Hadoop Development Training Malleswaram, Bangalore
Course Duraiton: 2 Month
IGEEKS Technologies: Hadoop Training Course Content
Hadoop Development Training course teaches experienced / knowledge peoples on purpose of Hadoop Technology, how to setup Hadoop Cluster, how to store BigData using Hadoop (HDFS) and how to process/analyze the BigData using Map-Reduce Programming or by using other Hadoop ecosystems.
Basic Unix Commands
Core Java (OOPS Concepts, Collections , Exceptions ) — For Map-Reduce Programming
SQL Query knowledge – For Hive Queries
Any Linux flavor OS (Ex: Ubuntu/Cent OS/Fedora/RedHat Linux) with 4 GB RAM (minimum), 100 GB HDD
Java 1.6+
MYSQL Database
Eclipse IDE
VM Ware (To use Linux OS along with Windows OS)
50 Hours, daily 1:30 Hours
Hadoop Training Course Content
High Availability
Scaling
Advantages and Challenges
What is Big data
Big Data opportunities
Big Data Challenges
Characteristics of Big data
Hadoop Distributed File System
Comparing Hadoop & SQL
Industries using Hadoop.
Data Locality.
Hadoop Architecture.
Map Reduce & HDFS.
Using the Hadoop single node image (Clone).
HDFS Design & Concepts
Blocks, Name nodes and Data nodes
HDFS High-Availability and HDFS Federation.
Hadoop DFS The Command-Line Interface
Basic File System Operations
Anatomy of File Read
Anatomy of File Write
Block Placement Policy and Modes
More detailed explanation about Configuration files.
Metadata, FS image, Edit log, Secondary Name Node and Safe Mode.
How to add New Data Node dynamically.
How to decommission a Data Node dynamically (Without stopping cluster).
FSCK Utility. (Block report).
How to override default configuration at system level and Programming level.
HDFS Federation.
ZOOKEEPER Leader Election Algorithm.
Exercise and small use case on HDFS.
Functional Programming Basics.
Map and Reduce Basics
How Map Reduce Works
Anatomy of a Map Reduce Job Run
Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
Job Completion, Failures
Shuffling and Sorting
Splits, Record reader, Partition, Types of partitions & Combiner
Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots.
Types of Schedulers and Counters.
Comparisons between Old and New API at code and Architecture Level.
Getting the data from RDBMS into HDFS using Custom data types.
Distributed Cache and Hadoop Streaming (Python, Ruby and R).
YARN.
Sequential Files and Map Files.
Enabling Compression Codec’s.
Map side Join with distributed Cache.
Types of I/O Formats: Multiple outputs, NLINEinputformat.
Handling small files using CombineFileInputFormat.
Hands on “Word Count” in Map/Reduce in standalone and Pseudo distribution Mode.
Sorting files using Hadoop Configuration API discussion
Emulating “grep” for searching inside a file in Hadoop
DBInput Format
Job Dependency API discussion
Input Format API discussion
Input Split API discussion
Custom Data type creation in Hadoop.
ACID in RDBMS and BASE in NoSQL.
CAP Theorem and Types of Consistency.
Types of NoSQL Databases in detail.
Columnar Databases in Detail (HBASE and CASSANDRA).
TTL, Bloom Filters and Compensation.
HBase Installation
HBase concepts
HBase Data Model and Comparison between RDBMS and NOSQL.
Master & Region Servers.
HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture.
Catalog Tables.
Block Cache and sharding.
SPLITS.
DATA Modeling (Sequential, Salted, Promoted and Random Keys).
JAVA API’s and Rest Interface.
Client Side Buffering and Process 1 million records using Client side Buffering.
HBASE Counters.
Enabling Replication and HBASE RAW Scans.
HBASE Filters.
Bulk Loading and Coprocessors (Endpoints and Observers with programs).
Real world use case consisting of HDFS, MR and HBASE.
Installation
Introduction and Architecture.
Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
Meta store
Hive QL
OLTP vs. OLAP
Working with Tables.
Primitive data types and complex data types.
Working with Partitions.
User Defined Functions
Hive Bucketed Tables and Sampling.
External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
Dynamic Partition
Differences between ORDER BY, DISTRIBUTE BY and SORT BY.
Bucketing and Sorted Bucketing with Dynamic partition.
RC File.
INDEXES and VIEWS.
MAPSIDE JOINS.
Compression on hive tables and Migrating Hive tables.
Dynamic substation of Hive and Different ways of running Hive
How to enable Update in HIVE.
Log Analysis on Hive.
Access HBASE tables using Hive.
Hands on Exercises
Installation
Execution Types
Grunt Shell
Pig Latin
Data Processing
Schema on read
Primitive data types and complex data types.
Tuple schema, BAG Schema and MAP Schema.
Loading and Storing
Filtering
Grouping & Joining
Debugging commands (Illustrate and Explain).
Validations in PIG.
Type casting in PIG.
Working with Functions
User Defined Functions
Types of JOINS in pig and Replicated Join in detail.
SPLITS and Multiquery execution.
Error Handling, FLATTEN and ORDER BY.
Parameter Substitution.
Nested For Each.
User Defined Functions, Dynamic Invokers and Macros.
How to access HBASE using PIG.
How to Load and Write JSON DATA using PIG.
Piggy Bank.
Hands on Exercises
Installation
Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV,Compressing,Control Parallelism, All tables Import)
Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
Free Form Query Import
Export data to RDBMS,HIVE and HBASE
Hands on Exercises.
Overview
Linking with Spark
Initializing Spark
Using the Shell
Resilient Distributed Datasets (RDDs)
Parallelized Collections
External Datasets
RDD Operations
Basics, Passing Functions to Spark
Working with Key-Value Pairs
Transformations
Actions
RDD Persistence
Which Storage Level to Choose?
Removing Data
Shared Variables
Broadcast Variables
Accumulators
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