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
- Atlanta
- Austin
- Baltimore
- Boston
- Charleston
- Charlotte
- Chicago
- Colorado Springs
- Columbus
- Dallas
- Denver
- Detroit
- Fort Worth
- Fresno
- Honolulu
- Houston
- Indianpolis
- Jacksonville
- Kansas City
- Las Vegas
- Los Angeles
- Lousville
- Memphis
- Miami
- Milwaukee
- Minneapolis
- Nashville
- New Orleans
- New York
- Oklahoma City
- Omaha
- Philadelphia
- Phoenix
- Portland
- Raleigh
- Sacramento
- San Antonio
- San Diego
- San Francisco
- San Jose
- Seattle
- Tampa
- Tucson
- Tulsa
- Virginia Beach
- Washington
- Wichita
Course Highlights
Our Locations
This on-site Big Data training will get you up and running in the most difficult professional abilities. The big data course in location is designed to prepare students for a future as Big Data Engineers in the industry. The NESTSOFT Big Data course covers a wide range of topics in big data, including data generation, storage, management, and transfer, as well as analytics, with a focus on cutting-edge technologies, tools, architectures, and systems that make up big-data computing solutions in high-performance networks. Our skilled instructors may train you during the web training.
. As a result, smarter professional decisions are made, operations are well-organized, profits are higher, and clients are happier. 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.
Big Data is the newest and most promising technology for processing large amounts of data and performing data analytics. Experienced industry specialists with more than ten years of experience cover in-depth knowledge of Big Data and Ecosystem technologies. 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. 
