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Data/Analytics

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last update : 19/07/2013

Big Data: Multi-Platform Analytics (Hadoop, NoSQL, Graph, Analytical Database)

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OVERVIEW

 

 

 

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.

 

AUDIENCE

IT directors, CIO’s, IT Managers, BI Managers, data warehousing professionals, data scientists, enterprise architects, data architects

 

LEARNING OBJECTIVES

Attendees to this seminar will learn:

  • 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 analyse un-modelled, multi-structured data using Hadoop, MapReduce & Spark
  • 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 organise Big Data projects including skills
  • How to make use of Big Data to deliver business value

 

 

 

MODULE 1: AN INTRODUCTION TO BIG DATA 

 

This session defines big data and looks at business reasons for wanting to make use of this new area of technology. It looks at Big Data use cases and what the difference is between traditional BI and Data Warehousing versus Big Data

  • What is Big Data?
  • Types of big data
  • Why analyse Big Data?
  • The need to analyse new more complex data sources
  • Industry use cases  - Popular big data analytic applications
  • What is Data Science?
  • Data Warehousing and BI Versus Big Data
  • Popular patterns for Big Data technologies

 

 

 

MODULE 2: AN INTRODUCTION TO BIG DATA ANALYTICS

 

This session looks at Big Data Analytical workloads, the technology components involved and how you can integrate these with existing DW/BI systems in a new architecture for end-to-end analytics and to enrich business insight. It also looks at how to preserve existing investment in data management and BI tools across DW and Big Data platforms

  • Types of Big Data analytical workloads
  • Streaming data analytics at high velocity
  • Exploratory analysis of multi-structured data
  • Complex analysis of structured data
  • Graph analytics
  • Challenges when managing and analysing big data
  • Key components in a Big Data Analytics environment
  • Preserving existing BI/DW investments
  • The Big Data Extended Analytical Ecosystem

 

 

 

MODULE 3: 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 - Hadoop NoSQL and analytical  RDBMSs, NewSQL DBMSs
  • NoSQL DBMSs
  • Key Value stores, Document DBMSs, Column Family DBMSs and Graph databases
  • An introduction to Hadoop and the Hadoop Stack
  • HDFS, MapReduce, Pig & Hive
  • Hadoop 2.0 Spark Framework
  • SQL on Hadoop options
  • The Big Data Marketplace
  • Hadoop distributions – Cloudera, Hortonworks, MapR, IBM BigInsights, Microsoft HD Insight, PivotalHD
  • Big Data Appliances – Oracle Big Data Appliance, IBM PureData System for Hadoop, HP HAVeN, Teradata Aster Discovery Server,
  • NoSQL databases, e.g. Datastax, Neo4J, Yarcdata, MongoDB, Riak
  • Analytical databases and DW appliances, e.g. Teradata, Exasol, IBM PureData, Oracle Exadata, SAP HANA, Kognitio, Actian ParAccel
  • Analytical appliances – SAS LASR, MicroStrategy PRIME
  • The Cloud deployment option – Microsoft Windows Azure, IBM, Amazon Elastic MapReduce, Altiscale Data Cloud
  • Creating a multi-platform analytical ecosystem  

 

 

 

MODULE 4: 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, and multi-structured content
  • Supplying consistent data to multiple analytical platforms
  • Loading Big Data – what’s different about loading HDFS, Hive & NoSQL Vs analytical relational databases
  • Change data capture – what’s possible
  • Data warehouse offload
  • Tools for ELT processing on Hadoop – The Enterprise Data Refinery
  • ETL tools Vs Pig Vs self-service DI/DQ
  • Dealing with data quality in a Big Data environment
  • Parsing unstructured data
  • Governing data in a Data Science environment
  • Joined up analytical processing from ETL to analytical workflows
  • The impact of data scientist and end user self-service DQ/DI – Paxata, Trifacta, MS Excel, MicroStrategy
  • Mapping discovered data of value into your DW and business vocabulary
  • Big data audit, protection and security – Dataguise, IBM Guardium, Protegrity

 

 

 

 

MODULE 5: TOOLS AND TECHNIQUES FOR ANALYSING BIG DATA

 

This session looks at tools and techniques available to data scientists, business analysts and traditional DW/BI professionals to analyse big data. It looks how different types of developers and users can exploit Big Data platforms such as Hadoop and NoSQL databases using programming techniques, text analytics, search, 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 virtualisation.

  • Data Science projects
  • Creating Sandboxes for Data Science projects
  • Options for analysing unstructured content – Text analytics, c ustom MapReduce code and MapReduce developer tools
  • Using R as an analytical language for Big Data
  • Text analysis and visualisation, Sentiment analysis and visualisation
  • Clickstream analysis and visualisation
  • Analysing big data using MapReduce BI Tools and applications for Hadoop, e.g. Datameer, Karmasphere, Platfora, IBM Customer Insight
  • Exploratory graph analysis and visualisations
  • Using search to analyse multi-structured data
  • Creating search indexes on multi-structured data
  • Building dashboards and reports on top of search engine indexed content
  • The integration of search with traditional BI platforms
  • Guided analysis using multi-faceted search
  • The marketplace: Apache Solr, Attivio, Cloudera Search, Connexica, DataRPM, HP IDOL, IBI WebFocus Magnify, IBM Watson Explorer, LucidWorks, Microsoft, Oracle Endeca Quid, Splunk
  • Analysing Big Data using Self-Service BI Tools, e.g. Tableau, QlikView, Spotfire, SAS Visual Analytics, MicroStrategy, SAP Lumira
  • SQL connectivity initiatives to Big Data – e.g. Impala, Hive, Stinger, Shark on Spark, HawQ, IBM BigSQL, CitusDB JethroData, Splice Machine
  • Big data analytics – query performance enablers
  • Managing stream computing in a Big Data environment
  • Tools and techniques for streaming analytics

 

 

 

MODULE 6: 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 in an end-to-end architecture to maximise business value from Big Data

  • Integrating Big Data platforms with traditional DW/BI environments – what’s involved
  • Integrating stream processing with Hadoop and Analytical DW Appliances
  • Integrating Hadoop with DW Appliances and Enterprise Data Warehouses
  • Tying together front end tools
  • Options for implementing multi-platform analytics
  • Cross-platform analytical workflows
  • The role of Data Virtualisation in a Big Data environment
  • Multi-platform optimisation

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