Big Data Online Live Class by Experts
Our Training/Internship Process
Big Data - Syllabus, Fees & Duration
MODULE 1: Introduction to Big Data and Hadoop
- Types of Digital Data
- Introduction to Big Data
- Big Data Analytics
- History of Hadoop
- Apache Hadoop
- Analysing Data with Unix tools
- Analysing Data with Hadoop
- Hadoop Streaming
- Hadoop Echo System
- IBM Big Data Strategy
- Introduction to Infosphere BigInsights
- Big Sheets
MODULE 2: HDFS (Hadoop Distributed File System)
- The Design of HDFS
- HDFS Concepts
- Command Line Interface
- Hadoop file system interfaces
- Data flow
- Data Ingest with Flume and Scoop and Hadoop archives
- Hadoop I/O: Compression
- Serialization
- Avro and File-Based Data structures
MODULE 3: Map Reduce
- Anatomy of a Map Reduce Job Run
- Failures, Job Scheduling
- Shuffle and Sort
- Task Execution
- Map Reduce Types and Formats
- Map Reduce Features
MODULE 4: Hadoop Eco System
- Pig : Introduction to PIG
- Execution Modes of Pig
- Comparison of Pig with Databases
- Grunt, Pig Latin
- User Defined Functions
- Data Processing operators
- Hive : Hive Shell
- Hive Services
- Hive Metastore
- Comparison with Traditional Databases
- HiveQL
- Tables
- Querying Data and User Defined Functions
- Hbase: HBasics
- Concepts
- Clients
- Hbase Versus RDBMS
- Big SQL: Introduction
MODULE 5: Data Analytics with R
- Machine Learning: Introduction
- Supervised Learning
- Unsupervised Learning
- Collaborative Filtering
- Big Data Analytics with BigR.
This syllabus is not final and can be customized as per needs/updates
- Since 2001, Making IT Experts by Experts
- Duration: 30 Hours (Vary as per your skill)
- Course Fees
- Individual Live Classes (1:1)
- Batch Classes @ Low Fees
- Training on Your Time, Any Where
- Access to Recorded Videos
- Practical Internship on Projects
- 100% Placement Support by our jobsHybrid.com
- Training/Internship Certificate
- Agra
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- Bangalore
- Belgaum
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- Coimbatore
- Delhi
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- Gulbarga
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- Gurgaon
- Haryana
- Hyderabad
- Idukki
- Indore
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- Kannur
- Kanpur
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- Kerala
- Kolkata
- Kollam
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- Malappuram
- Mangaluru
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- Mysuru
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- Punjab
- Salem
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- Tiruchirappalli
- Tiruppur
- Trivandrum
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- Varanasi
- Vellore
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- Visakhapatnam
- Wayanad
Course Highlights
Our Locations
Our Big Data online training course will assist you in learning Big Data and advancing your Big Data profession. The big data course in location is designed to prepare students for a future as Big Data Engineers in the industry.
Big data refers to methods for analysing, methodically extracting information from, or generally dealing with data collections that are too large or complicated for typical data-processing application software to handle.
Our Big Data certification will assist you in seizing this opportunity in your career. The advantages of Big Data include that it aids in the advancement of science and research, it enhances public health and healthcare through the availability of patient records, it aids in financial trading, and it is a single platform that can carry a limitless amount of data. As a result, smarter professional decisions are made, operations are well-organized, profits are higher, and clients are happier. You can land your dream positions like Data Engineer, Big Data Lead, Technical Program Manager, Big Data Developer, and Product Engineer by completing a Big Data certification course from location. NESTSOFT Big Data Training is designed to turn you become a certified Big Data specialist by providing you with extensive hands-on experience.
Real-life As an illustration of the event, execution, and deployment of an enormous range of new big-data solutions, Big Data applications and workflows in many fields are provided as use cases. Students who complete NESTSOFT's massive data course will be able to: Identify Big Data and its Business Implications, List the components of Hadoop and Hadoop Eco-System, Access and Process Data on Distributed File Systems, Manage Job Execution in Hadoop Environment, Develop Big Data Solutions using Hadoop Eco System, Analyze Infosphere Big Insights Big Data Recommendations, and Apply Machine Learning Techniques in R. 
