Facebook Flexes Its Big Data Muscles

Businesses are beginning to wake up to the need for participation in Big Data. Small businesses, especially, have a lot to gain – from more efficient operations to significant cash savings – from implementing data aggregation and analytics tools into their work flow.

But while a new wave of startups are developing ways to help SMEs in their efforts to get Big Data ready, small businesses could benefit from looking at their larger peers for best practices in data technology.

Facebook is one such company whose data storage tactics could prove worthy of emulation. Every day, reports said, Facebook processes about 2 billion photos across its network. With consumer demand growing to share more information across the Facebook platform – and demand to analyze this data for consumer behavior purposes – the company has reportedly worked to strengthen its Big Data storage capacities.

In an engineering blog post last week, Facebook revealed some new insight into the lengths it goes to make sure its data storage is efficient, its data storage software is flexible, and its hardware is robust and reliable.

Unsurprisingly, this was not an uncomplicated task. But the project could provide new ways of thinking among the Data-as-a-Service community as innovators explore how to solve businesses’ own data storage and analytics problems.

Facebook has also become a major face of the need for businesses to secure data tools in place as the world expands the Internet of Things. The social media site revealed last March its new Parse for IoT software development kit. While Facebook admitted that it is not entirely sure just how Facebook will play into the Internet of Things phenomenon, the new software will reportedly connect devices to a mobile app and transmit data between the two.

Already, lithium battery developer Roost is planning to use Parse for IoT SDK to run its cloud and data analytics operations as it creates a battery that warns individuals on their smartphones if the battery charge is low.