Agility is an important factor to Big Data Applications. Agile data needs to fulfill 3 different agility factors which are: model agility, operational agility and programming ability. (Rys, 2011)

Data agility

Data agility

Agile data: model agility

Model agility means how easy it is to change the Data Model. Traditionally, in SQL Systems it is rather hard to change a schema. Other Systems such as non-relational Databases allow easy change to the Database. If we look at Key/Value Storages such as DynamoDB (Amazon Web Services, 2013), the change to a Model is very easy. Databases in fast changing systems such as Social Media Applications, Online Shops and other require model agility. Updates to such systems occur frequently, often weekly to daily (Paul, 2012).

Operational agility

In distributed environments, it is often necessary to change operational aspects of a System. New Servers get added often, also with different aspects such as Operating System and Hardware. Database systems should stay tolerant to operational changes, as this is a crucial factor to growth.

Programming agility

Database Systems should support the software developers. This is when programming agility comes into play. Programming agility describes the approach that the Database and all associated SDK’s should easy the live of a developer that is working with the Database itself. Furthermore, it should also support fast development.

I hope you enjoyed the first part of this tutorial about transformable and filterable data. This tutorial is part of the Big Data Tutorial. Make sure to read the entire tutorials.

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