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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.
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