The Advantages and Disadvantages Of Real-Time Big Data Analytics

Having a lot of data pouring into your organisation is one thing, being able to store it, analyse it and visualize it in real-time is a whole different ball game. More and more organisation want to have real-time insights in order to fully understand what is going on within their organisation. What are the advantages ofReal-Time Big Data Analytics and what are the challenges and which tools can be used for real-time processing of Big Data?

The Advantages of Real-Time Big Data Analytics

The advantages of processing Big Data in real-time are many:

Errors within the organisation are known instantly. Real-time insight into errors helps companies react quickly to mitigate the effects of an operational problem. This can save the operation from falling behind or failing completely or it can save your customers from having to stop using your products.

New strategies of your competition are noticed immediately. With Real-Time Big Data Analytics you can stay one step ahead of the competition or get notified the moment your direct competitor is changing strategy or lowering its prices for example.

Service improves dramatically, which could lead to higher conversion rate and extra revenue. When organisations monitor the products that are used by its customers, it can pro-actively respond to upcoming failures. For example, cars with real-time sensors can notify before something is going wrong and let the driver know that the car needs maintenance.

Fraud can be detected the moment it happens and proper measures can be taken to limit the damage. The financial world is very attractive for criminals. With a real-time safeguard system, attempts to hack into your organisation are notified instantly. Your IT security department can take immediately appropriate action.

Cost savings: The implementation of a Real-Time Big Data Analytics tools may be expensive, it will eventually save a lot of money. There is no waiting time for business leaders and in-memory databases (useful for real-time analytics) also reduce the burden on a company’s overall IT landscape, freeing up resources previously devoted to responding to requests for reports.

Better sales insights, which could lead to additional revenue. Real-time analytics tell exactly how your sales are doing and in case an internet retailer sees that a product is doing extremely well, it can take action to prevent missing out or losing revenue.

Keep up with customer trends: Insight into competitive offerings, promotions or your customer movements provides valuable information regarding coming and going customer trends. Faster decisions can be made with real-time analytics that better suit the (current) customer.

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The Challenges of Real-Time Big Data Analytics

Of course, Real-Time Big Data Analytics is not only positive as it also offers some challenges.

It requires special computer power: The standard version of Hadoop is, at the moment, not yet suitable for real-time analysis. New tools need to be bought and used. There are however quite some tools available to do the job and Hadoop will be able to process data in real-time in the future.

Using real-time insights requires a different way of working within your organisation: if your organisation normally only receives insights once a week, which is very common in a lot of organisations, receiving these insights every second will require a different approach and way of working. Insights require action and instead of acting on a weekly basis this action is now in real-time required. This will have an affect on the culture. The objective should be to make your organisation an information-centric organisation.

Real-Time Big Data Analytics Tools

More and more tools offer the possibility of real-time processing of Big Data. As Hadoop at the moment does not offer Real-Time Big Data Analytics, other products should be used. Fortunately, there a quite some (open source) tools that do the job well.

Storm

Storm, which is now owned by Twitter, is a real-time distributed computation system. It works the same way as Hadoop provides batch processing as it uses a set of general primitives for performing real-time analyses. Storm is easy to use and it works with any programming language. It is very scalable and fault-tolerant.

Cloudera

Cloudera offers the Cloudera Enterprise RTQ tools that offers real-time, interactive analytical queries of the data stored in HBase or HDFS. It is an integral part of Cloudera Impala, an open source tool of Cloudera.

Gridgrain

GridGain is an enterprise open source grid computing made for Java. It is compatible with Hadoop DFS and it offers a substitute to Hadoop’s MapReduce. GridGain offers a distributed, in-memory, real-time and scalable data grid, which is the link between data sources and different applications.

SpaceCurve

The technology that SpaceCurve is developing can discover underlying patterns in multidimensional geodata. Geodata is different data than normal data as mobile devices create new data really fast and not in a way traditional databases are used to. They offer a Big Data platform and their tool set a new world record on February 12, 2013 regarding running complex queries with tens of gigabytes per second.

Of course there are many more different Real-Time Big Data Analytics tools available but it would be a bit too much to describe all of theses tools here. Fact is that Real-Time Big Data Analytics is a Big Data trend that will increase substantially in the coming period and will have a large impact on any organisation due to the many advantages. Real-Time Big Data Analytics is probably the ultimate usage of Big Data.

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