Big Data - Hadoop
Big Data is the fastest growing and most promising technology for handling large volumes of data for doing data analytics. This Big Data Hadoop training will help you to be up and running in the most demanding professional skills. Almost all the top MNC are trying to get into Big Data Hadoop hence there is a vast demand for Certified Big Data professionals. Our Big Data training will help you to learn big data and upgrade your career in the Big data domain.
Hadoop is
- It can process Distributed data and no need to store whole data in centralized storage as it is required for SQL based tools.
- Contains several tools for entire ETL data processing Framework
- Based on open source platforms
- A solution for BigData Problem
- Open Source Technology
Big data
- Data management – Industry Challenges
- Batch vs. Streaming data processing
- Big data Hadoop opportunities
- Characteristics of Big Data
- What is streaming data?
- Overview of Analytics
- Distributed computing
- Overview of Big Data
- Sources of Big Data
- Big Data examples
- Types of data
Hadoop
- Understanding the Hadoop configurations and Installation.
- Data centers and Hadoop Cluster overview
- Learning Linux required for Hadoop
- Overview of Hadoop Daemons
- Hadoop ecosystem tools overview
- Hadoop Cluster and Racks
- Why we need Hadoop
HDFS (Storage)
- HDFS Daemons – Namenode, Datanode, Secondary Namenode
- Hadoop FS and Processing Environment’s UIs
- How to read and write files
- How to read and write files
- Block Replication
- High Availability
- Fault-Tolerant
- HDFS
YARN (Hadoop Processing Framework)
- YARN Daemons – Resource Manager, NodeManager, etc.
- Job assignment & Execution flow
- YARN
MapReduce using Java (Processing Data)
- Writing and Executing the Basic MapReduce Program using Java
- Will cover five to Ten Map Reduce Examples with real-time data.
- Understand Difference Between Block and InputSplit
- Word Count Example(or) Election Vote Count
- Submission & Initialization of MapReduce Job.
- File Input/Output Formats in MapReduce Jobs
- Basic Configuration of MapReduce
- The introduction of MapReduce.
- Sequence File Input Format
- MapReduce Architecture
- Data flow in MapReduce
- How MapReduce Works
- Role of RecordReader
- Key Value Input Format
- MapReduce life cycle
- Reducer-side Joins
- NLine Input Format
- Text Input Format
- Map-side Joins
- Joins
Apache Hive
- Working with Avro Schema and AVRO file format
- Hands-on Multiple Real-Time datasets.
- Metastore DB and Metastore Service
- JDBC, ODBC connection to Hive
- Managed and External Tables
- Hiveserver2 (Thrift server)
- Hive Query Language (HQL)
- Partitioning & Bucketing
- OLTP vs. OLAP Concepts
- Data warehouse basics
- Query Optimization
- Hive Transactions
- Hive Architecture
- Hive UDFs
- Hive
Apache Pig
- Hands-On Two more examples daily use case data analysis in google. And Analysis on Date time dataset.
- Structured, Semi-Structure data processing in Pig
- The advantage of Pig over MapReduce
- Pig Latin (Scripting language for Pig)
- Schema and Schema-less data in Pig
- Pig vs. Hive Use case
- Apache Pig
- HCatalog
- Pig UDFs
HBASE (Real NoSQL )
- Column Family and Column Qualifier.
- Client-side buffering or bulk uploads
- How HBase differs from RDBMS
- Categories of NoSQL Data Bases
- Basic Configurations of HBASE
- Column Family Database
- Fundamentals of HBase
- HBase Designing Tables
- Cell and its Versioning
- Introduction to HBase
- Key-Value Database
- Document Database
- HBASE Architecture
- HBase Data Model
- HBase Operations
- What is NoSQL?
- Table and Row.
- Region Servers
- SQL vs. NoSQL
- HDFS vs. HBase
- Live Dataset
- MemStore
- HMaster
- Regions
- Delete
- Store
- Get
- Scan
- Put
Sqoop
- Sqoop practical implementation
- Importing data into HDFS
- Exporting data to RDBMS
- Importing data to Hive
- Sqoop connectors
- Sqoop commands
Flume
- How to load data in Hadoop that is coming from the web server or other storage
- How to load streaming data from Twitter data in HDFS using Hadoop
- The configuration of Source, Channel and Sink
- Fan-out flume agents
- Flume commands
Oozie
- How to schedule jobs which are time-based
- Action Node and Control Flow node
- Designing workflow jobs
- Oozie Conf file
- Oozie
- Apache ZooKeeper
- Apache ZooKeeper
- Apache ZooKeeper