Big Data Applications

Big Data Application Development

A large data project involves several risks. It is therefore important to follow a structured approach, centered on a business problem to solve that is clear and well defined. Our approach involves the following steps :


The application development methodology can be used for:

  • Preliminary assessment
  • System requirements (non- functional) and architecture
  • Components definition (functional & data) and architecture
  • Component design and development
  • Verification and Validation
  • Pre -production and Production

Big Data Applications

The applications or high-level use cases are numerous. They are divided in two categories: what the technology enables directly in terms of new capabilities, and then which business problems are most commonly addressed by organizations that comes from being able to manipulate and leverage large and varied sets of data.

Technology

  • Staging area for data warehouses or analytical stores, often called "data lakes"
  • Sandbox, for discovery and analysis
  • Storage of semi-structured and unstructured data
  • Allow that all data is available for analysis
  • Storage of large volumes at lower costs

Business Issues

  • Behaviour analysis
  • Targeting and micro-targeting
  • Marketing analysis
  • Cause-and-effect analysis
  • Sentiment analysis
  • Fraud detection
  • Risk Management