Big data and Hadoop Training Brochure
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The document outlines a big data analytics training program offered by MCAL, emphasizing the growing demand for big data expertise in business decision-making. It highlights the course structure, which includes hands-on sessions with tools like Hadoop and practical case studies, aimed at managers, professionals, and graduates. Additionally, MCAL is recognized as a leading training provider in South Asia, with strong affiliations to global bodies and a history of successfully advancing the careers of over 5000 professionals.
Big data and Hadoop Training Brochure
- 1. TM MCAL A Div. of MindMap IT Solution (P) Ltd. Big Data Analytics For Business
- 2. Big Data Analytics – Why? • Data is now generated by more sources and at ever increasing rates • Examples include Social Media sites, GPS based tracking systems, point of sale equipment, etc. • The ability to process such data can provide that essential edge required for business success • Demand for Big Data professionals is rapidly increasing • Knowledge of Big Data can provide an advantage leading to faster professional advancement With increasing complexities of business decisions, CxOs are seeking active tools and dashboards that will help them in planning, forecasting, and budgeting for rapid business growth. Big Data analytics has already become an important tool in their arsenal to achieve these objectives. MCAL’s Big Data Analytics for Business program helps you launch yourself with excellent exposure to Big Data techniques and tools TM Why MCAL ? TM MCAL (Management Consulting & Advanced Learnings) is the leading high-end consulting and training company in South Asia having expertise in the areas like Business Analysis, Management Consulting, Project Management , Sales, Finance, Cloud Computing, Six Sigma, Android, Big Data, Data Analytic IT Service Management / ITIL framework. We are serving most of the fortune 500 companies present in India. In the last 5 years, we helped more than 5000 professionals have shaped their career with accelerated growth. Our exceptional track record and innovative approach make us one of most liked Training and Consulting Partner for our clients, from Individuals to MNCs. We are associated and approved from by the Global Bodies like IIBA -Canada, PMI -US, TUV -Germany, IREB ® Germany and ITIL -UK, this is a unique achievement for any Indian Company. ® ® ® ®
- 3. About this course: This course on Big Data Analytics for Business is a combination of essential fundamentals, practical techniques, hands-on sessions on Hadoop, and case studies to cement all this together. By completing this course: Understand fundamentals of analytics Know what‘Big Data’ analytics is all about Get a grip on Big Data applications Identify problem areas that can be tackled by Big Data analytics Effectively Use Big Data tools like Hadoop Choose the most appropriate tools to solve Big Data problems Propose and lead Big Data related projects in your organizations Who Should Attend: = Managers and Executives who wish to harness the power of business analytics to sharpen their decision making process. = Experienced professionals who wish to get well rounded insights into business analytics and its applications. = Graduates who wish to build & pursue career in business analytics Course Variants = For Executives (CXOs and Managers): One day intensive course = For practitioners and students: Five day in depth course Note: Customized corporate programs can also be scheduled as per requirements Course Delivery Method: Classroom Contact Program. In-depth variant will consist of: = Theory sessions = Hands-on sessions = Case Study and Analysis sessions COURSE COVERAGE: Data Processing 4 Data processing over the years 4 New data generation agents 4of relational database systems Inadequacy 4 Nature of queries - Earlier planned, now dynamic 4 queries that need to be answered now Examples of 4 Solutions - New database trends (Columnar databases) 4 Solutions - New data processing methods (Map Reduce) 4 Technology companies Case studies: 4 Other companies Case studies: Modern Database Concepts: 4 Relational databases and their limitations 4 the problem of flexible schema - Key/Value method of data storage How to solve 4of Key / value method of storage & limitations Advantages 4 provide schema flexibility? How does this 4 support efficient data storage? How does this 4 support distributed data storage? How does this 4 provide fault tolerance? How does it 4 ensure speed of query? How does it 4 How is it capable of answering conventional database queries? 4 some columnar databases: MongoDB, Dynamo, Hbase Overview of 4 Who uses these databases? 4 Store now, process later philosophy; and how to go about it? Introduction to MapReduce: 4 What is MapReduce and the need for it. 4 Real Life Examples of Map Reduce in action 4 MapReduce technique Evolution of 4 of some frequent tasks into MapReduce Decomposition 4 How MapReduce can be used to perform database tasks 4 Inputs and Outputs; files and programs Map Reduce: 4Reduce implemented in real life? Architecture of MapReduce system. How is Map 4 to Hadoop and overview Introduction Hadoop Hands-on: 4 to AWS Introduction 4 to EMR: Inputs and outputs Introduction 4 wordcount example Running the 4 Analysis of inputs and outputs Hadoop: 4 into Hadoop architecture Getting deeper 4 HDFS 4 Stages of Hadoop Mapreduce 4 Hadoop interfaces 4 Hadoop Ecosystem - An introduction 4 6-Hadoop installation and Configuration 4 Cygwin 4 SSH Hadoop Distributions: 4 Hadoop distributables - and introduction 4 Extraction 4 Directory walk-through 4 Documentation walk-through 4 of important files / configuration elements Identification Hadoop Installation: 4 Hadoop installation: Step-by-step 4 Hadoop Configuration 4 Hadoop Administration 4 Hadoop: Programmer's view Hadoop Ecosystem: 4 Hive 4 Hbase 4 Pig 4 Sqoop 4 Avro Business Analytics: 4 to Analysis and Analytics Introduction 4 Business Statistics 4 Predictive / Prescriptive Statistics Descriptive / 4 Big Data Analytics 4 Trends in Analytics Application: 4 Project – Introduction 4 Solving the problems using MapReduce / Hadoop: An example 4 Project presentation 4 Problem identification and discussion 4 Solution + presentation preparation 4 + discussion Presentation