Hbase training

Learn online HBase course and HBase certification course from experts at Rs. // USD 99 with 24*7 support and Lifetime access. Build skills & knowledge and become a HBase certified professional.

Learn online HBase course and HBase certification course from experts at Rs.5643 // USD 99 with 24*7 support and Lifetime access. Build skills & knowledge and become a HBase certified professional. Source URL - https://intellipaat.com/hbase-training/

Learn online HBase course and HBase certification course from experts at Rs.5643 // USD 99 with 24*7 support and Lifetime access. Build skills & knowledge and become a HBase certified professional. Source URL - https://intellipaat.in/hbase-training/

HADOOP ONLINE TRAINING,CORPORATE TRAINING,JOB AND INTERVIEW SUPPORT BY CORPORATE PROFESSIONALS Interview Questions and Answers, Recorded Video Sessions, Materials, Mock Interviews Assignments Will be provided Hadoop Concepts (we can modify the course content as per your requirement) HADOOP ADMIN AND DEVELOPMENT Introduction:- • Big Data Introduction • Hadoop Introduction • What is Hadoop? Why Hadoop? • Hadoop History? • Different types of Components in Hadoop? • HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on… • What is the scope of Hadoop? Deep Drive in HDFS (for Storing the Data):- • Introduction of HDFS • HDFS Design • HDFS role in Hadoop • Features of HDFS • Daemons of Hadoop and its functionality • Name Node • Secondary Name Node • Job Tracker • Data Node • Task Tracker • Anatomy of File Wright • Anatomy of File Read • Network Topology • Nodes • Racks • Data Center • Parallel Copying using DistCp • Basic Configuration for HDFS • Data Organization • Blocks and • Replication • Rack Awareness • Heartbeat Signal • How to Store the Data into HDFS • How to Read the Data from HDFS • Accessing HDFS (Introduction of Basic UNIX commands) • CLI commands MapReduce using Java (Processing the Data):- • Introduction of MapReduce. • MapReduce Architecture • Data flow in MapReduce • Splits • Mapper • Portioning • Sort and shuffle • Combiner • Reducer • Understand Difference Between Block and InputSplit • Role of RecordReader • Basic Configuration of MapReduce • MapReduce life cycle • Driver Code • Mapper • and Reducer • How MapReduce Works • Writing and Executing the Basic MapReduce Program using Java • Submission & Initialization of MapReduce Job. • File Input/output Formats in MapReduce Jobs • Text Input Format • Key Value Input Format • Sequence File Input Format • NLine Input Format • Joins • Map-side Joins • Reducer-side Joins • Word Count Example • Partition MapReduce Program • Side Data Distribution • Distributed Cache (with Program) • Counters (with Program) • Types of Counters • Task Counters • Job Counters • User Defined Counters • Propagation of Counters • Job Scheduling PIG:- • Introduction to Apache PIG • Introduction to PIG Data Flow Engine • MapReduce vs PIG in detail • When should PIG used? • Data Types in PIG • Basic PIG programming • Modes of Execution in PIG • Local Mode and • MapReduce Mode • Execution Mechanisms • Grunt Shell • Script • Embedded • Operators/Transformations in PIG • PIG UDF’s with Program • Word Count Example in PIG • The difference between the MapReduce and PIG SQOOP:- • Introduction to SQOOP • Use of SQOOP • Connect to mySql database • SQOOP commands • Import • Export • Eval • Codegen and etc… • Joins in SQOOP • Export to MySQL HIVE:- • Introduction to HIVE • HIVE Meta Store • HIVE Architecture • Tables in HIVE • Managed Tables • External Tables • Hive Data Types • Primitive Types • Complex Types • Partition • Joins in HIVE • HIVE UDF’s and UADF’s with Programs • Word Count Example HBASE:- • Introduction to HBASE • Basic Configurations of HBASE • Fundamentals of HBase • What is NoSQL? • HBase DataModel • Table and Row • Column Family and Column Qualifier • Cell and its Versioning • Categories of NoSQL Data Bases • Key-Value Database • Document Database • Column Family Database • SQL vs NOSQL • How HBASE is differ from RDBMS • HDFS vs HBase • Client side buffering or bulk uploads • HBase Designing Tables • HBase Operations • Get • Scan • Put • Delete MongoDB:-- • What is MongoDB? • Where to Use? • Configuration On Windows • Inserting the data into MongoDB? • Reading the MongoDB data. Cluster Setup:-- • Downloading and installing the Ubuntu12.x • Installing Java • Installing Hadoop • Creating Cluster • Increasing Decreasing the Cluster size • Monitoring the Cluster Health • Starting and Stopping the Nodes OOZIE • Introduction to OOZIE • Use of OOZIE • Where to use? Hadoop Ecosystem Overview Oozie HBase Pig Sqoop Casandra Chukwa Mahout Zoo Keeper Flume • Case Studies Discussions • Certification Guidance • Real Time Certification and • interview Questions and Answers • Resume Preparation • Providing all Materials nd Links • Real time Project Explanation and Practice

