Aerospike, ThoughtSpot, Alteryx and AI-inspired integration

Partnerships are nothing new in the analytics world, and neither are integrations between technologies. But this week has seen a a couple of announcements that fall into the partnership/integration category and, this time, it’s all about AI. Aerospike announced interesting integrations with two popular Apache Software Foundation open source data analytics technologies and ThoughtSpot is announcing integration with Alteryx. The latter integration ties in handily with another one Alteryx announced last month. And all of these integrations are AI-relevant.

Let’s start with Aerospike, whose eponymous product is an in-memory NoSQL database that can leverage flash memory as well as RAM. The company announced on Tuesday the releases of Aerospike Connect for Spark and Aerospike Connect for Kafka, which connect to Apache Spark and Kafka, respectively. Of course, connectivity to those two open source technologies is fairly common, but there’s more to it than that.

First off, the Spark integration is pretty cool…this isn’t just an important-export bridge…it’s something that lets Spark developers query Aerospike and get the results back as a Spark DataFrame. From there, almost any Spark operation on the data is possible. On the Kafka side, meanwhile, things are nicely bidirectional — so not only can data streaming off Kafka topics come into Aerospike (that support was already there), but now data in Aerospike can stream into a Kafka topic as it changes. As explained to me by Srini Srinivasan, Aerospike’s Chief Product Officer and Founder, the combination of these integrations brings three benefits:

Aerospike also announced a new Aerospike REST Client, to be released in April, that will augment its current language-specific software developer kits (SDKs) for developer connectivity.

Moving on, ThoughtSpot is today announcing a partnership and integration with data prep/data pipeline specialist Alteryx that mashes up ThoughtSpot’s search-based analytics with Alteryx’s ability to build machine learning (ML) models. The new integration allows Alteryx users to add native ThoughtSpot Bulk Loader connections and ThoughtSpot TQL statements directly into an Alteryx workflow. As a result, a search-based query can trigger the scoring of data that’s in ThoughtSpot against an Alteryx ML model (which itself is built utilizing R or Python/scikit-learn, behind the scenes). In a call-and-response fashion, the resulting predicted value(s) will come back from Alteryx and can be visualized in ThoughtSpot, automatically.

That may sound a little Rube Goldberg and, granted, I have not had this integration demoed for me. But the ability to pipe a result set out of ThoughtSpot and into an Alteryx workflow, then get the scoring data set back in, seems reasonable.