You should have a working knowledge of the Microsoft Windows platform. A basic knowledge of programming is helpful but not required.

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1 What is this course about? This course is an overview of Big Data tools and technologies. It establishes a strong working knowledge of the concepts, techniques, and products associated with Big Data. Attendees learn to store, manage, process and analyze massive amounts of unstructured data for competitive advantage, select and implement the correct Big Data stores and apply sophisticated analytic techniques and tools to process and analyze big data. They also leverage Hadoop to mine large data sets to inform and benefit business and technical decision making, evaluate and select appropriate vendor products as part of a Big Data implementation plan for their organization. Who will benefit from this course? Anyone seeking to exploit the benefits of Big Data technologies. The course provides an overview of how to plan and implement a Big Data solution and the various technologies that comprise Big Data. Many examples and exercises of Big Data systems are provided throughout the course. The programming examples are in Java but the primary focus is on best practices that can be applied to any supported programming language. Attendees with a technical background will gain an understanding of the inner workings of a Big Data solution and how to implement it in their workplace. Management attendees will gain an understanding of where Big Data can be used to benefit their businesses. What background do I need? You should have a working knowledge of the Microsoft Windows platform. A basic knowledge of programming is helpful but not required. I am from a non-technical background. Will I benefit from the course? Yes! The course presents both the business and technical benefits of Big Data. The technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.

2 What is Big Data? Big Data is a term used to define data sets that have the potential to rapidly grow so large that they become unmanageable. The Big Data movement includes new tools and ways of storing information that allow efficient processing and analysis for informed business decision-making. What is MapReduce? MapReduce is a parallel programming model that allows distributed processing on large data sets on a cluster of computers. MapReduce was originally implemented by Google as part of their searching and indexing of the Internet. It has since grown in popularity and is quickly being adopted by most industries. What is Hadoop? Hadoop is an open source implementation of MapReduce by the Apache group. It is a high performance distributed storage and processing system. Hadoop fills the gap in the market by effectively storing and providing computational capabilities for substantial amounts of data. There is commercial support from multiple vendors and prepackaged cloud solutions. Which Big Data products and tools does this course use? The course provides hands-on exposure to a number of Big Data products including Redis, MongoDB, Cassandra, Neo4J, Hadoop/MapReduce, Pig, Hive, RHadoop, Mahout. Other data stores are also discussed during the course. Will there be any programming in the course? While programming experience is not required to attend the course, we will discuss programming examples to enable attendees to gain practical experience working with Big Data solutions. The exercises are structured in such a way that all experience levels will be challenged. For those attendees that do not have any programming experience, the exercises include guided instructions to enable them to complete the programming exercises. Those that have programming experience are challenged with bonus activities to help showcase specific Big Data capabilities.

3 How much time is spent on each topic? Content Hours Introduction to Big Data 1.5 Storing Big Data 4.5 Processing Big Data 3.5 Tools and Techniques to Analyze Big Data 3.0 Developing a Big Data Strategy 3.0 Implementing a Big Data Solution 1.5 Times, including the workshops, are estimates; exact times may vary according to the needs of each class. How much of the course is devoted to hands-on exercises? Approximately 40 percent of the time in this course is spent doing hands-on exercises. The course incorporates computer-based exercises including: Creating an interactive Hadoop MapReduce job flow Querying Hadoop MapReduce jobs using Hive Loading unstructured data into Hadoop Distributed File System (HDFS) Simplifying Big Data processing with Pig Latin Creating custom applications to analyze data Implementing a targeted Big Data strategy

4 How much time is dedicated to Hadoop? Approximately 50 percent of the time in this course is dedicated to Hadoop. This course includes coverage of of Hadoop, the HDFS, the MapReduce algorithm and Hadoop related tools and products including Pig, Hive, RHadoop and Mahout. The datastores discussed such as Cassandra can also integrate with Hadoop. What are the advantages of using Hadoop? Hadoop is the most widely used platform on which to solve problems in processing large, complex data sets that would otherwise be intractable using conventional means. It runs on commodity clusters that can be scaled incrementally as needed for increasing compute or storage needs. Commodity clusters can be maintained inhouse or supplied from a cloud services vendor such as Amazon. The Hadoop cluster has self-healing capabilities to survive hardware failures. Hadoop can operate on many different types of data and can adapt to meet varying degrees of structure and can solve a range of problems. The Hadoop Distributed File System (HDFS) automatically provides robustness and redundancy for performance and reliability. There are many associated projects that enhance the Hadoop ecosystem and ease development such as Pig, Hive, Mahout, and etcetera. How are Hadoop programs developed? Primarily programs are written in Java although Hadoop has facilities to handle programs written in other languages like C++, Python, and.net. Programs can also be written in scripting languages like Pig. Data in HDFS can be queried using a SQL-like syntax with Hive.

5 Introduction to Big Data Defining Big Data The four dimensions of Big Data: volume, velocity, variety, veracity Introducing the Storage, MapReduce and Query Stack Delivering business benefit from Big Data 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 Selecting data sources for analysis Eliminating redundant data Establishing the role of NoSQL Overview of Big Data stores Data models: key value, graph, document, column-family Hadoop Distributed File System HBase Hive Cassandra Hypertable Amazon S3 BigTable DynamoDB

6 MongoDB Redis Riak Neo4J Selecting Big Data stores 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 Mapping data to the programming framework Connecting and extracting data from storage Transforming data for processing Subdividing data in preparation for Hadoop MapReduce Employing 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 The building blocks of Hadoop MapReduce Distinguishing Hadoop daemons Investigating the Hadoop Distributed File System

7 Selecting appropriate execution modes: local, pseudo-distributed, fully distributed Tools and Techniques to Analyze Big Data Abstracting Hadoop MapReduce jobs with Pig Communicating with Hadoop in Pig Latin Executing commands using the Grunt Shell Streamlining high-level processing Performing ad-hoc Big Data querying with Hive Persisting data in the Hive MegaStore Performing queries with HiveQL Investigating Hive file formats Creating business value from extracted data Mining data with Mahout Visualizing processed results with reporting tools Developing a Big Data Strategy Defining a Big Data strategy for your organization Establishing your Big Data needs Meeting business goals with timely data Evaluating commercial Big Data tools Managing organizational expectations Enabling analytic innovation Focusing on business importance Framing the problem Selecting the correct tools

8 Achieving timely results Statistical analysis of Big Data Leveraging RHadoop functionality Generating statistical reports with RHadoop Exploiting RHadoop visualization Making use of analytical results Implementing a Big Data Solution Selecting suitable vendors and hosting options Balancing costs against business value Keeping ahead of the curve

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