big data, spark

For many firms, one of the biggest challenges when they are implementing big data analytics initiatives is dealing with the vast amount of information they collect in a timely manner.

Getting quick results is essential to the success of such a project. With the most advanced users of the technology able to gain real-time insights into the goings-on within their business and in the wider market, enterprises that lack these capabilities will struggle to compete. While the most alert companies can spot potential opportunities even before they emerge, they may have already passed-by by the time a slower business’ analytics have even noticed an opportunity.

So what can companies do to ensure they are not falling behind with their big data? In many cases, the speed of their analytics is limited by the infrastructure they have in place. But there are a growing number of solutions now available that can address these issues.

Spark and more

One of the most-hyped of these technologies is Apache Spark. This is open-source software that many are touting as a potential replacement for Hadoop. Its key features are much faster data processing speeds – claimed to be up to ten times faster on disk than Hadoop map reduce, or 100 times faster for in-memory operations.

In today’s demanding environment, this speed difference could be vital. With optional features for SQL, real-time stream processing and machine learning that promise far more than what generic Hadoop is capable of, these integrated components could be the key to quickly unlocking the potential of a firm’s data.

However, it shouldn’t be assumed that Spark is the only option available for companies looking to boost their data operations. There are a range of in-memory platforms (Kognitio being one!) and open-source platforms available to help with tasks like analytics and real-time processing, such as Apache Flink. And Hadoop itself should not be neglected: tech like Spark should not be seen as a direct replacement for this until its feature set matures, as they do not perform exactly the same tasks and can – and often should – be deployed together as part of a comprehensive big data solution.

Is your big data fast enough?

It’s also important to remember that no two businesses are alike, so not every firm will benefit from the tech in the same way. When deciding if Spark or analytical platforms like it are for you, there are several factors that need to be considered.

For starters, businesses need to determine how important speedy results are to them. If they have a need for timely or real-time results – for instance as part of a dynamic pricing strategy or if they need to monitor financial transactions for fraud – then the speed provided by Spark and it’s like will be essential.

As technology such as the Internet of Things becomes more commonplace in businesses across many industries, the speed provided by Spark and others will be beneficial. If companies are having to deal with a constant stream of incoming data from sensors, they will need an ability to deal with this quickly and continuously.

Giving your big data a boost

Turning to new technologies such as Spark or Flink can really help improve the speed, flexibility and efficiency of a Hadoop deployment. One of the key reasons for this is the fact that they take full advantage of in-memory technology.

In traditional analytics tools, information is stored, read-from and written-to physical storage solutions like hard disk drives during the process – map reduce will do this many times for a given job. This is typically one of the biggest bottlenecks in the processing operation and therefore a key cause of slow, poorly-performing analytics.

However, technologies such as Spark conduct the majority of their tasks in-memory – copying the information in much faster RAM and keeping it there as much as possible, where it can be accessed instantaneously. As the cost of memory continues to fall, these powerful capabilities are now within much easier reach of many businesses and at a scale not previously thought possible.