Big Data Hadoop Training in Hyderabad
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Big Data Hadoop Certification Course Training in Hyderabad
Lucid IT Training Expertise in delivering high quality Training on Full Big Data Hadoop and Apache Spark Course in Hyderabad. We Trained students both Online and Corporate Trainings on hadoop and Spark. Provided Bigdata Infrastructure and Architectural solutions across multiple domains, Created effective BigData Course Content and Delivery methods based on Organizational requirements.
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Lucid IT Training delivers high quality Training with complete Big Data Hadoop and Spark Stack in Madhapur Hyderabad.
- Online and Corporate Training.
- Facilitating Bigdata Infrastructure and Architectural solutions across multiple domains
- Create effective BigData Course Content and Delivery methodolossed on Organizational requirements.
How we Teach Big Data Hadoop Training
Lucid IT Training is considered as the Big data Hadoop Training Institute in Madhapur, Hyderabad that offers quality and Instructor-led classes to all the intended audience. Students easy to interact with trainer Domain Expertise mainly helps the audience to leverage practical knowledge skills in Hadoop tool to solve complex business issues with an optimized solution.
Why choose Hadoop/Spark as a Career
So lets See What is Big Data?
How does that make Big Data Relevant?
If You are still thinking why you should learn BIG DATA analytics here are 3 important reasons for you to take Big Data as career-
Big Data Analytics are in High Demand
There is a immense scope of beginning a career in Big Data. Majority of companies are implementing Big Data technology. Companies are hunting for skilled Candidates as very few candidates are available in BigData. The job opportunities in Bigdata is increasing day by day and many IT professionals are investing their time and money to grab this opportunity. Big data market will grow 7 Times faster than the overall IT market and there is a shortage of 1500000 big data professionals by the end of 2018.So this is the right time to start your career in Big Data .
Huge Job opportunities and meeting skill Gap- Inspite of having the highest concentration of analytics globally, the scarcity of data analytics talent is particularly active and the demand for talent is expected to be on higher side. This is happening globally and still there is a large no of unfilled jobs across the globe.
Salary Aspects- According to 2015 survey reports published by the institute of analytics professional of Australia(IAPA) the annual salary of data analyst is $130000, up 4% from last year. The average hike of analytics profession in india is on an average 50% which is more than any It professionals.
Why train at Lucid IT Training?
- Instructor led Training.
- Expert trainers who can help solve complex queries on their feet.
- Hands on experience gained through assisted lab time.
- Certification assistance provided through exam like quizzes.
- Industry ready course content.
- Certified Trainers.
- Well equipped lab.
- Cost-effective training structure.
Hadoop Big Data Training Course Content In Madhapur Hyderabad
Hadoop
- BigData Introduction.
- Hadoop Introduction.
- Relation between BigData and Hadoop.
- Why all industries are talking about Bigdata (Real time use of Bigdata)
- Challenges with Big Data
- Storage
- Processing
- Technologies supporting Bigdata.
- Hadoop
- Database
» Traditional
» No SQL
- Comparison with other Technologies
- RDBM
- Data Warehouse
- Tera Data
- Components of Hadoop Eco System
- Storage Components
- Processing Components
- Integration of Hadoop with other Bigdata solutions.
HDFS (Hadoop Distributed File System)
- What is a Cluster Environment?
- Cluster Vs Hadoop Cluster
- Significance of HDFS in Hadoop
- Features of HDFS
- Storage aspects of HDFS
- Block
- How to Configure Block size
- Default Vs Configurable Block size
- Why HDFS Block size so large?
- Design Principles of Block size
- HDFS Architecture -5 Daemons of Hadoop
- Name Node and its Functionality
- Data Node and its Functionality
- Job Tracker and its Functionality
- Task Tracker and its Functionality
- Secondary Name Node and its Functionality
- Replication in Hadoop – Fail Over Mechanism
- Data Storage in Data Nodes
- Fail over Mechanism in Hadoop – Replication
- Replication Configuration
- Custom Replication
- Design Constraints with Replication Factor
- Accessing HDFS
- CLI (Command Line Interface) and HDFS Commands
- Java Based Approach
- Configuration of HDFS
- How to overcome drawbacks in HDFS
Map Reduce
- Why Map Reduce is essential in Hadoop?
