We are thrilled to announce a new technical partnership between Tower and VeloDB. This collaboration brings together the best of Python-native data orchestration and lightning-fast real-time analytics to help data teams build faster, smarter, and more reliable systems.
Who is Tower?
At Tower, we believe running data infrastructure should be intuitive and scalable. Tower provides teams with data infrastructure as a service, to build, schedule, and monitor complex data pipelines using the tools they already know and love. We handle the deep intricacies of DataOps so teams can focus entirely on building resilient, mission-critical workflows without the operational overhead. Ship product, not infrastructure.
Who is VeloDB?
VeloDB is built for absolute speed and concurrency. It is a high-performance real-time analytical and search database designed to deliver sub-second query responses across massive, rapidly changing data. Whether handling complex hybrid searches, log analytics, or customer-facing analytics, VeloDB provides the real-time serving layer that modern, data-intensive applications demand.
Tower & VeloDB: Orchestration Meet Speed
From a technical standpoint, Tower and VeloDB provide a seamless way to unify the "historical" and "real-time" tracks of your data architecture. Tower acts as the intelligent orchestration layer for both systems. On the historical side, it manages complex pipelines feeding data from platforms like Snowflake and Databricks into your Iceberg data lakehouse. On the real-time side, VeloDB acts as the high-speed analytical engine, ingesting data streams directly for sub-second performance. Crucially, the integration can go both ways. Because VeloDB natively supports Iceberg (V3) and other open formats, it can directly read and join data residing in your lakehouse.
This allows teams to consolidate their entire data serving layer, seamlessly combining ad hoc querying of historical data with real-time workloads directly within VeloDB. Meanwhile, Tower sits in the middle, orchestrating where the data lands and running critical jobs that extract fresh real-time data from VeloDB to merge back into the Iceberg lakehouse. This ensures your historical archive is always up to date without building entirely separate, brittle pipelines.
Teams would want to use us together because it eliminates the traditional compromise between robust, governed data archival and lightning-fast real-time querying. The benefits are clear: developers get a unified control plane with full visibility and governance over data movement across the entire hybrid stack, while applications and users get an ultra-fast, high-concurrency serving layer through VeloDB.
Unifying Your Serving Layer: Bridging Batch and Real-Time
Let us face it, the current DataOps and modern architecture landscape is painful to navigate. Data engineers are routinely forced to string together disjointed tools, manage brittle dependencies, and maintain entirely separate architectures for batch and streaming data. When you add AI agents and large language models to the mix, the complexity skyrockets.
Delivering a unified serving layer that combines fresh, real-time insights with massive historical context remains a monumental challenge, often leading to data inconsistencies and significant operational overhead.
This is where Tower and VeloDB introduce an elegant, modified lambda architecture that simplifies DataOps while radically boosting performance. Tower orchestrates the full lifecycle of data movement. It manages the pipelines ingesting historical data into an open, governed Iceberg lakehouse, and it also sits atop the real-time stream, directing data into VeloDB for immediate processing. Crucially, Tower then acts as the data-bridging mechanism. It runs jobs that extract the newest data from VeloDB and seamlessly merges it into the Iceberg lakehouse. This makes VeloDB the single, ultra-fast analytical engine and serving layer for all of your BI and dashboards, while Iceberg provides the massive-scale, reliable historical archive that is always in sync.
This architecture fundamentally changes how you deliver interactive analytics to your end-users. By unifying your serving layer on top of VeloDB, you are no longer limited by batch windows. Pipelines are orchestrated continuously by Tower. VeloDB serves up sub-second queries for dashboards, embedded analytics, and embedded AI agents, giving your users a real-time pulse of your entire business. You get the infinite scale and robust governance of an Iceberg-based modern data lake combined with the rapid interactivity of a world-class real-time analytical engine.
The partnership between Tower and VeloDB is all about simplifying the modern data stack while radically boosting performance. By bringing Python-native orchestration and real-time analytics together, we are helping data teams stop wrestling with infrastructure and start building the future of AI-driven data systems. We cannot wait to see what you build with it.
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