FOR FREE DEMO contact us at: Phone/WhatsApp: +91-(850) 012-2107 HADOOP ONLINE TRAINING BY REALTIME CORPORATE PROFESSIONALS Hadoop Course Content (we can customize the course content as per your requirement) HADOOP Using Cloudera Development & Admin Course Introduction to BigData, Hadoop:- • Big Data Introduction • Hadoop Introduction • What is Hadoop? Why Hadoop? • Hadoop History? • Different types of Components in Hadoop? • HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on… • What is the scope of Hadoop? Deep Drive in HDFS (for Storing the Data):- • Introduction of HDFS • HDFS Design • HDFS role in Hadoop • Features of HDFS • Daemons of Hadoop and its functionality • Name Node • Secondary Name Node • Job Tracker • Data Node • Task Tracker • Anatomy of File Wright • Anatomy of File Read • Network Topology • Nodes • Racks • Data Center • Parallel Copying using DistCp • Basic Configuration for HDFS • Data Organization • Blocks and • Replication • Rack Awareness • Heartbeat Signal • How to Store the Data into HDFS • How to Read the Data from HDFS • Accessing HDFS (Introduction of Basic UNIX commands) • CLI commands MapReduce using Java (Processing the Data):- • Introduction of MapReduce. • MapReduce Architecture • Data flow in MapReduce • Splits • Mapper • Portioning • Sort and shuffle • Combiner • Reducer • Understand Difference Between Block and InputSplit • Role of RecordReader • Basic Configuration of MapReduce • MapReduce life cycle • Driver Code • Mapper • and Reducer • How MapReduce Works • Writing and Executing the Basic MapReduce Program using Java • Submission & Initialization of MapReduce Job. • File Input/output Formats in MapReduce Jobs • Text Input Format • Key Value Input Format • Sequence File Input Format • NLine Input Format • Joins • Map-side Joins • Reducer-side Joins • Word Count Example • Partition MapReduce Program • Side Data Distribution • Distributed Cache (with Program) • Counters (with Program) • Types of Counters • Task Counters • Job Counters • User Defined Counters • Propagation of Counters • Job Scheduling PIG:- • Introduction to Apache PIG • Introduction to PIG Data Flow Engine • MapReduce vs PIG in detail • When should PIG used? • Data Types in PIG • Basic PIG programming • Modes of Execution in PIG • Local Mode and • MapReduce Mode • Execution Mechanisms • Grunt Shell • Script • Embedded • Operators/Transformations in PIG • PIG UDF’s with Program • Word Count Example in PIG • The difference between the MapReduce and PIG SQOOP:- • Introduction to SQOOP • Use of SQOOP • Connect to mySql database • SQOOP commands • Import • Export • Eval • Codegen and etc… • Joins in SQOOP • Export to MySQL HIVE:- • Introduction to HIVE • HIVE Meta Store • HIVE Architecture • Tables in HIVE • Managed Tables • External Tables • Hive Data Types • Primitive Types • Complex Types • Partition • Joins in HIVE • HIVE UDF’s and UADF’s with Programs • Word Count Example HBASE:- • Introduction to HBASE • Basic Configurations of HBASE • Fundamentals of HBase • What is NoSQL? • HBase DataModel • Table and Row • Column Family and Column Qualifier • Cell and its Versioning • Categories of NoSQL Data Bases • Key-Value Database • Document Database • Column Family Database • SQL vs NOSQL • How HBASE is differ from RDBMS • HDFS vs HBase • Client side buffering or bulk uploads • HBase Designing Tables • HBase Operations • Get • Scan • Put • Delete MongoDB:-- • What is MongoDB? • Where to Use? • Configuration On Windows • Inserting the data into MongoDB? • Reading the MongoDB data. Cluster Setup:-- • Downloading and installing the Ubuntu12.x • Installing Java • Installing Hadoop • Creating Cluster • Increasing Decreasing the Cluster size • Monitoring the Cluster Health • Starting and Stopping the Nodes OOZIE • Introduction to OOZIE • Use of OOZIE • Where to use? Hadoop Ecosystem Overview Oozie HBase Pig Sqoop Casandra Chukwa Mahout Zoo Keeper Flume • Case Studies Discussions • Certification Guidance • Real Time Certification and • interview Questions and Answers • Resume Preparation • Providing all Materials nd Links • Real time Project Explanation and Practice Phone/WhatsApp: +91-(850) 012-2107

BIGDATA –HADOOP REALTIME TRAINING,JOB SUPPORT,INTERVIEW SUPPORT BY HANDS-ON EXPERTS Phone/WhatsApp: +91-(850) 012-2107 Course Content HADOOP Using Cloudera Development & Admin Course Introduction to BigData, Hadoop:- • Big Data Introduction • Hadoop Introduction • What is Hadoop? Why Hadoop? • Hadoop History? • Different types of Components in Hadoop? • HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on… • What is the scope of Hadoop? Deep Drive in HDFS (for Storing the Data):- • Introduction of HDFS • HDFS Design • HDFS role in Hadoop • Features of HDFS • Daemons of Hadoop and its functionality • Name Node • Secondary Name Node • Job Tracker • Data Node • Task Tracker • Anatomy of File Wright • Anatomy of File Read • Network Topology • Nodes • Racks • Data Center • Parallel Copying using DistCp • Basic Configuration for HDFS • Data Organization • Blocks and • Replication • Rack Awareness • Heartbeat Signal • How to Store the Data into HDFS • How to Read the Data from HDFS • Accessing HDFS (Introduction of Basic UNIX commands) • CLI commands MapReduce using Java (Processing the Data):- • Introduction of MapReduce. • MapReduce Architecture • Data flow in MapReduce • Splits • Mapper • Portioning • Sort and shuffle • Combiner • Reducer • Understand Difference Between Block and InputSplit • Role of RecordReader • Basic Configuration of MapReduce • MapReduce life cycle • Driver Code • Mapper • and Reducer • How MapReduce Works • Writing and Executing the Basic MapReduce Program using Java • Submission & Initialization of MapReduce Job. • File Input/output Formats in MapReduce Jobs • Text Input Format • Key Value Input Format • Sequence File Input Format • NLine Input Format • Joins • Map-side Joins • Reducer-side Joins • Word Count Example • Partition MapReduce Program • Side Data Distribution • Distributed Cache (with Program) • Counters (with Program) • Types of Counters • Task Counters • Job Counters • User Defined Counters • Propagation of Counters • Job Scheduling PIG:- • Introduction to Apache PIG • Introduction to PIG Data Flow Engine • MapReduce vs PIG in detail • When should PIG used? • Data Types in PIG • Basic PIG programming • Modes of Execution in PIG • Local Mode and • MapReduce Mode • Execution Mechanisms • Grunt Shell • Script • Embedded • Operators/Transformations in PIG • PIG UDF’s with Program • Word Count Example in PIG • The difference between the MapReduce and PIG SQOOP:- • Introduction to SQOOP • Use of SQOOP • Connect to mySql database • SQOOP commands • Import • Export • Eval • Codegen and etc… • Joins in SQOOP • Export to MySQL HIVE:- • Introduction to HIVE • HIVE Meta Store • HIVE Architecture • Tables in HIVE • Managed Tables • External Tables • Hive Data Types • Primitive Types • Complex Types • Partition • Joins in HIVE • HIVE UDF’s and UADF’s with Programs • Word Count Example HBASE:- • Introduction to HBASE • Basic Configurations of HBASE • Fundamentals of HBase • What is NoSQL? • HBase DataModel • Table and Row • Column Family and Column Qualifier • Cell and its Versioning • Categories of NoSQL Data Bases • Key-Value Database • Document Database • Column Family Database • SQL vs NOSQL • How HBASE is differ from RDBMS • HDFS vs HBase • Client side buffering or bulk uploads • HBase Designing Tables • HBase Operations • Get • Scan • Put • Delete MongoDB:-- • What is MongoDB? • Where to Use? • Configuration On Windows • Inserting the data into MongoDB? • Reading the MongoDB data. Cluster Setup:-- • Downloading and installing the Ubuntu12.x • Installing Java • Installing Hadoop • Creating Cluster • Increasing Decreasing the Cluster size • Monitoring the Cluster Health • Starting and Stopping the Nodes OOZIE • Introduction to OOZIE • Use of OOZIE • Where to use? Hadoop Ecosystem Overview Oozie HBase Pig Sqoop Casandra Chukwa Mahout Zoo Keeper Flume • Case Studies Discussions • Certification Guidance • Real Time Certification and • interview Questions and Answers • Resume Preparation • Providing all Materials nd Links • Real time Project Explanation and Practice

