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Data Modeling in a Big Data environment

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by Christopher Bradley download a PDF brochure Download Event Brochure

Description

In the modern era, the volume of data we deal with has grown significantly. As the volume, variety, velocity and veracity of data keeps growing, the types of data generated by applications become richer than before. As a result, traditional relational databases are challenged to capture, store, search, share, analyse, and visualize data.

Many companies attempt to manage Big Data challenges using a NoSQL (“Not only SQL”) database and may employ a distributed computing system such as Hadoop. NoSQL databases are typically key-value stores that are non-relational, distributed, horizontally scalable, and schema-free. Many organisations ask, “do we still need data modelling today?”

Traditional Data Modelling focuses on resolving the complexity of relationships among schema-enabled data. However, these considerations do not apply to non-relational, schema-less databases. As a result, old ways of Data Modelling no longer apply.

This course will show Data Modelling approaches that apply to not only Relational, but also to Big Data, NoSQL, XML, and other formats. In addition, the uses of data models beyond simply development of databases will be explored.

What you will learn

  • Learn about the need for and application of Data Models in Big Data and NoSQL environments
  • See the areas where Data modelling adds value to Data Management activities beyond Relational Database design
  • Understand the critical role of Data Models in other Data Management disciplines particularly Master Data Management and Data Governance
  • Learn the Best Practices for developing Data Models for Big Data and NoSQL environment
  • Understand how to create Data Models that can be easily read by humans
  • Recognise the difference between Enterprise, Conceptual, Logical, Physical and Dimensional Data Models
  • Through practical examples, learn how to apply different Data Modelling techniques

Main Topics

  • Data Modelling recap
  • Data Modelling - Back to the Future?
  • Data Modelling for Big Data & NoSQL
  • Modelling for hierarchic systems & XML
  • Services Oriented Architecture (SOA) • Massively denormalised files
  • Dimensional Data Models
  • Application Packages & Data ModelsUsing Data Models for Data Integration & Lineage
  • Top down requirements capture
  • Bottom up requirements synthesis
  • How to capture requirements for both Data and Process needs.
  • Checking the Data vs the MetaData; why does it matter?
  • Use of standard data model constructs, and pattern models
  • Understanding the Bill of materials (BOM) construct. Where can it be applied, why it’s one of the most powerful modelling constructs
  • Party; Role; Relationship: Why mastering this construct can provide phenomenal flexibility.
  • Mastering Hierarchies: Different approaches for modelling hierarchies
  • Alternative Data Modelling Notations and tooling
  • Normalisation: Progressing beyond 3NF. 4NF, 5NF Boyce-Codd, and why, and when to use them