Big Data Analytics as a Service: The next big thing

IT organizations have generated, collected, and stored vast amounts of data. And now, IT is being asked to provide the infrastructure to perform analytics on this data. But this task is a resource-intensive proposition and is being solved by cloud technology in the form of Big Data-as-a-Service.

The cloud is a fantastic resource for Big Data because of its scalability, and also because it is extremely economical. As datasets grow larger, some organizations are moving their datasets to and from the cloud; and this is referred to as (BDaaS).

What is Big Data-as-a-Service?

Currently, BDaaS is a somewhat nebulous term that is often used to describe the outsourcing of various Big Data functions to the cloud. These functions can range from the supply of data to the supply of analytical tools with which to interrogate the data, to carrying out the real analysis and presenting reports. Some BDaaS providers also include consulting and advisory services within their BDaaS packages.

What types of BDaaS are available?

Cloud-based BDaaS offerings currently fall into one of the three competing types:

Core BDaaS

The Core BDaaS offering employs a minimal platform of Hadoop along with YARN and HDFS and a few other services such as Hive. This service was found useful by several companies that use it as part of a larger architecture or for irregular workloads. A prominent example of core BDaaS is that of Amazon Web Service’s Elastic Map Reduce (EMR). It integrates easily with the NoSQL store, S3 storage, DynamoDB and other services. The generic nature of Amazon’s EMR service is allowing companies to combine other services around it to build anything from company infrastructures to complete data pipelines.

Performance BDaaS

As the name implies, this service is focused on helping organizations that are already working with Hadoop, in streamlining their infrastructure and optimizing Hadoop performance. Organizations that are rapidly growing are finding themselves limited by complexity, and at the same time, they are hesitant to take on the formidable task of building their own data architecture. By outsourcing their infrastructure and platform needs to an external provider, organizations can focus on domain-specific processes, thereby adding value and eliminating many of the headaches that are associated with complex big data deployments.

Feature BDaaS

Companies that require additional features, which go beyond those offered in the common Hadoop ecosystem, find Feature BDaaS worth their consideration. Focusing on abstraction and productivity, the feature driven BDaaS approach is designed to get users up and running with big data quickly and economically.

The term 'Big Data-as-a -Service' may be rather unwieldy but the concept is rock solid. As more and more companies are realizing the worth of implementing Big Data strategies, more services are emerging to support them. Data analysis brings positive change to any organization that takes it seriously. With the growth in popularity of software as a service, companies are increasingly used to working in a virtualized environment via a web interface, and integrating big data analytics into this process is a natural next step. We can already see that BdaaS is making Big Data projects viable for many businesses that previously considered them out of reach, and hence BDaaS is rightly being considered to be the next big thing in this industry.