BEST HADOOP LIVE TRAINING BY REALTIME CORPORATE PROFESSIONALS Hadoop Course Agenda (we can customize the course content as per your requirement) HADOOP Using Cloudera Development & Admin Course Introduction to BigData, Hadoop:- • Big Data Introduction • Hadoop Introduction • What is Hadoop? Why Hadoop? • Hadoop History? • Different types of Components in Hadoop? • HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on… • What is the scope of Hadoop? Deep Drive in HDFS (for Storing the Data):- • Introduction of HDFS • HDFS Design • HDFS role in Hadoop • Features of HDFS • Daemons of Hadoop and its functionality • Name Node • Secondary Name Node • Job Tracker • Data Node • Task Tracker • Anatomy of File Wright • Anatomy of File Read • Network Topology • Nodes • Racks • Data Center • Parallel Copying using DistCp • Basic Configuration for HDFS • Data Organization • Blocks and • Replication • Rack Awareness • Heartbeat Signal • How to Store the Data into HDFS • How to Read the Data from HDFS • Accessing HDFS (Introduction of Basic UNIX commands) • CLI commands MapReduce using Java (Processing the Data):- • Introduction of MapReduce. • MapReduce Architecture • Data flow in MapReduce • Splits • Mapper • Portioning • Sort and shuffle • Combiner • Reducer • Understand Difference Between Block and InputSplit • Role of RecordReader • Basic Configuration of MapReduce • MapReduce life cycle • Driver Code • Mapper • and Reducer • How MapReduce Works • Writing and Executing the Basic MapReduce Program using Java • Submission & Initialization of MapReduce Job. • File Input/output Formats in MapReduce Jobs • Text Input Format • Key Value Input Format • Sequence File Input Format • NLine Input Format • Joins • Map-side Joins • Reducer-side Joins • Word Count Example • Partition MapReduce Program • Side Data Distribution • Distributed Cache (with Program) • Counters (with Program) • Types of Counters • Task Counters • Job Counters • User Defined Counters • Propagation of Counters • Job Scheduling PIG:- • Introduction to Apache PIG • Introduction to PIG Data Flow Engine • MapReduce vs PIG in detail • When should PIG used? • Data Types in PIG • Basic PIG programming • Modes of Execution in PIG • Local Mode and • MapReduce Mode • Execution Mechanisms • Grunt Shell • Script • Embedded • Operators/Transformations in PIG • PIG UDF’s with Program • Word Count Example in PIG • The difference between the MapReduce and PIG SQOOP:- • Introduction to SQOOP • Use of SQOOP • Connect to mySql database • SQOOP commands • Import • Export • Eval • Codegen and etc… • Joins in SQOOP • Export to MySQL HIVE:- • Introduction to HIVE • HIVE Meta Store • HIVE Architecture • Tables in HIVE • Managed Tables • External Tables • Hive Data Types • Primitive Types • Complex Types • Partition • Joins in HIVE • HIVE UDF’s and UADF’s with Programs • Word Count Example HBASE:- • Introduction to HBASE • Basic Configurations of HBASE • Fundamentals of HBase • What is NoSQL? • HBase DataModel • Table and Row • Column Family and Column Qualifier • Cell and its Versioning • Categories of NoSQL Data Bases • Key-Value Database • Document Database • Column Family Database • SQL vs NOSQL • How HBASE is differ from RDBMS • HDFS vs HBase • Client side buffering or bulk uploads • HBase Designing Tables • HBase Operations • Get • Scan • Put • Delete MongoDB:-- • What is MongoDB? • Where to Use? • Configuration On Windows • Inserting the data into MongoDB? • Reading the MongoDB data. Cluster Setup:-- • Downloading and installing the Ubuntu12.x • Installing Java • Installing Hadoop • Creating Cluster • Increasing Decreasing the Cluster size • Monitoring the Cluster Health • Starting and Stopping the Nodes OOZIE • Introduction to OOZIE • Use of OOZIE • Where to use? Hadoop Ecosystem Overview Oozie HBase Pig Sqoop Casandra Chukwa Mahout Zoo Keeper Flume • Case Studies Discussions • Certification Guidance • Real Time Certification and • interview Questions and Answers • Resume Preparation • Providing all Materials nd Links • Real time Project Explanation and Practice

Demo has Scheduled Today BEST HADOOP ONLINE CERTIFICATION LEVEL TRAINING FROM HYDERABAD,INDIA FOR FREE DEMO contact us: Phone/WhatsApp: +91-(850) 012-2107 Interview Questions and Answers, Recorded Video Sessions, Materials, Mock Interviews Assignments Will be provided Hadoop Key Concepts (we can modify the course content as per your requirement) HADOOP Using Cloudera Development & Admin Course Introduction:- • Big Data Introduction • Hadoop Introduction • What is Hadoop? Why Hadoop? • Hadoop History? • Different types of Components in Hadoop? • HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on… • What is the scope of Hadoop? Deep Drive in HDFS (for Storing the Data):- • Introduction of HDFS • HDFS Design • HDFS role in Hadoop • Features of HDFS • Daemons of Hadoop and its functionality • Name Node • Secondary Name Node • Job Tracker • Data Node • Task Tracker • Anatomy of File Wright • Anatomy of File Read • Network Topology • Nodes • Racks • Data Center • Parallel Copying using DistCp • Basic Configuration for HDFS • Data Organization • Blocks and • Replication • Rack Awareness • Heartbeat Signal • How to Store the Data into HDFS • How to Read the Data from HDFS • Accessing HDFS (Introduction of Basic UNIX commands) • CLI commands MapReduce using Java (Processing the Data):- • Introduction of MapReduce. • MapReduce Architecture • Data flow in MapReduce • Splits • Mapper • Portioning • Sort and shuffle • Combiner • Reducer • Understand Difference Between Block and InputSplit • Role of RecordReader • Basic Configuration of MapReduce • MapReduce life cycle • Driver Code • Mapper • and Reducer • How MapReduce Works • Writing and Executing the Basic MapReduce Program using Java • Submission & Initialization of MapReduce Job. • File Input/output Formats in MapReduce Jobs • Text Input Format • Key Value Input Format • Sequence File Input Format • NLine Input Format • Joins • Map-side Joins • Reducer-side Joins • Word Count Example • Partition MapReduce Program • Side Data Distribution • Distributed Cache (with Program) • Counters (with Program) • Types of Counters • Task Counters • Job Counters • User Defined Counters • Propagation of Counters • Job Scheduling PIG:- • Introduction to Apache PIG • Introduction to PIG Data Flow Engine • MapReduce vs PIG in detail • When should PIG used? • Data Types in PIG • Basic PIG programming • Modes of Execution in PIG • Local Mode and • MapReduce Mode • Execution Mechanisms • Grunt Shell • Script • Embedded • Operators/Transformations in PIG • PIG UDF’s with Program • Word Count Example in PIG • The difference between the MapReduce and PIG SQOOP:- • Introduction to SQOOP • Use of SQOOP • Connect to mySql database • SQOOP commands • Import • Export • Eval • Codegen and etc… • Joins in SQOOP • Export to MySQL HIVE:- • Introduction to HIVE • HIVE Meta Store • HIVE Architecture • Tables in HIVE • Managed Tables • External Tables • Hive Data Types • Primitive Types • Complex Types • Partition • Joins in HIVE • HIVE UDF’s and UADF’s with Programs • Word Count Example HBASE:- • Introduction to HBASE • Basic Configurations of HBASE • Fundamentals of HBase • What is NoSQL? • HBase DataModel • Table and Row • Column Family and Column Qualifier • Cell and its Versioning • Categories of NoSQL Data Bases • Key-Value Database • Document Database • Column Family Database • SQL vs NOSQL • How HBASE is differ from RDBMS • HDFS vs HBase • Client side buffering or bulk uploads • HBase Designing Tables • HBase Operations • Get • Scan • Put • Delete MongoDB:-- • What is MongoDB? • Where to Use? • Configuration On Windows • Inserting the data into MongoDB? • Reading the MongoDB data. Cluster Setup:-- • Downloading and installing the Ubuntu12.x • Installing Java • Installing Hadoop • Creating Cluster • Increasing Decreasing the Cluster size • Monitoring the Cluster Health • Starting and Stopping the Nodes OOZIE • Introduction to OOZIE • Use of OOZIE • Where to use? Hadoop Ecosystem Overview Oozie HBase Pig Sqoop Casandra Chukwa Mahout Zoo Keeper Flume • Case Studies Discussions • Certification Guidance • Real Time Certification and • interview Questions and Answers • Resume Preparation • Providing all Materials nd Links • Real time Project Explanation and Practice Phone/WhatsApp: +91-(850) 012-2107

