Course Demo Video
Course Overview
- What is BigData
- Hadoop Overview
- Introduction to HDFS
- HDFS Architecture
- MapReduce v1
- MapReduce v2/YARN
- HBase
- Hive
- Pig
- Flume
- Sqoop
This course brings together several key information technologies used in manipulating, storing, and analyzing big data. We look at the basic tools for statistical analysis, R, and key methods used in machine learning. We review MapReduce techniques for parallel processing and Hadoop, an open source framework that allow us to cheaply and efficiently implement MapReduce on Internet scale problems. We touch on related tools that provide SQL-like access to unstructured data: Pig and Hive. We analyze so-called NoSQL storage solutions exemplified by HBase for their critical features: speed of reads and writes, data consistency, and ability to scale to extreme volumes. We examine memory resident databases and streaming technologies which allow analysis of data in real time. We work with the public cloud as unlimited resource for big data analytics. Students gain the ability to design highly scalable systems that can accept, store, and analyze large volumes of unstructured data in batch mode and/or real time
Training Features
With Mycareercube Online’s e-learning system, certification made simpler! You can take your career to next level.Our e-learning system is proven as the best elearning system available in the market and we gaurantee to make you a certified practicener.Mycareercube courses includes
- Expert Instructor-Led Training
We use only the industry's finest instructors in the IT industry.Learn from our instructor and interact live at your desired place via virtual learning programs scheduled to run at specific times. -
Online Exam Mockup Test
Mycareercube prepares you for live exam by attemping the online mocks. We test you in different ways; first, our learning tool, gives you feedback as to why an answer is correct or incorrect. Next, you’ll receive the questions presented in a randomized, timed format very similar to the live exam. Every time you take the exam, new questions appear. At the end of the test you will be shown, in percentage, what areas of the curriculum you are strong on as well as what areas you’re weak on. - Navigation and Controls
We provide self-paced training programs are designed in a modular fashion to allow you the flexibility to work with expert level instruction anytime. All courses are arranged in defined sections with navigation controls allowing you to control the pace of your training. Decide when you want to learn at your own pace. 24 x 7. Audio-Video Courses for self-paced evaluation based learning.
Course Outline
Introduction to Big Data
- Defining Big Data
- Delivering business benefit from Big Data
- The four dimensions of Big Data: volume, velocity, variety, veracity
- Introducing the Storage, MapReduce and Query Stack
- Establishing the business importance of Big Data
- Addressing the challenge of extracting useful data
- Integrating Big Data with traditional data
Storing Big Data
- Analyzing your data characteristics
- Overview of Big Data stores
- Selecting Big Data stores
- Selecting data sources for analysis
- Eliminating redundant data
- Establishing the role of NoSQL
- Data models: key value, graph, document, column-family
- Hadoop Distributed File System
- HBase
- Hive
- Cassandra
- Hypertable
- Amazon S3
- BigTable
- DynamoDB
- MongoDB
- Redis
- Riak
- Neo4J
- Choosing the correct data stores based on your data characteristics
- Moving code to data
- Implementing polyglot data store solutions
- Aligning business goals to the appropriate data store
Processing Big Data
- Integrating disparate data stores
- Employing Hadoop MapReduce
- The building blocks of Hadoop MapReduce
- Handling streaming data
- Tools and Techniques to Analyze Big Data
- Abstracting Hadoop MapReduce jobs with Pig
- Performing ad hoc Big Data querying with Hive
- Creating business value from extracted data
- Mapping data to the programming framework
- Connecting and extracting data from storage
- Transforming data for processing
- Subdividing data in preparation for Hadoop MapReduce
- Creating the components of Hadoop MapReduce jobs
- Distributing data processing across server farms
- Executing Hadoop MapReduce jobs
- Monitoring the progress of job flows
- Distinguishing Hadoop daemons
- Investigating the Hadoop Distributed File System
- Selecting appropriate execution modes: local, pseudo-distributed and fully distributed
- Comparing real-time processing models
- Leveraging Storm to extract live events
- Lightning-fast processing with Spark and Shark
- Communicating with Hadoop in Pig Latin
- Executing commands using the Grunt Shell
- Streamlining high-level processing
- Persisting data in the Hive MegaStore
- Performing queries with HiveQL
- Investigating Hive file formats
- Mining data with Mahout
- Visualizing processed results with reporting tools
- Querying in real time with Impala
Developing a Big Data Strategy
- Defining a Big Data strategy for your organization
- Enabling analytic innovation
- Implementing a Big Data Solution
- Establishing your Big Data needs
- Meeting business goals with timely data
- Evaluating commercial Big Data tools
- Managing organizational expectations
- Focusing on business importance
- Framing the problem
- Selecting the correct tools
- Achieving timely results
- Selecting suitable vendors and hosting options
- Balancing costs against business value
- Keeping ahead of the curve
Pricing*
USD
499.00
TRAINING BY:
Bhavyam Infotech as a Global IT Consulting firm, with a presence in more than 15 countries & headquartered in Singapore, providing End to End Business integration services including Intensive Training on Project Management.
Bhavyam Infotech Services Pte. Ltd.