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Big Data Hadoop Analyst training course helps you master Big Data Analysis using Hadoop, Pig and Hive.
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Course PreviewThis 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.
A basic knowledge in any programming language is beneficial but not necessary.
Hadoop is gaining a steady groundswell with some of the biggest companies exclusively relying on Hadoop for making sense of Big Data. This combo course will help you work on the Hadoop framework and process humungous amounts of data at top speeds so as to make sense of it in real time. There is a huge demand for professionals with the exact skills that this training course is providing. This course shall ensure that you get top salaries and a career growth.
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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
Hadoop Analyst Projects
This course is designed for clearing the Intellipaat Hadoop Analyst exam.
As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast track your career effortlessly.
At the end of this training program, there will be a quiz that perfectly reflects the type of questions asked in the certification exam and helps you score better marks.
The certification will be awarded upon the completion of assignments and the project work (after expert review) and on scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
Land Your Dream Job Like Our Alumni
Intellipaat is a leader in Big Data Hadoop online training. This Hadoop Analyst training will help you be fully proficient in becoming a master Data Analyst in order to collect, analyze and transform huge volumes of data on the Hadoop cluster setup by deploying powerful tools like SQL and other scripting languages. Upon the successful completion of the training, you will be awarded the Intellipaat Hadoop Analyst Certification.
Intellipaat offers lifetime access to videos, course materials, 24/7 support and course material upgrades to the latest version at no extra fees. For Big Data Hadoop Analyst training, you get the Intellipaat Proprietary Virtual Machine for lifetime and free cloud access for 6 months for performing training exercises. Hence, it is clearly a one-time investment.
3 technical 1:1 sessions per month will be allowed.
Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.
Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.
No, our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.