- Processing Daemons of Hadoop
- Job Tracker
» Roles of Job Tracker
» Drawbacks w.r.t Job Tracker, Failure in Hadoop Cluster
» How to Configure Job Tracker in Hadoop Cluster
- Task Tracker
» Roles of Task Tracker
» Drawbacks w.r.t Task Tracker Failure in Hadoop Cluster
- Input Split
- Input Split
- Need of Input Split in Map Reduce
- Input Split size
- Input Split size Vs Block size
- Input Split Vs Mappers
- Map Reduce Life Cycle
- Communication Mechanism of Job Tracker & Task Tracker
- Input Format Class
- Record Reader Class
- Success Case Scenarios
- Failure Case Scenarios
- Retry Mechanism in Map Reduce
- Map Reduce Programming Model
- Different Phases of Map Reduce Algorithm
- Different Data types in Map Reduce
» Primitive Data types Vs Map Reduced Data types
- How to write a basic Map Reduce Program
» Driver code
- Importance of Driver code in a Map Reduce Program
- How to Identify the Driver code in Map Reduce Program
- Different Section of Driver code
- Mapper code
- Importance of Mapper phase in Map Reduce
- How to Write Reducer Class
- Methods in Mapper Class
- Reducer code
- Importance of Reduce Phase in Map Reduce
- How to Write Reducer Class?
- Methods in Reducer Class
- Identity Mapper & Identity Reducer
- Input Formats in Map Reduce
» Text lnput Format
» Key Value Text Input Format
» N Line Input Format
» DB Input Format
» Sequence File Input Format
» How to use the Specific input Format in Map Reduce
- Map reduce API(Application Programing Interface)
» New API
» Deprecated API
- Combiner in Map Reduce
» Is combiner mandate in Map Reduce
» How to use the combiner class in Map Reduce
» Performance tradeoffs w.r.t combiner
- Partitioner in Map Reduce
» Importance of partitioner class in Map Reduce
» How to use the partitioner class in Map Reduce
» Hash partitioner functionality
» How to write a custom partitioner
- Compression techniques in Map Reduce
- Compression techniques in Map Reduce
» Importance of compression in Map Reduce
» What is CODEC?
» Compression types
» GzipCodec
» BzipCodec
» LZOCodec
» SnappuCodec
» Configurations w.r.t compressions techniques
» How to customize the compressions per one job Vs all the job
- Joins in Map Reduce
» Map Side Join
» Reduce Side Join
» Performance trade off
» Distributed cache
- How to debug Map Reduce jobs in local and pseudo cluster mode?