With 2.6 quintillion bytes of data being produced on a daily basis, there is a fast growing need of big data & Hadoop training to nurture professionals who can manage and analyse massive (tera/petabytes) data-sets to bring out business insights. To do this requires specialized knowledge on various tools like the Hadoop ecosystem. Through this big data & Hadoop training course, the participants will gain all the skills requisite to be a Hadoop Developer. The participant will learn the installation of Hadoop cluster, understand the basic and advanced concepts of Map Reduce tools used by Big Data analysts like Pig, Hive, Flume, Sqoop and Oozie. The course will train you towards global certifications offered by Cloudera, Hortonworks etc. The course will be very useful for engineers and software professionals with a programming background. HADOOP Online Training by Peopleclick with an excellent and real time faculty. Our Hadoop Big Data course content designed as per the current IT industry requirement. Apache Hadoop is having very good demand in the market, huge number of job openings are there in the IT world. We provide regular and weekend classes as well as Normal track/Fast track based on the learners requirement and availability. As all our faculty is real time professionals our trainer will cover all the real time scenarios. We have trained many students on Apache Hadoop Big Data, We provide Corporate Trainings and Online trainings throughout the world. We will give you 100% satisfaction guarantee, After completion of Hadoop Training we provide days technical support for required candidates. We will provide 100% certification support, resume preparation and Placements. HADOOP ONLINE TRAINING COURSE CONTENT: We provide both Hadoop Development and Admin Training. 1. INTRODUCTION What is Hadoop? History of Hadoop Building Blocks – Hadoop Eco-System Who is behind Hadoop? What Hadoop is good for and why it is Good 2. HDFS Configuring HDFS Interacting With HDFS HDFS Permissions and Security Additional HDFS Tasks HDFS Overview and Architecture HDFS Installation Hadoop File System Shell File System Java API 3. MAPREDUCE Map/Reduce Overview and Architecture Installation Developing Map/Red Jobs Input and Output Formats Job Configuration Job Submission Practicing Map Reduce Programs (atleast 10 Map Reduce Algorithms) 4. Getting Started With Eclipse IDE Configuring Hadoop API on Eclipse IDE Connecting Eclipse IDE to HDFS 5. Hadoop Streaming 6. Advanced MapReduce Features Custom Data Types Input Formats Output Formats Partitioning Data Reporting Custom Metrics Distributing Auxiliary Job Data 7. Distributing Debug Scripts 8.Using Yahoo Web Services 9. Pig Pig Overview Installation Pig Latin Pig with HDFS 10. Hive Hive Overview Installation Hive QL Hive Unstructured Data Analyzation Hive Semistructured Data Analyzation 11.HBase HBase Overview and Architecture HBase Installation HBase Shell CRUD operations Scanning and Batching Filters HBase Key Design 12.ZooKeeper Zoo Keeper Overview Installation Server Mantainace 13.Sqoop Sqoop Overview Installation Imports and Exports 14.CONFIGURATION Basic Setup Important Directories Selecting Machines Cluster Configurations Small Clusters: 2-10 Nodes Medium Clusters: Nodes Large Clusters: Multiple Racks 15.Integrations 16.Putting it all together Distributed installations Best Practices Benefits of taking the Hadoop & Big Data course •Learn to store, manage, retrieve and analyze Big Data on clusters of servers in the cloud using the Hadoop eco-system •Become one of the most in-demand IT professional in the world today •Don't just learn Hadoop development but also learn how to analyze large amounts of data to bring out insights •Relevant examples and cases make the learning more effective and easier •Gain hands-on knowledge through the problem solving based approach of the course along with working on a project at the end of the course Who should take this course? This course is designed for anyone who: •wants to architect a big data project using Hadoop and its Eco System components •wants to develop Map Reduce programs to handle enormous amounts of data •you have a programming background and want to take your career to another level Pre-requisites •The participants need basic knowledge of core Java for this course. (If you do not have Java knowledge, then do not worry. We provide you with our 'Java Primer for Hadoop' course complimentary with this course so that you can learn the requisite Java skills) •Any experience of Linux environment will be helpful but is not necessary

