD Overview
RDD Data Sources
Creating and Saving RDDs
RDD Operations
Transformations and Actions
Converting Between RDDs and Data Frames
Key-Value Pair RDDs
Map-Reduce operations
Other Pair RDD Operations
Creating Data Frames from Data Sources
Saving Data Frames to Data Sources
Data Frame Schemas
Eager and Lazy Execution
Querying Data Frames Using Column Expressions
Grouping and Aggregation Queries
Joining Data Frames
Querying Tables, Files, Views in Spark Using SQL
Comparing Spark SQL and Apache Hive-on-Spark
Creating Datasets
Loading and Saving Datasets
Dataset Operations
Introduction to Flume & features
Flume topology & core concepts
Flume Agents: Sources, Channels and Sinks
Property file parameters logic
Apache Kafka Installation
Apache Kafka Overview and Architecture
Consumer and Producer
Deploying Kafka in real world business scenarios
Integration with Spark for Spark Streaming
Introduction to zookeeper concepts
Overview and Architecture of Zookeeper
Zookeeper principles & usage in Hadoop framework
Use of Zookeeper in Hbase and Kafka
Oozie Fundamentals
Oozie workflow creations
Concepts of Coordinates and Bundles
HDFS
Map Reduce
Hadoop Shell Commands
Difference Between Questions
Apache Hive
Loading Methods and TablesPartition and Bucketing
Joins and Serde
File Formats and Data Types
Query Based Questions
Difference Between Questions
Infycle Technologies
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