Lucidworks Delivers Fusion 2.0 With Spark Integration
“Fusion 2.0 is continuing on this mission of making more of an organization’s data usable and crafting the user experience around data,” Hayes said. “More and more of what a satisfactory customer experience comes down to is getting to the right information. This means you can help resolve a help desk ticket faster or purchase an item faster or find an article or object they’re most interested in. And one of the ways we do that is by leveraging Apache Spark. Apache Spark, with its streaming capabilities is allowing us to process data in real time and use the results of that data processing to better weight the search results.” The company has closed some significant deals in financial services, life sciences, technology services and online services, he added. Fusion 2.0 helps people leverage their data as part of driving better user experiences, particularly for areas like e-commerce and financial services. “We help people create a data experience and drive users to the most productive data possible and apply it to things like fraud detection and customer service,” Hayes told eWEEK. Hayes said Lucidworks is looking at Spark to help customers with fraud detection. For instance, if a company us watching for fraudulent transactions and they have a stream of data that says if a customer is fulfilling a prescription in two towns that are both 50 miles away from where this person lives and they’re happening within a certain timeframe, the system should process that as being a fraudulent transaction. With Spark that processing can be done in real-time, Hayes said. The system also can, as the application is being used, surface information that says this particular patient has been flagged for suspicious activity.In addition to being available from Lucidworks, Fusion is also available in the Amazon Web Services Marketplace, making it available on the full spectrum of hosting options: on-premise, multi-tenant and cloud.
“Spark brings a number of other applications we are excited about,” Hayes said. “We are starting to get into more streaming analytics around data. So as you’re making decisions about how you want to curate a data experience within your application, there’s a lot that you can do with these real-time streams. For instance, if I’m processing social media and I’m looking for certain trends, and within those trends there are keywords that I want to use to surface information within my application, Spark gives us the ability to do what we’ve always done, which is rank these signals and understand them in terms of what’s important within the data sets that we’re serving up to our application. But now we’re doing it with a real-time capability as opposed to doing it with batch processing like we were doing it historically.”