Through this big data & Hadoop training course, the participants will gain all the skills requisite to be a Hadoop Developer. The participant will learn the installation of Hadoop cluster, understand the basic and advanced concepts of Map Reduce tools used by Big Data analysts like Pig, Hive, Flume, Sqoop and Oozie. The course will train you towards global certifications offered by Cloudera, Hortonworks etc. The course will be very useful for engineers and software professionals with a programming background. HADOOP Online Training by Peopleclick with an excellent and real time faculty. Our Hadoop Big Data course content designed as per the current IT industry requirement. Apache Hadoop is having very good demand in the market, huge number of job openings are there in the IT world. We provide regular and weekend classes as well as Normal track/Fast track based on the learners requirement and availability. As all our faculty is real time professionals our trainer will cover all the real time scenarios. We have trained many students on Apache Hadoop Big Data, We provide Corporate Trainings and Online trainings throughout the world. We will give you 100% satisfaction guarantee, After completion of Hadoop Training we provide days technical support for required candidates. We will provide 100% certification support, resume preparation and Placements. HADOOP ONLINE TRAINING COURSE CONTENT: We provide both Hadoop Development and Admin Training. 1. INTRODUCTION What is Hadoop? History of Hadoop Building Blocks – Hadoop Eco-System Who is behind Hadoop? What Hadoop is good for and why it is Good 2. HDFS Configuring HDFS Interacting With HDFS HDFS Permissions and Security Additional HDFS Tasks HDFS Overview and Architecture HDFS Installation Hadoop File System Shell File System Java API 3. MAPREDUCE Map/Reduce Overview and Architecture Installation Developing Map/Red Jobs Input and Output Formats Job Configuration Job Submission Practicing Map Reduce Programs (atleast 10 Map Reduce Algorithms) 4. Getting Started With Eclipse IDE Configuring Hadoop API on Eclipse IDE Connecting Eclipse IDE to HDFS 5. Hadoop Streaming 6. Advanced MapReduce Features Custom Data Types Input Formats Output Formats Partitioning Data Reporting Custom Metrics Distributing Auxiliary Job Data 7. Distributing Debug Scripts 8.Using Yahoo Web Services 9. Pig Pig Overview Installation Pig Latin Pig with HDFS 10. Hive Hive Overview Installation Hive QL Hive Unstructured Data Analyzation Hive Semistructured Data Analyzation 11.HBase HBase Overview and Architecture HBase Installation HBase Shell CRUD operations Scanning and Batching Filters HBase Key Design 12.ZooKeeper Zoo Keeper Overview Installation Server Mantainace 13.Sqoop Sqoop Overview Installation Imports and Exports 14.CONFIGURATION Basic Setup Important Directories Selecting Machines Cluster Configurations Small Clusters: 2-10 Nodes Medium Clusters: Nodes Large Clusters: Multiple Racks 15.Integrations 16.Putting it all together Distributed installations Best Practices Benefits of taking the Hadoop & Big Data course •Learn to store, manage, retrieve and analyze Big Data on clusters of servers in the cloud using the Hadoop eco-system •Become one of the most in-demand IT professional in the world today •Don't just learn Hadoop development but also learn how to analyze large amounts of data to bring out insights •Relevant examples and cases make the learning more effective and easier •Gain hands-on knowledge through the problem solving based approach of the course along with working on a project at the end of the course Who should take this course? This course is designed for anyone who: •wants to architect a big data project using Hadoop and its Eco System components •wants to develop Map Reduce programs to handle enormous amounts of data •you have a programming background and want to take your career to another level Pre-requisites •The participants need basic knowledge of core Java for this course. (If you do not have Java knowledge, then do not worry. We provide you with our 'Java Primer for Hadoop' course complimentary with this course so that you can learn the requisite Java skills) •Any experience of Linux environment will be helpful but is not necessary

With 2.6 quintillion bytes of data being produced on a daily basis, there is a fast growing need of big data & Hadoop training to nurture professionals who can manage and analyse massive (tera/petabytes) data-sets to bring out business insights. To do this requires specialized knowledge on various tools like the Hadoop ecosystem. Through this big data & Hadoop training course, the participants will gain all the skills requisite to be a Hadoop Developer. The participant will learn the installation of Hadoop cluster, understand the basic and advanced concepts of Map Reduce tools used by Big Data analysts like Pig, Hive, Flume, Sqoop and Oozie. The course will train you towards global certifications offered by Cloudera, Hortonworks etc. The course will be very useful for engineers and software professionals with a programming background. HADOOP Online Training by Peopleclick with an excellent and real time faculty. Our Hadoop Big Data course content designed as per the current IT industry requirement. Apache Hadoop is having very good demand in the market, huge number of job openings are there in the IT world. We provide regular and weekend classes as well as Normal track/Fast track based on the learners requirement and availability. As all our faculty is real time professionals our trainer will cover all the real time scenarios. We have trained many students on Apache Hadoop Big Data, We provide Corporate Trainings and Online trainings throughout the world. We will give you 100% satisfaction guarantee, After completion of Hadoop Training we provide days technical support for required candidates. We will provide 100% certification support, resume preparation and Placements. HADOOP ONLINE TRAINING COURSE CONTENT: We provide both Hadoop Development and Admin Training. 1. INTRODUCTION What is Hadoop? History of Hadoop Building Blocks – Hadoop Eco-System Who is behind Hadoop? What Hadoop is good for and why it is Good 2. HDFS Configuring HDFS Interacting With HDFS HDFS Permissions and Security Additional HDFS Tasks HDFS Overview and Architecture HDFS Installation Hadoop File System Shell File System Java API 3. MAPREDUCE Map/Reduce Overview and Architecture Installation Developing Map/Red Jobs Input and Output Formats Job Configuration Job Submission Practicing Map Reduce Programs (atleast 10 Map Reduce Algorithms) 4. Getting Started With Eclipse IDE Configuring Hadoop API on Eclipse IDE Connecting Eclipse IDE to HDFS 5. Hadoop Streaming 6. Advanced MapReduce Features Custom Data Types Input Formats Output Formats Partitioning Data Reporting Custom Metrics Distributing Auxiliary Job Data 7. Distributing Debug Scripts 8.Using Yahoo Web Services 9. Pig Pig Overview Installation Pig Latin Pig with HDFS 10. Hive Hive Overview Installation Hive QL Hive Unstructured Data Analyzation Hive Semistructured Data Analyzation 11.HBase HBase Overview and Architecture HBase Installation HBase Shell CRUD operations Scanning and Batching Filters HBase Key Design 12.ZooKeeper Zoo Keeper Overview Installation Server Mantainace 13.Sqoop Sqoop Overview Installation Imports and Exports 14.CONFIGURATION Basic Setup Important Directories Selecting Machines Cluster Configurations Small Clusters: 2-10 Nodes Medium Clusters: Nodes Large Clusters: Multiple Racks 15.Integrations 16.Putting it all together Distributed installations Best Practices Benefits of taking the Hadoop & Big Data course •Learn to store, manage, retrieve and analyze Big Data on clusters of servers in the cloud using the Hadoop eco-system •Become one of the most in-demand IT professional in the world today •Don't just learn Hadoop development but also learn how to analyze large amounts of data to bring out insights •Relevant examples and cases make the learning more effective and easier •Gain hands-on knowledge through the problem solving based approach of the course along with working on a project at the end of the course Who should take this course? This course is designed for anyone who: •wants to architect a big data project using Hadoop and its Eco System components •wants to develop Map Reduce programs to handle enormous amounts of data •you have a programming background and want to take your career to another level Pre-requisites •The participants need basic knowledge of core Java for this course. (If you do not have Java knowledge, then do not worry. We provide you with our 'Java Primer for Hadoop' course complimentary with this course so that you can learn the requisite Java skills) •Any experience of Linux environment will be helpful but is not necessary For Further query call us @ or

