Factual to Enhance First Data’s Insightics Solution for SMBs

First Data has chosen to partner with Factual to enhance its Insightics solution for small and medium sized businesses. Insightics is an innovative cloud-based software that unlocks the power of big data behind payment transactions to give SMB merchants the ability to monitor key business metrics and better understand their customers. First Data already evaluates a vast amount of data, but needed more in depth data for additional attributes and intelligence. This collaboration goes beyond transactional processing and produces advanced business and consumer insights that help merchants to grow their businesses.

It’s a big deal for small businesses to get actionable insights about their business. How many new versus repeat customers they have been getting the past few weeks, is an important thing to know. SMBs may also want to know which part of the local neighborhood customers come from and how far are customers willing to travel to come to their store. If the SMB is having a good or bad month, is it just that SMB or all similar businesses in the area? Delivering such capabilities requires all the horsepower of state of the art big data analytics presented in an extremely easy to understand manner to the merchant. That’s what First Data Insightics does.

How Factual Uses Persistent Storage For Its Real-Time Services

As part of Factual’s Geopulse product suite, we need to be able to absorb and process large amounts of data, and deliver back a somewhat smaller amount of data. There is a significant amount of technology available for the processing stage, but fewer for both the intake and delivery. Today, we’re open sourcing two libraries that we’ve used for for these purposes, s3-journal and riffle. Both of these libraries are notable for making efficient use of persistent storage by avoiding random writes, which will be discussed in more detail later in this post.

Using Clojure To Generate Java To Reimplement Clojure

Most data structures are designed to hold arbitrary amounts of data. When we talk about their complexity in time and space, we use big O notation, which is only concerned with performance characteristics as n grows arbitrarily large. Understanding how to cast an O(n) problem as O(log n) or even O(1) is certainly valuable, and necessary for much of the work we do at Factual. And yet, most instances of data structures used in non-numerical software are very small. Most lists are tuples of a few entries, and most maps are a few keys representing different facets of related data. These may be elements in a much larger collection, but this still means that the majority of operations we perform are on small instances.