2, 4, 6, 8 - How Do You Accelerate?

Big Data Product Marketing Manager, IBM

“The hardest part of any journey is taking that first step.” - Unknown

No, Tony Robbins is not guest blogging today. And it’s a shame, too, because if he were, he would come up with plenty of inspiring quotes designed to help you overcome the perils of a big data journey. He’d motivate you to build the skill set to understand and work with numerous data sources, learn an abundance of programming languages and advanced analytics, and set out to build the Mount Everest of big data solutions that will take over the world – or at least solve some pretty complex problems.

But here’s a thought: what if that first step in your big data journey didn’t have to be so difficult? What if, rather than build these complicated components yourself, you could get a head start and essentially jumpstart your journey?

That’s exactly what the IBM big data accelerators are designed to do.

The accelerators are software components that accelerate development and/or implementation of specific solutions or use cases on top of the big data platform. They provide business logic, data processing, and UI/visualization – all tailored for a given use case.

What does this mean for you? You can save time and money on your big data deployments.

And these accelerators are not standalone products that you have to purchase separately. They come bundled with two of our core big data platform components, InfoSphere BigInsights and InfoSphere Streams.

A recent data sheet published by IBM highlights the numerous accelerators available in the products today, including three brand new ones.

Types of Accelerators

There are two types of accelerators:

  1. Analytic Accelerators, which address specific data types or operations with advanced analytics (e.g. Text Analytics, Geospatial, etc.)
  2. Application Accelerators, which address specific applications, such as log analysis, or industry functions like cyber security, healthcare, etc. They address use cases or specific business processes – for example, customer insights from social media – and can be industry-specific or cross-industry.

An example of an analytic accelerator is text analytics. Developed by IBM research, this sophisticated text analytics engine can identify meaning within text. With more than 100 pre-built annotators that understand textual meaning – names (e.g., what is a first name vs. a last name), addresses (what is a street, apartment), and more – IBM’s text analytics accelerator is designed for use in big data and MapReduce parallel processing environments with exceptional performance. It’s easy to see how this can be used, too. Consider the healthcare industry. The text analytics accelerator can cull unorganized patient notes, understand the context and detect meaning. This leads to more accurate records, better diagnoses and improved patient care.

IBM released three new application accelerators in the latest version of Streams and BigInsights:

  • Machine data accelerator (in BigInsights)
  • Social data accelerator (in BigInsights and Streams)
  • Telco event data accelerator (in Streams)

The machine data accelerator addresses a growing challenge: handling and processing massive amounts of machine data for better insights. Machine data sources may include anything from IT machines to sensors and meters and GPS devices.

The social data accelerator address another hot item in the big data world: ingesting and processing large volumes of social media data, which can then be used to improve things lie customer retention, customer acquisition, lead generation, brand management and campaign effectiveness.

The telco event data accelerator is designed to analyze Call Detail Records, which stream in large volumes. One telco customer using this accelerator is able to access real-time analyses of 7 billion CDRs a day, reducing data processing time from 12 hours to 1 minute and cutting hardware costs down to 1/8.

The demand for big data solutions is very real. We’re past the point of debating what it is, and how many V’s define it. Now the conversation is around “how do I get started?” And for every day spent thinking about this question as opposed to taking action and deploying big data solutions, a competitor is pulling that much further ahead. Would you rather start from scratch, or get a boost from software that’s already built on best practices?

Related Links: for an overview of the IBM big data platform

InfoSphere BigInsights - What’s New

InfoSphere Streams - What’s New

Watch the video below, "Getting Started with IBM InfoSphere BigInsights"