SADGURU TECHNOLOGIES - 04040154733 8179736190 1.Intro to Hadoop, BigData •What is BigData? •Parallel Computer vs. Distributed Computing •Brief history of Hadoop •RDBMS/SQL vs. Hadoop •Scaling with Hadoop •Intro to the Hadoop ecosystem •Optimal hardware and network configurations for Hadoop 2.HDFS – Hadoop Distributed File System •Linux File system options •NameNode architecture •Secondary NameNode architecture •DataNode architecture •Heartbeats, Rack Awareness, Health Check •Exploring the HDFS Web UI LAB #2: HDFS CMD Line 3.Beginning MapReduce •MapReduce Architecture •JobTracker/TaskTracker •Combiner •Partitioner •Shuffle and Sort •Exploring the MapReduce Web UI •Walkthrough of a simple Java MapReduce example •Use case: Word Count in MapReduce LAB #3: Running MapReduce in Java 4.Advanced MapReduce •Data Types and File Formats. •Driver, Mapper & Reducer Class Code. •Build Map & Reduce programs using Eclipse. •Serialization and File-Based Data Structures •Input/output formats •Counters •Run Map Reduce locally and on cluster. LAB #4: Java MapReduce API 5.Hive for Structured Data •Hive architecture •Hive vs. RDBMS •HiveQL and Hive Shell •Managing tables •Data types and schemas •Querying data •Partitions and Buckets •Intro to User Defined Functions LAB #5: Exploring Hive Commands 6.Overview of NoSql and HBase •Introduction of NoSql. •CAP Theorem. •HBase architecture •HBase versions and origins •HBase vs. RDBMS •Data Modeling •Column Families and Regions LAB #6: Intro to HBase Command Line 7.Working with Sqoop •Introduction to Sqoop •Import Data •Export Data •Sqoop Syntaxes •Database Connection Lab#7: Hands on exercise on Sqoop and mysql DB. Thanks & Regards, SADGURU TECHNOLOGIES H. No: 7-1-621/10, Flat No: 102, Sai Manor Apartment, S.R. Nagar Main Road, Hyderabad-500038, Landmark: Beside Umesh Chandra Statue, Approach Road Parallel to Main Road Mob: 91-8179736190, Ph: 040-40154733 USA: +1 (701) 660-0529

Big Data & Hadoop Classroon Developer Training In Bangalore. Introduction Course Objective Summary During this course, you will learn: • Introduction to Big Data and Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • HBASE Concepts • Mongo DB Concepts • Sqoop Concepts • Real Life Use Cases Introduction to Big Data and Hadoop • What is Big Data? • What are the challenges for processing big data? • What technologies support big data? • What is Hadoop? • Why Hadoop? • History of Hadoop • Use Cases of Hadoop • Hadoop eco System • HDFS • Map Reduce • Statistics Understanding the Cluster •Typical workflow • Writing files to HDFS • Reading files from HDFS • Rack Awareness • 5 daemons Let's talk Map Reduce • Before Map reduce • Map Reduce Overview • Word Count Problem • Word Count Flow and Solution • Map Reduce Flow • Algorithms for simple problems • Algorithms for complex problems Developing the Map Reduce Application • Data Types • File Formats • Explain the Driver, Mapper and Reducer code • Configuring development environment - Eclipse • Writing Unit Test • Running locally • Running on Cluster • Hands on exercises c • Anatomy of Map Reduce Job run • Job Submission • Job Initialization • Task Assignment • Job Completion • Job Scheduling • Job Failures • Shuffle and sort • Oozie Workflows • Hands on Exercises Map Reduce Types and Formats • MapReduce Types • Input Formats - Input splits & records, text input,binary input, multiple inputs & database input. • Output Formats - text Output, binary output, multiple outputs, lazy output and database output • Hands on Exercises. Map Reduce Features •Counters • Sorting • Joins - Map Side and Reduce Side • Side Data Distribution • MapReduce Combiner • MapReduce Partitioner • MapReduce Distributed Cache • Hands Exercises Hive and PIG • Fundamentals • When to Use PIG and HIVE • Concepts • Hands on Exercises HBASE • CAP Theorem • Hbase Architecture and concepts • Programming and Hands on Exercises Case Studies Discussions Thanks&Regards Softech Solution.

Syllabus- Course Objective Summary During this course, you will learn: Introduction to Big Data and Hadoop Hadoop ecosystem - Concepts Hadoop Map - reduce concepts and features Developing the map - reduce Applications Pig concepts Hive concepts Oozie workflow concepts HBASE Concepts Real Life Use Cases Introduction to Big Data and Hadoop What is Big Data? What are the challenges for processing big data? What technologies support big data? What is Hadoop? Why Hadoop? History of Hadoop Use Cases of Hadoop Hadoop eco Syst em HDFS Map Reduce Statistics Understanding the Cluster Typical workflow Writing files to HDFS Reading files from HDFS Rack Awareness 5 daemons Let's talk Map Reduce Before Map reduce Map Reduce Overview Word Count Problem Word Count Flow and Solution Map Reduce Flow Algorithms for simple & Complex problems Developing the Map Reduce Application Data Types File Formats Explain the Driver, Mapper and Reducer code Configuring development environment - Eclipse Writing Unit Test Running locally Running on Cluster Hands on exercises How Map - Reduce Works Anatomy of Map Reduce Job run Job Submission Job Initialization Task Assignment Job Completion Job Scheduling Job Failures Shuffle and sort Oozie Workflows Hands on Exercises Map Reduce Types and Formats Map Reduce Types Input Formats - Input splits & records, text input, binary input, multiple inputs & database input Output Formats - text Output, binary output, multiple outputs, lazy output and database output Hands on Exercises Map Reduce Features Counters Sorting Joins - Map Side and Reduce Side Side Data Distribution MapReduce Combiner MapReduce Partitioner MapReduce Distributed Cache Hands Exercises Hive and PIG Fundamentals When to Use PIG and HIVE Concepts Hands on Exercises HBASE CAP Theorem  Introducti on to NOSQL Hbase Architecture and concepts Programming and Hands on Exercises Case Studies Discussions Certification Guidance Fee- INR+100 INR Registration Duration-45 hours(weekdays & weekends) Online training available on request Get trained on latest technology by ZEN industry expert. Technology: Java-SCJP/OCJP/Hibernate/Struts/Sprint etc.NET- VB.Net/C#/ASP.net/ MVC frarmrework etc Big data/Hadoop etc ETL/ data Stage/SQL Oracle// DBA/App / forms & reports etc C,C++ PHP/HTML5/javascript/JQuery/AJAX/AngularJS etc For other course like software testing, java, PHP, web technologies, Mobile application using Android/iOS and phone GAP please visit our office or contact us on given number Zeuristech Enterprise Networks Pvt. Ltd.,-ZEN 2nd Floor, Saikar Complex,Beside Ginger Hotel Bhumkar Chowk -pune- Land Mark- ICICI/AXIS Bank ATM Building

