Big Data and Analytics

When application requirements push beyond the limitations of relational database systems, Big Data solutions come into play. Big Data refers to technologies and initiatives that involve manipulation and analysis of data that is diverse, fast-changing and massive. These technologies create, store, retrieve and analyze large volumes of diverse data at high speeds.

The Big Data ecosystem can be categorized into two classes: systems that provide operational capabilities for real-time, interactive workload; and systems that provide analytical capabilities for complex analysis.

Operational Big Data technologies such as NoSQL are faster, scale quickly and are inexpensive compared to relational databases. Most operational systems need to provide some degree of real-time intelligence about the active data in the system. NoSQL systems were designed to take advantage of cloud computing architecture and can run on commodity hardware.

Analytical Big Data workloads tend to be addressed by MPP database systems and Map Reduce. Map Reduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL. As applications gain traction and their users generate increasing volumes of data, there are a number of analytical workloads that provide real value to the business. These workloads involve algorithms that are more sophisticated than simple aggregation.

Data Preparation

Data Discovery
Data Gathering
Data Cleaning
Data Translations
Data Enrichment

Real-Time Processing

Streaming Data
Multi-Parallel Processing
Adaptive Scalability

Big Data Analytics

Reports and Visualizations
Actuarial Analytics
Data Modeling
Artificial Intelligence Algorithms

Streaming Solutions

Video on Demand
Customer Analytics
Real-Time Market Predictions
Accelerated Incident Response
Incident Detection