Big Data 101: Data agility


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

Data agility
Data 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).

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.

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.

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Published by

Mario Meir-Huber

I work as Big Data Architect for Microsoft. With this role, I support my customers in applying Big Data technologies - mainly Hadoop/Spark - for their use-cases. I also teach this topic at various universities and frequently speak at various Conferences. In 2010 I wrote a book about Cloud Computing, which is often used at German & Austrian Universities. In my home country (Austria) I am part of several organisations on Big Data.

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