Big Data Analytics: From Strategy to Implementation

Learning Objectives:

• What Big Data is
• How Big Data creates several new types of analytical workload
• Big Data technology platforms beyond the data warehouse
• Big Data analytical techniques and front-end tools
• How to analyze un-modeled, multi-structured data using Hadoop and MapReduce
• How to integrate Big Data with traditional data warehouses and BI systems
• How to clearly understand business use cases for different Big Data technologies
• How to set up and organize Big Data projects
• How to make use of Big Data to deliver business value

Module 1: Introduction to Big Data

This new two day workshop is aimed at getting Data Scientists, Data Warehousing and BI professionals up to scratch on Big Data, Hadoop, other NoSQL DBMSs and Multi-Platform Analytics. What is Big Data? How can you make use of it? How does it fit within a traditional analytical environment? What skills do you need to develop for Big Data Analytics? All of these questions are addressed in this new knowledge packed workshop.

• What is Big Data?
• Types of Big Data
• Why analyze Big Data?
• What is Data Science?
• Data Warehousing and BI Versus Big Data
• Popular patterns for Big Data technologies

Module 2: Introduction to Big Data Analytics

This session looks at Big Data Analytics, the tools and techniques involved in it and how you can integrate this new environment into an existing DW/BI environment to enrich business insight. It also looks at how to get more value out of existing data management tools and BI tools across DW and Big Data platforms

• Traditional data warehousing and BI in the enterprise
• The need to analyze new more complex data sources
• Types of Big Data analytical workloads
• Streaming data at high velocity
• Structured data analysis
• Multi-structured data analysis
• Challenges when managing and analyzing big data
• Key components in a Big Data Analytics environment
• The Big Data Extended Analytical Ecosystem

Module 3: Big Data Integration And Governance in a Multi-Platform Analytical Environment

This session will look at the challenge of integrating and governing Big Data and the unique issues it raises. How do you deal with very large data volumes and different varieties of data? How does loading data into Hadoop differ from loading data into analytical relational databases? What about NoSQL databases? How should low-latency data be handled? Topics that will be covered include:

• Types of Big Data
• Connecting to Big Data sources, e.g. web logs, clickstream, sensor data, unstructured and semi-structured content
• The role of information management in an extended analytical environment
• Supplying consistent data to multiple analytical platforms
• Best practices for integrating and governing multi-structured and structured Big data
• Change data capture – what’s possible
• Dealing with data quality in a Big Data environment
• Big Data transformation and integration
• Loading Big Data – what’s different about loading Hadoop files versus NoSQL and analytical relational databases
• Governing data in a Data Science environment
• Joined up analytical processing from ETL to analytical workflows
• Mapping discovered data of value into your DW and business vocabulary

Module 4: Big Data Platforms and Storage Options

This session looks at platforms and data storage options for big data analytics

• The new multi-platform Analytical Ecosystem
• Beyond the Data Warehouse - Analytical databases, Hadoop and NoSQL DBMSs
• Analytical databases and DW appliances
• An introduction to Hadoop and the Hadoop Stack
• What is Hive?
• What are Graph databases
• Cassandra as a Big Data Platform
• The Big Data Marketplace
  • Data Warehouse Appliances
  • Hadoop distributions – Cloudera, HortonWorks, DataStax, MapR
  • Big Data Appliances – Oracle Big Data Appliance, IBM BigInsights, Microsoft PDW and HD Insight, EMC GreenPlum DCA & PivotalHD
  • NoSQL databases, e.g. Neo4J, Yarcdata, MongoDB
• Creating a multi-platform analytical ecosystem
• The role of Data Virtualization in a Big Data environment
• Multi-platform optimization – the new trend in Big Data Analytics

Module 5: Tools and Techniques for Analyzing Big Data

This session looks at tools and techniques available to data scientists, business analysts and traditional DW/BI professionals. It looks how different types of developers and users can exploit Big Data platforms such as Hadoop and NoSQL databases using programming techniques, self-service BI tools as well as how vendors are making it easier to gain access both the NoSQL/Hadoop world and the Analytical RDBMS world by using data virtualization.

• Data Science projects
• Creating Sandboxes for Data Science projects
• MapReduce developers Versus SQL developers
• MapReduce developer tools What is R?
• Using R as an analytical language for Big Data
• Managing stream computing in a Big Data environment
• Tools and techniques for streaming analytics
• Using Data virtualization to simplify access Big Data and traditional DW/BI systems
• SQL connectivity initiatives to Big Data – e.g. Impala, Hive
• Speeding up Hive with Stinger
• Analyzing Big Data using Self-Service BI Tools, e.g. Tableau, QlikView, Spotfire MicroStrategy, SAP BO,
• NoSQL BI Tools and applications for Hadoop, e.g. Datameer, Karmasphere, Platfora, IBM Customer Insight
• Big data analytics – query performance enablers
• Data visualization and in-memory data in a big data environment

Module 6: Search, BI & Big Data

This session will examine the growing role of search in an analytical environment both as an information consumer tool for self-service BI and as a way of analyzing both structured and unstructured data. Search has been incorporated into BI tools for some time, but with the emergence of Big Data as a platform for analyzing unstructured information, it is taking on a major new role. Search is a simple mechanism that is familiar to most people, and opening up the interactive use of BI via search can have enormous business benefits. Search can be used to grow the use of BI to a much wider group of users and also provide a way to extract additional insight from unstructured content. Topics that will be covered include:

• Why Search and BI?
• The growing importance of analyzing unstructured content
• The implications of Big Data on search and BI
• Creating search indexes on multi-structured data
• Building dashboards and reports on top of search engine indexed content
• Using search to analyze multi-structured data
• The integration of search with traditional BI platforms
• Using Search to find BI content and metrics
• Guided analysis using multi-faceted search
• The marketplace: Attivio, ExaLead, Connexica, HP Autonomy IDOL, IBI WebFocus Magnify, IBM Vivisimo, Lucene, Microsoft Fast, Oracle Endeca Quid, SAP BusinessObjects

Module 7: Integrating Big Data Analytics into the Enterprise

This session looks at how new Big Data platforms can be integrated with traditional Data Warehouses and Data Marts. It looks at stream processing, Hadoop, NoSQL databases, Data Warehouse appliances and shows how to put them together to maximize business value from Big Data Analytics.

• Integrating Big Data platforms with traditional DW/BI environments – what’s involved
• Integrating event processing with Hadoop and Analytical DW Appliances
• Integrating Hadoop with DW Appliances and Enterprise Data Warehouses
• Tying together front end tools
• Multi-platform Analytics