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Big Data Hadoop Analyst Training Online

Course Summary

Master Big Data Analysis using Hadoop, Pig and Hive through Intellipaat's Hadoop Analyst Certification Training


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    Course Syllabus

    Hadoop Analyst Course Content

    Introduction to Big Data & Hadoop and its Ecosystem, Map Reduce and HDFS
    What is Big Data, Where does Hadoop fit in, Hadoop Distributed File System – Replications, Block Size, Secondary Namenode, High Availability, Understanding YARN – ResourceManager, NodeManager, Difference between 1.x and 2.x
    Hadoop Installation & setup
    Hadoop 2.x Cluster Architecture , Federation and High Availability, A Typical Production Cluster setup , Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Cloudera Single node cluster
    Deep Dive in Mapreduce
    How Mapreduce Works, How Reducer works, How Driver works, Combiners, Partitioners, Input Formats, Output Formats, Shuffle and Sort, Mapside Joins, Reduce Side Joins, MRUnit, Distributed Cache
    Lab exercises :
    Working with HDFS, Writing WordCount Program, Writing custom partitioner, Mapreduce with Combiner , Map Side Join, Reduce Side Joins, Unit Testing Mapreduce, Running Mapreduce in Local Job Runner Mode
    Graph Problem Solving
    What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,Exercise 1: Exercise 2:Exercise 3:
    Detailed understanding of Pig
    A. Introduction to PigUnderstanding Apache Pig, the features, various uses and learning to interact with PigB. Deploying Pig for data analysisThe syntax of Pig Latin, the various definitions, data sort and filter, data types, deploying Pig for ETL, data loading, schema viewing, field definitions, functions commonly used.C. Pig for complex data processingVarious data types including nested and complex, processing data with Pig, grouped data iteration, practical exerciseD. Performing multi-dataset operationsData set joining, data set splitting, various methods for data set combining, set operations, hands-on exerciseE. Extending PigUnderstanding user defined functions, performing data processing with other languages, imports and macros, using streaming and UDFs to extend Pig, practical exercisesF. Pig JobsWorking with real data sets involving Walmart and Electronic Arts as case study
    Detailed understanding of Hive
    A. Hive IntroductionUnderstanding Hive, traditional database comparison with Hive, Pig and Hive comparison, storing data in Hive and Hive schema, Hive interaction and various use cases of HiveB. Hive for relational data analysisUnderstanding HiveQL, basic syntax, the various tables and databases, data types, data set joining, various built-in functions, deploying Hive queries on scripts, shell and Hue.C. Data management with HiveThe various databases, creation of databases, data formats in Hive, data modeling, Hive-managed Tables, self-managed Tables, data loading, changing databases and Tables, query simplification with Views, result storing of queries, data access control, managing data with Hive, Hive Metastore and Thrift server.D. Optimization of HiveLearning performance of query, data indexing, partitioning and bucketingE. Extending HiveDeploying user defined functions for extending HiveF. Hands on Exercises – working with large data sets and extensive queryingDeploying Hive for huge volumes of data sets and large amounts of queryingG. UDF, query optimizationWorking extensively with User Defined Queries, learning how to optimize queries, various methods to do performance tuning.
    Impala
    A. Introduction to ImpalaWhat is Impala?, How Impala Differs from Hive and Pig, How Impala Differs from Relational Databases, Limitations and Future Directions, Using the Impala ShellB. Choosing the Best (Hive, Pig, Impala)C. Modeling and Managing Data with Impala and HiveData Storage Overview, Creating Databases and Tables, Loading Data into Tables, HCatalog, Impala Metadata CachingD. Data PartitioningPartitioning Overview, Partitioning in Impala and Hive
    (AVRO) Data Formats
    Selecting a File Format, Tool Support for File Formats, Avro Schemas, Using Avro with Hive and Sqoop, Avro Schema Evolution, Compression
    Introduction to Hbase architecture
    What is Hbase, Where does it fits, What is NOSQL
    Hadoop Cluster Setup and Running Map Reduce Jobs
    Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster
    ETL Connectivity with Hadoop Ecosystem
    How ETL tools work in Big data Industry, Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool, End to End ETL PoC showing big data integration with ETL tool.
    Job and Certification
    Major Project, Hadoop Development, cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques, certification preparation.
    Hadoop Analyst Project
    Project 1 – Working with MapReduce, Hive, SqoopProblem Statement – It describes that how to import mysql data using sqoop and querying it using hive and also describes that how to run the word count mapreduce job.Project 2 – Connecting Pentaho with Hadoop Eco-systemProblem Statement – It includes:Topics: Quick Overview of ETL and BI, Configuring Pentaho to work with Hadoop Distribution, Loading data into Hadoop cluster, Transforming data into Hadoop cluster, Extracting data from Hadoop Cluster


Course Fee:
USD 126

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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