Description

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 course.

Why attend

You 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

Who should attend

IT directors, CIO’s, IT Managers, BI Managers, Business Intelligence and Data Warehousing Professionals, Data Scientists, Enterprise Architects, Data Architects.

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Outline

 
 
 
 
 
 
 
 
 
 

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.

  • Demand for data
  • Types of Big Data
  • Why Analyse Big Data?
  • Industry use cases – Popular Big Data Analytic Applications
  • What is Data Science?
  • Data Warehousing and Business Intelligence versus Big Data
  • Popular Patterns for Big Data Technologies
  • Types of Big Data Analytical Workloads
  • The Big Data Extended Analytical Ecosystem

Big Data Technology

This session looks at Big Data platforms and storage options. It also looks at tools and techniques available to data scientists, business analysts and traditional Data Warehousing and Business Intelligence professionals to analyze Big Data.

  • RDBMS and NoSQL Options
  • An Introduction to Hadoop and the Hadoop stack
  • Apache Spark Framework
  • The Big Data Hadoop Marketplace
  • The Cloud Deployment Option
  • Accessing Big Data via SQL on Hadoop
  • Analyzing Big Data – What’s in the Toolkit

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.

  • Integrated Management of the Analytical Ecosystem
  • Integrating Hadoop with your Enterprise Data Warehouse and MDM
  • Simplifying access to multiple platforms using data virtualisation
  • Multi-platform optimisation – the final frontier

Ingest, Prepare, Analyze and Govern Big Data

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? It also looks at tools and techniques available to data scientists, business analysts and traditional DW/BI professionals to analyse big data. Topics that will be covered include:

  • Data Ingestion onto Hadoop
    • Sources of Big Data
    • Challenges of Capturing Different Types of Big Data
    • Getting Data into Hadoop
    • Streaming Data Ingest
    • Change Data Capture – What’s Possible
    • ETL Jobs to Ingest Data
  • Data Preparation & Integration
    • Scaling ETL Transformations for In-Hadoop ELT Processing
    • ETL tools vs Pig vs Self-Service Data Integration / Data Quality Tools
    • Parsing Unstructured Data
    • The Impact of Data Scientist and End-User Self-Service Data Integration / Data Quality
    • Joined Up Analytical Processing From ETL to Analytical Workflows
  • Data Governance in a Big Data Environment
  • Analyzing Big Data
    • Creating Sandboxes for Data Science Projects
    • Options for Analyzing Big Data Using Hadoop
    • Supervised and Unsupervised Machine Learning
    • Cloud Machine Learning Analytics Marketplace
    • Text & Sentiment Analysis
    • Search, BI & Big Data
    • Exploratory Analytics Using Graph Analytics
    • Analysing Big Data Using Self-Service BI tools
    • Analysing Data in Motion Using Streaming Analytics

Instructor

Mike Ferguson

Mike Ferguson

Mike is Managing Director of Intelligent Business Strategies Limited.  As an analyst and consultant he specialises in business intelligence / analytics, data management, big data and enterprise architecture.  With over 35 years of IT experience, Mike has consulted for dozens of companies on business intelligence strategy, technology selection, enterprise architecture, and data management.  He has spoken at events all over the world and written numerous articles.  Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of Database Associates.  He teaches popular master classes in Big Data, Predictive and Advanced Analytics, Fast Data and Real-time Analytics, Enterprise Data Governance, Master Data Management, Data Virtualisation, Building an Enterprise Data Lake and Enterprise Architecture.

Dates

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Pricing

The fee for this one-day course is EUR 725 per person. This includes one day of instruction, lunch and morning/afternoon snacks and course materials.

We offer the following discounts.

  • 10% discount for groups of 2 or more students from the same company registering at the same time.
  • 20% discount for groups of 5 or more students from the same company registering at the same time.

Note: Groups that register at a discounted rate must retain the minimum group size or the discount will be revoked. Discounts cannot be combined.

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