What is Big Data, where does Hadoop fit in, Hadoop Distributed File System (HDFS): replications, block size, secondary name node, high availability, understanding Yarn: resource manager, node manager and the difference between 1.x and 2.x
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 and Cloudera single-node cluster
How does MapReduce work, how does Reducer work, how does Driver work, combiners, partitioners, input formats, output formats, shuffle and sort, Map Side Joins, Reduce Side Joins, MR Unit and distributed cache
Working with HDFS, writing a word count program, writing custom partitioner, MapReduce with combiner, Map Side Joins, Reduce Side Joins, unit testing MapReduce and running MapReduce in local job runner mode
What is Graph, Graph Representation, Breadth First Search Algorithm, Graph Representation of MapReduce, how to do the Graph Algorithm and examples of Graph MapReduce
Exercise 1: Exercise 2: Exercise 3:
A. Introduction to Pig
Understanding Apache Pig, its features, various uses and learning to interact with Pig
B. Deploying Pig for Data Analysis
The syntax of Pig Latin, various definitions, data sort and filter, data types, deploying Pig for ETL, data loading, schema viewing, field definitions and commonly used functions
C. Pig for Complex Data Processing
Various data types including nested and complex, processing data with Pig, grouped data iteration and practical exercises
D. Performing Multi-Data Set Operations
Data set joining, data set splitting, various methods for data set combining, set operations and hands-on exercises
E. Extending Pig
Understanding user-defined functions, performing data processing with other languages, imports and macros, using streaming and UDFs to extend Pig and practical exercises
F. Pig Jobs
Working with real data sets involving Walmart and Electronic Arts as case studies
A. Hive Introduction
Understanding Hive, traditional database comparison with Hive, Pig and Hive comparison, storing data in Hive and Hive schema, Hive interaction and various use cases of Hive
B. Hive for Relational Data Analysis
Understanding HiveQL, basic syntax, 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 Hive
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 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 querying, deploying Hive for huge volumes of data sets and large amounts of querying and deploying Hive for huge volumes of data sets and large amounts of querying
G. UDF and Query Optimization
Working extensively with user-defined queries, learning how to optimize queries and various methods to do performance tuning
A. Introduction to Impala
What is impala, how impala differs from Hive and Pig, how impala differs from relational databases and limitations and future directions using the Impala Shell
B. Choosing the Best (Hive, Pig and Impala)
C. Modeling and Managing Data with Impala and Hive
Data storage overview, creating databases and tables, loading data into tables, HCatalog and Impala metadata caching
D. Data Partitioning
Partitioning overview and partitioning in Impala and Hive
Selecting a file format, tool support for file formats, Avro schemas, using Avro with Hive and Sqoop and Avro schema evolution and compression
What is HBase, where does it fit in and what is NoSQL
Multi-node cluster setup using Amazon EC2: creating four-node cluster setup and running MapReduce jobs on cluster
How do 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 MapReduce job in ETL tool and end-to-end ETL PoC showing Big Data integration with ETL tool
Major Project, Hadoop development, Cloudera certification tips and guidance and mock interview preparation, practical development tips and techniques and certification preparation
Project 1: Working with MapReduce, Hive and Sqoop
Problem Statement: – It describes how to import MySQL data using Sqoop and querying it using Hive and describes how to run the word count MapReduce job.
Project 2: Connecting Pentaho with Hadoop Ecosystem
Problem 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 and extracting data from Hadoop cluster
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A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.
An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.