Let’s talk physics today.
In the physical world we all belong to, what is the half-life of a data tool? Some of them come and go. Some of them spark and fade away, sometimes being acquired by a more prominent vendor. Lastly, some of them make a dent in the universe, survive a donation to an open-source foundation, a few rounds of VC funding at excessively growing valuations, a recession in the economy, maybe an IPO, eventually win the hearts of data practitioners, and stay in the data pipelines for years.
And just like in the physical world, the introduction of an additional tool to a data pipeline is similar to solving the n-body problem: even if you know the current market position and momentum for every vendor, predicting where your data stack ends up in a few years is nearly impossible (not that it should prevent anyone from speculating on Twitter and LinkedIn).
Data vendors as if they are celestial bodies is indeed a good analogy. As ancient as it might sound, the geocentric model is still quite popular among vendors, with everyone convinced that the whole data world revolves around them. But who can blame? Whether you’re offering a data replication, transformation, querying, observability, visualization, or orchestration product, it’s hard to resist the idea that other tools are centered around yours and merely complement it.
Here at Cube, we’re also a bit guilty of this type of thinking, at least internally, when we often describe a semantic layer as centered between data sources and consumers. However, we use this to fuel our desire for tighter integration, better interoperability, and reduced friction between data tools. We genuinely believe that being territorial and the fixed-pie bias should stay part of the ancient times, where they belong. The future that we envision is open, collaborative, and with every vendor winning its share of an ever-growing pie. We’re making this future closer by integrating and working with anyone from early-stage vendors of AI-powered conversational interfaces to publicly traded companies, billion-dollar unicorns, and anyone in between.
So, this week, we’re happy to announce several updates that evolve and strengthen Cube’s integration and interoperability with other tools in the data stack and beyond.
On Tuesday, we are launching Data Graph as part of our vision that a semantic layer should provide means to work with the data model and reason about relationships between its entities both in human-readable and machine-readable ways. Here, we see a massive potential for semantic layer vendor collaboration and potentially an open standard to facilitate integrations with other data tools, and we’re happy to take the first step.
On Wednesday, we are presenting the Orchestration API that enables our semantic layer to interoperate with data orchestration tools and be a team player in your data workflows. For many years, Cube has supported the model in which it pulls updated data from upstream data sources. With the Orchestration API, data orchestration tools and custom automations can also push updated data to Cube, achieving the same goal of keeping it in sync with the upstream.
On Thursday, we are introducing Semantic Layer Sync to remove the gap between semantic layers and BI tools and enable instantaneous, zero-friction self-serve analytics. With Semantic Layer Sync, you can develop the data model and surface metrics from Cube to one or many BI tools in seconds while still having the benefit of never, ever breaking dashboards in a BI tool by untested changes.
On Friday, we also announce integrations with identity providers and monitoring tools many mature companies have in place. Now, your data engineering team can log into Cube Cloud via any authentication service with support for SAML 2.0 protocol and deliver logs and performance indicators to various monitoring services.
Please stay tuned for the upcoming announcements, blog posts, and details we will reveal this week. If there’s something that resonates with you, let’s discuss it. Also, don’t hesitate to support us by spreading the word on social media—you might end up with a cute Rubik’s cube.
Lastly, please join us at the closing event on Friday. We’ll host a Twitter space with Cube’s co-founders, team, partners, users, and a broader data community—needless to say, we invite you to join as well. We want to discuss semantic layers, interoperability between data tools, and all the features outlined above.
P.S. Interoperability in the AI era? If you visit Snowflake Summit later in June, please stop by our booth to hear from Josh Patterson of Patterson Consulting, a member of Cube Partner Network, about how Cube integrates with LangChain and enables data-driven AI experiences.