- Introduction to Map reduce streaming
- Data localization in Map Reduce
- Secondary sorting use Map Reduce
Scala
Introduction to Scala
- Scala Introduction
- Scala versus Java
- Scala basics
- Scala Data types
- Scala packages
- Variable Declarations
- Variable Type Inference
- Control Structures
- Interactive Scala - Scala shell
- Writing Scala Scripts – Compiling the Scala Programs
- Defining Functions in Scala
- Different IDEs for Scala
Object Oriented Programming in Scala(Traits and OOPs)
- Traits introduction
- When to use Traits in Scala
- Creating Traits Basic OOPs
- Classes and Objects Basics
- Pattern matching in Scala
- Exception handling in Scala
Functional Programming in Scala
- What is Functional Programming
- Difference between Object Oriented and Functional Programming Paradigm
- Closures in Scala
Scala Environment Setup
- Scala setup on Windows
- Java Setup
» Scala setup
- Scala setup on Linux
» Java Setup
» Scala setup
SPARK
- Motivation for spark
- Spark vs Map Reduce Processing
- Architecture of Spark
- Spark Shell introduction
- Creating Spark Context
- File operations in Spark Shell
- Spark Project with SBT in Eclipse
- Caching in Spark
- Real time Examples of Spark
Resilient Distributed Dataset
- What is RDD and why it is important in Spark
- Transformation in Spark
- Actions in RDD
- Loading Data through RDD
- Saving Data
- Key-Value pair RDD
- Pair RDD operations
- Running Spark in a clustered Mode
- Deploying Application with Spark-submit
- Cluster Management
Spark SQL
- Introduction to Spark SQL
- The SQL Context
- Hive vs Spark SQL
- Spark SQL support for Text Files, Parquet and JSON files
- Data Frames
- Real time Examples of Spark SQL
Spark Streaming
- Introduction to Spark Streaming
- Architecture of Spark Streaming
- Spark Streaming vs Flume
- Introduction to Kafka
- Spark streaming Integration with Kafka overview
- Real time examples of Spark Streaming and Kafka
Spark Mlib
- Introduction to Machine Learning
- Vector Class in Mlib
- Spark Mlib Algorithms introduction
- Classification and Regression Algorithms
- Naïve Bayes Classification Algorithms
- Decision Trees Algorithm Overview
Apache PIG
- Introduction to Apache Pig
- Map Reduce versus Apache Pig
- SQL versus Apache Pig
- Different Data Types in Pig
- Modes of excellence in Pig
- Local ModeMap
- Reduce OR Distributed mode
- Execution Mechanism
- Grunt Shell
- Script
- Embedded
- Transformations in pig
- How to write a simple pig script
- How to develop the complex pig script
- Bags, Tuples and Fields in pig
- UDFs in Pig
- Need of using UDX in Pig
- How to use UDFs
- REGISTER Key word in Pig
- When to use Map Reduce and Apache Pig in real time projects
HIVE
- HIVE Introduction
- Need of Apache HIVE in Hadoop
- HIVE Architecture
- Driver
- Compiler
- Executor (Semantic Analyzer)
- Meta store in HIVE
- Importance of HIVE Meta store
- Embedded Meta store configuration
- External Meta store configuration
- Communication mechanism with Meta Store
- HIVE integration with Hadoop
- HIVE query language (HIVE QL)
- Configuring HIVE with MySQL
- Meta store
- SQL versus HIVE QL
- Data slicing mechanism
- Partitions in HIVE
- Buckets in HIVE
- Partitioning versus Bucketing
- Real time use cases
- Collection Data types in HIVE
- Array
- Struct
- Map
- UDFs in HIVE
- UDFs
- UDAFs
- UDTFs
- Need of UDFs in HIVE
- Hive serializer or Deserializer
- HIVE- HBASE Integration
SQOOP
- Introduction to SQOOP
- MySQL client and Server Installation
- How to connect to Relational Database using SQOOP
- Different SQOOP Commands
- Different flavors of Imports
- Export
- Hive-Imports
HBase
- HBase introduction
- HDFS Vs HBase
- HBase usecase
- HBase basics
- Column families
- Scans
- HBase architecture
- Clients
- Rest
- Thrif
- Java Based
- Arvo
- Map Reduce Integration
- Map Reduce over HBase
- HBase Admin
- Schema Definition
- Basic CRUD Operations
FLUME
- Flume introduction
- Flume Architecture
- Flume Master , Flume Collector and Flume Agent
- Flume Time Use Case using Apache Flume
Oozie
- Oozie introduction
- Oozie architecture
- Oozie Configuration Files
- Oozie Job Submission
- Workflow.xml
- Coordinator.xml
- job.coordinator.properties
YARN (Yet Another Resource Negotiators)
- YARN introduction
- YARN architecture
- Resource Manager
- Application Master
- Node Manager
- When should we go ahead with YARN
- Classic Map versus YARN Map Reduce
- Different configuration files for YARN
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