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his course will enable an Analyst to work on Big Data and Hadoop which takes into consideration the burgeoning demands of the industry to process and analyze data at high speeds. This Training Course will give you the right skills to deploy various tools and techniques to be a Hadoop Analyst working with Big Data.
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
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:
A. Introduction to Pig
Understanding Apache pig, learning to talk with features, different uses and pigs
B. Deploying Pig for data analysis
Pig Latin syntax, various definitions, data sort and filter, data type, pig deployment for ETL, data loading, schema viewing, field definitions, commonly used functions
C. Pig for complex data processing
Various data types including nests and complexes, processing data with pig, grouped data repetition, practical exercises
D. Performing multi-dataset operations
Joining the Data Set, Data Set Partition, Different Methods for Combining Data Set, Set Operations, Handheld Practice
E. Extending Pig
Understanding user-defined functions, streaming to increase pig and using UDF to do data processing with other languages, import and macros, practical exercises
F. Pig Jobs
A. Hive Introduction
Understanding hive, traditional database comparison with hive, pig and hive comparison, data collection in hive and hive schema, different use cases of hive interaction and hive
B. Hive for relational data analysis
HiveQL, basic syntax, deploying various tables and databases, data types, data sets, understanding various underlying tasks, deploying hive queries on scripts, shell and hue.
C. Data management with Hive
Various databases, creation of databases, data formats in the hive, data modeling, hive-managed tables, self-managed tables, data loading, changing database and tables, query simplification with views, storage results of queries,data access control, managing data with Hive, Hive Metastore and Thrift server.
D. Optimization of Hive
Learning performance of query, data indexing, partitioning and bucketing
E. Extending Hive
Deploying user defined functions for extending Hive
F. Hands on Exercises – Working with large data sets and extensive inquiries
Deploying hive for large amounts of data sets and large amounts of inquiries
G. UDF, query optimization
Working extensively with User Defined Queries, learning how to optimize queries, various methods to do performance tuning.
A. Introduction to Impala
What is Impala?, How Impala Differs from Hive and Pig, How are the relation databases, boundaries and impala distorted from future directions using Impla Shell
B. Choosing the Best (Hive, Pig, Impala)
C. Modeling and Managing Data with Impala and Hive
Data Storage Overview, Creating Database and Tables, Loading Tables, Hctel, Impla Metadata Caching Data
D. Data Partitioning
Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster
How ETL tools work in the Big Data Industry, connect with HDFS with ETL equipment and taking data from HDFS to local system, taking data from DBMS to HDFS, working with it Hive with ETL Tool, Creating Map Reduce job in ETL tool, End to End ETL PoC showing big data integration with ETL tool.
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