2017 has been a busy year for us and we’re thrilled you’re coming along for the ride! Here’s a recap of just how incredible and transformative the last 12 months have been.
We did 14 GA releases in 2017, including our newest major release–Stardog 5–in June. We consistently focused on introducing tangible, business benefits to the Stardog platform; here are a few highlights.
All the data unified means better analytics because now Machine Learning is baked into the Knowledge Graph. Training a model is just writing a query. And applying that model to all the data is another query, too. Machine Learning inside of Stardog is here and it’s awesome.
We rewrote an all-new Virtual Graph engine this year. It’s faster, easier to extend, and as a result Stardog seamlessly subsumes both ways of unifying data, federated queries and materialization, within an enterprise-class system of record database, a mix of data unification capabilities you won’t find anywhere else.
You have a frightening variety of data and it’s not always perfectly structured. Storing and processing unstructured data (e.g., PDFs, emails, images, text files, et al) for query & analysis blended seamlessly with its more structured counterpart is mission critical. Now that’s a solved problem since we released BITES in Stardog.
When the answer you’re after isn’t a simple fact, but rather a collection of facts and relationships drawn from many enterprise data silos, we’ve got you covered. Path Queries in Stardog now return nodes and edges from the graph. For example, what does the supply chain look like for this batch of troublesome parts? This kind of query is now directly expressible in Stardog’s SPARQL, making it the best platform available for answering simple and complex questions over unified enterprise data.
More developers know and are learning GraphQL than all graph query languages combined. We’ve flattened the learning curve by adding GraphQL support to Stardog, which means your team can get up and running even faster.
We’re committed to constant performance improvements. Core Stardog services are production-grade. Recent query, search, and geospatial performance gains and native memory management improvements mean better answers faster, safer, and more reliably.
Our state of the art query planner now features hints, including for reasoning, virtual graphs, and query evaluation, with more to come. SPARQL Query Hints are real and they’re incredible!
We’ve also made a number of new cloud integrations possible.
Thanks to open-source Stardog Graviton, you have a single binary executable that provides a one-click virtual appliance installation of a Stardog high-availability cluster. AWS deployment couldn’t be easier.
Support for Tableau means better visualizations of the Knowledge Graph, which means more actionable insights, better horizontal vision across the enterprise, and happier data scientists. Apart from clearing technical hurdles, we made some significant strides on the business front, to include our completing our Series A round of funding and two great new partnerships
In addition to lots of product maturity and innovation, we also closed our Series A Round. We announced a partnership with Linkurious, a graph visualization software, which will enable enterprise users to generate interactive and embeddable visualizations within their data.
And best of all we added a ton of new customers to the Stardog portfolio with notable strength in manufacturing and financial services.
If you want to know where we’re headed and what Big Problem we’re solving, check out our strong thesis about how Stardog Knowledge Graph addresses and solves the Enterprise Data Silo problem. Watch this space for a preview of what’s to come in 2018 after the New Year.
Here’s to a wonderful holiday season for you and yours.