Hadoop Develoment Course Contents: Big Data Concepts: 1.What is Big Data 2.How it differs from traditional data 3.Characteristics of big data Hadoop: 1.Overview 2.Components of Hadoop 3.Performance and scalability 4.Hadoop in the context of other data stores 5.Differences between noSQL and Hadoop Unix: 1.installation 2.Basic commands 3.Users and groups Hadoop installations: 1.Standalone mode 2.Pseudo distributed mode HDFS: 1.Architecture 2.Configuring HDFS 3.Blocks, name node and data nodes 4.Job tracker and task tracker 5.Hadoop fs command line interace 6.Basic file system operations & file system api 7.Executing default map reduce examples in hadoop MapReduce: 1.What is MapReduse and how it works 2.Configuring eclipse with Hadoop 3.Mapper, reducer and driver 4.Serialization 5.Custom writable implementation examples in java 6.Input formats 7.Output formats 8.Counters 9.Writing custom counters in java 10.Streaming 11.Sorting (partial, total and secondary) 12.Joins (map side and reduce side joins) 13.No reducer programs 14.Programs in map reduce Hive 1.What is Hive 2.How is data organizes in Hive 3.Data units 4.Data types 5.Operators and functions 6.Creation of tables and partitions 7.Loading the data into HDFS 8.Partition based query 9.Joins 10.Aggregations 11.Multi Tables file inserts 12.Arrays, maps, union all 13.Altering and dropping tables 14.Custom map reduce scripts HBase 1.Zookeeper 2.Data organization in hbase 3.Creating, altering, dropping, inserting the data 4.Joins, Aggregations 5.Custom map reduce scripts 6.Integration of HBase, hive and Hadoop Hadoop Adminstration: The following are common from the above course BigData concepts, Hadoop, unix, Hadoop installations, HDFS. Mapreduce (introduction only) Extra concepts: 1.Hadoop fully distributed mode installation 2.Execution of map reduce programs in fully distributed mode 3.Job scheduling with oozie 4.Monitoring with nagios 5.Logging with Flume 6.Data transfer from other RDBMS using Sqoop Contact us for more details ELEGANT IT SERVICES

SequelGate is one of the best training institutes for Data Science & Big Data /Data Analytics Training. We have been providing Classroom and Online Trainings and Corporate training. All our training sessions are COMPLETELY PRACTICAL. DATA SCIENCE & BIG DATA/ DATA ANALYTICS TRAINING WITH PROJECT - ONLINE TRAINING COURSE DETAILS: Real time training on latest concepts related to Big Data Technologies: Hadoop – Modules, HDFS - Hadoop Distributed File System, PIG, HIVE, HBASE, SQOOP, OOZIEE, FLUME, Kafka, Spark/Scala, R –Language, and Python.This training course is exclusively designed addressing all practical aspects of Big Data concepts and implementing the real-time aspects. Material provided during the course. Data Science & Bigdata Analytics Overview HADOOP - Frame work for Big data HDFS Understanding the Cluster Map Reduce: PIG HIVE Hbase Cassandra Scala - Language for Data Science & Bigdata Scala Introduction &Environment Setup: Scala Basic Syntax Scala Data TYPES: Scala Variables: Scala Operators: Scala Conditions Scala Loops Scala Strings: Scala Regular Expressions: Scala Functions: Scala Arrays Scala Collections Scala Classes & Objects: SPark - Frame work for Data Science & Bigdata Analytics Spark Core Spark SQL Spark Streaming Spark GraphX SPARK Mlib STATISTICS STATISTICS: Descriptive & Inferential Statistics DESCRIPTIVE STATISTICS INFERENTIAL STATICS Data quality and outlier treatment Data Visualization. Cumulative Frequency plots Data Quality checking R–Lan/Python for Data Analytics Getting Started R Python Probability Graphics Machine Learning All Sessions are Completely Practical and Realtime. Duration: 4 Months, every day for 1.5 hours and all sessions are completely practical. One Real-time Project included in the course. For free Data Science Online Demo, please visit Schedules for PRACTICAL Data Science & Big Data /Data Analytics Online Training Office: (+91) 040 65358866 Mobile: (+91) 0 9030040801 

SequelGate is one of the best training institutes for Data Science & Big Data /Data Analytics Training. We have been providing Classroom and Classroom Trainings and Corporate training. All our training sessions are COMPLETELY PRACTICAL. DATA SCIENCE & BIG DATA/ DATA ANALYTICS TRAINING WITH PROJECT - CLASSROOM TRAINING COURSE DETAILS: Real time training on latest concepts related to Big Data Technologies: Hadoop – Modules, HDFS - Hadoop Distributed File System, PIG, HIVE, HBASE, SQOOP, OOZIEE, FLUME, Kafka, Spark/Scala, R –Language, and Python.This training course is exclusively designed addressing all practical aspects of Big Data concepts and implementing the real-time aspects. Material provided during the course. Data Science & Bigdata Analytics Overview HADOOP - Frame work for Big data HDFS Understanding the Cluster Map Reduce: PIG HIVE Hbase Cassandra Scala - Language for Data Science & Bigdata Scala Introduction &Environment Setup: Scala Basic Syntax Scala Data TYPES: Scala Variables: Scala Operators: Scala Conditions Scala Loops Scala Strings: Scala Regular Expressions: Scala Functions: Scala Arrays Scala Collections Scala Classes & Objects: SPark - Frame work for Data Science & Bigdata Analytics Spark Core Spark SQL Spark Streaming Spark GraphX SPARK Mlib STATISTICS STATISTICS: Descriptive & Inferential Statistics DESCRIPTIVE STATISTICS INFERENTIAL STATICS Data quality and outlier treatment Data Visualization. Cumulative Frequency plots Data Quality checking R–Lan/Python for Data Analytics Getting Started R Python Probability Graphics Machine Learning All Sessions are Completely Practical and Realtime. Duration: 3 Months, every day for 1.5 hours and all sessions are completely practical. One Real-time Project included in the course. For free Data Science Classroom Demo, please visit Schedules for PRACTICAL Data Science & Big Data /Data Analytics Classroom Training SequelGate Training Institute  Office: (+91) 040 65358866 Mobile: (+91) 0 9030040801  Sai Anu Avenue, Street No #3, Patrika Nagar, HITEC City, Hyderabad - 81 (India).

SequelGate is one of the best training institutes for Data Science & Big Data /Data Analytics Training. We have been providing Classroom and Online Trainings and Corporate training. All our training sessions are COMPLETELY PRACTICAL. DATA SCIENCE & BIG DATA/ DATA ANALYTICS TRAINING WITH PROJECT - ONLINE TRAINING COURSE DETAILS: Real time training on latest concepts related to Big Data Technologies: Hadoop – Modules, HDFS - Hadoop Distributed File System, PIG, HIVE, HBASE, SQOOP, OOZIEE, FLUME, Kafka, Spark/Scala, R –Language, and Python.This training course is exclusively designed addressing all practical aspects of Big Data concepts and implementing the real-time aspects. Material provided during the course. Data Science & Bigdata Analytics Overview HADOOP - Frame work for Big data HDFS Understanding the Cluster Map Reduce: PIG HIVE Hbase Cassandra Scala - Language for Data Science & Bigdata Scala Introduction &Environment Setup: Scala Basic Syntax Scala Data TYPES: Scala Variables: Scala Operators: Scala Conditions Scala Loops Scala Strings: Scala Regular Expressions: Scala Functions: Scala Arrays Scala Collections Scala Classes & Objects: SPark - Frame work for Data Science & Bigdata Analytics Spark Core Spark SQL Spark Streaming Spark GraphX SPARK Mlib STATISTICS STATISTICS: Descriptive & Inferential Statistics DESCRIPTIVE STATISTICS INFERENTIAL STATICS Data quality and outlier treatment Data Visualization. Cumulative Frequency plots Data Quality checking R–Lan/Python for Data Analytics Getting Started R Python Probability Graphics Machine Learning All Sessions are Completely Practical and Realtime. Duration: 4 Months, every day for 1.5 hours and all sessions are completely practical. One Real-time Project included in the course. For free Data Science Online Demo, please visit SequelGate Training Institute  Office: (+91) 040 65358866 Mobile: (+91) 0 9030040801  Sai Anu Avenue, Street No #3, Patrika Nagar, HITEC City, Hyderabad - 81 (India).

We are providing world class training in HADOOP and we are having experienced trainers. Well equipped infrastructure with feasible cost offered in our institute. We placed more than 80% of students in top MNC‘s companies after completed their training course. BENEFITS OF PERIDOT • Flexible timings • Limited batch size • Interactive classes • Free interview preparation • Demo classes for two days at free of cost • LCD equipped classroom • Fast response • Free lab access upto 6 months • Technical support is provided for more than 1 year HADOOP Hadoop is an open-source system that permits to store and process huge information in a dispersed domain crosswise over groups of PCs utilizing straightforward programming models. SYLLABUS OF HADOOP • Big data • History of Hadoop • Technologies of big data • Ecosystems tour • Vendor comparison • Use cases of Hadoop • RDBMS vs Hadoop • Map reduce • Installation and setup of Hadoop • HDFS federation • Map reduce architecture • Apache pig • Grunt shell • Loading data • Hbase • Write pipeline • Read pipeline • Hbase commands If you want to know the detailed syllabus of Salesforce, please visit our website www.peridotsystems.in MAIL ID: papitha.v@peridotsystems.in MOBILE NO: 8056102481

HADOOP - Modules that we undertake: Introduction to Bigdata •Architecture of BD •3V Concept •Framework and Applications/Tools •Samples •What is Hadoop? •History of Hadoop •Building Blocks - Hadoop Eco - System •Who is behind Hadoop? •What Hadoop is good for and what it is not •HDFS Overview and Architecture •HDFS Installation •HDFS Use Cases •Hadoop FileSystem Shell •FileSystem Java API •Hadoop Configuration •Map/Reduce 2.0 •Pig •Hive •Map Reduce Workflows •Sqoop •HBase - The Hadoop DataBase •Big Data Analysis with R •Data Science •Application and Certification Introduction to Big Data and Hadoop •What is Big Data? •What are the challenges for processing big data? •What technologies support big data? •Distributed systems •What is Hadoop? •Why Hadoop? •History of Hadoop •Use Cases of Hadoop •Hadoop eco System •HDFS •Map Reduce •Statistics Understanding the Cluster •Typical workflow •Writing files to HDFS •Reading files from HDFS •Rack Awareness •5 daemons Best Practices for Cluster Setup •Best Practices •How to choose the right hadoop distribution •How to choose right hardware Cluster Setup •Install Pseudo cluster •Install Multi node cluster •Configuration •Setup cluster on Cloud - EC2 •Tools •Security •Benchmarking the cluster Routine Admin procedures •Metadata & Data Backups •Filesystem check (fsck) •File system Balancer •Commissioning and decommissioning nodes •Upgrading •Using DFSAdmin Monitoring the Cluster •Using the Web user interfaces •Hadoop Log files •Setting the log levels •Monitoring with Nagios Install,Configure and use •PIG •HIVE •HBASE •Flume and Sqoop •Zookeeper Contact Details Dotexe Technologies, #23, South Sivan Koil Street, Vadapalani Chennai–,Phone:+,

Introduction to Big Data and Hadoop What is Big Data? What are the challenges for processing big data? What technologies support big data? Distribution systems. What is Hadoop? Why Hadoop? History of Hadoop Use Cases of Hadoop Hadoop eco System HDFS Map Reduce Statistics Understanding the Cluster Typical workflow Writing files to HDFS Reading files from HDFS Rack Awareness 5 daemons Developing the Map Reduce Application Configuring development environment - Eclipse Writing Unit Test Running locally Running on Cluster MapReduce workflows How MapReduce Works Anatomy of a MapReduce job run Failures Job Scheduling Shuffle and Sort Task Execution MapReduce Types and Formats MapReduce Types Input Formats - Input splits & records, text input, binary input, multiple inputs & database input Output Formats - text Output, binary output, multiple outputs, lazy output and database output MapReduce Features Counters Sorting Joins - Map Side and Reduce Side Side Data Distribution MapReduce Combiner MapReduce Partitioner MapReduce Distributed Cache Hive and PIG Fundamentals When to Use PIG and HIVE Concepts HBASE CAP Theorem Hbase Architecture and concepts Programming

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