Validate Lakehouse Savings with a 2 Week Dremio Pilot
For IT and analytics leaders, the promise of lakehouse cost savings is appealing—but real decisions require real proof. That’s why SIT, Dremio’s trusted partner in Israel, offers a hands-on, two-to-three week pilot that uses your actual dashboards and data to compare BigQuery spend and performance to a governed Dremio data lakehouse—all with minimal disruption to your existing operations.
What Does the Pilot Deliver?
The pilot is designed to answer four questions:
- Will you really spend less on compute and storage?
- Does performance (dashboard refresh, ad hoc queries) actually improve?
- Can you enforce consistent governance, lineage, and access policies?
- And will your analysts keep their SQL workflows and BI integrations, or will the change create new headaches?
How the Pilot Works
We begin by selecting a focused, representative scope—typically two or three dashboards that drive heavy warehouse usage and one or two data flows that generate frequent derived tables or ETL jobs. You don’t need to migrate everything; we want to show impact, not add risk.
In the first few days, SIT lands your curated datasets in open formats like Apache Iceberg, connects your cloud and database sources to Dremio, and wires up your BI tools to governed views. We establish role-based and row/column-level access, so you maintain full control and auditing from the start. This is also when we baseline your current BigQuery costs, refresh times, and ETL schedules, creating a clear “before” snapshot.
The next stage is all about acceleration and optimization. SIT configures Dremio reflections to speed up your heaviest dashboards, adjusts engine sizing to match your business hours, and isolates batch or ad hoc workloads as needed. Throughout, we monitor Dremio DCU consumption in real time, comparing it directly to your warehouse slot or credit usage. If analysts want to explore data further, they can pull filtered subsets locally (for example, using DuckDB), then promote useful logic back into governed Dremio views.
By the end of the second week, you’ll see your “after” results—lower compute hours, fewer required ETL jobs, and faster dashboard refreshes. We don’t just measure the technical KPIs; we verify that access policies, lineage, and auditing are working as intended, and that your BI tools and SQL queries run without retraining or disruption.
If anything falls short—whether refresh times, cost targets, or access control—we iterate, optimizing reflections, tweaking engine schedules, or even moving a workload back to BigQuery if that’s truly the better fit. The goal is to ensure you see tangible, low-risk results, not just a theoretical model.
What Happens Next?
At the end of the pilot, SIT delivers a decision-ready package:
- A side-by-side cost and performance comparison (BigQuery vs. Dremio DCUs and storage),
- A blueprint for a governed data lakehouse tailored to your actual workloads and business cycles,
- A practical rollout roadmap, so you can adopt the lakehouse at your own pace without breaking existing workflows or budgets.
We’ll answer your team’s common questions along the way: No, you don’t have to move all your data at once. No, you don’t have to rewrite dashboards or retrain analysts. Cold start times for engines are handled by warming up before refresh windows. And the warehouse stays in play for workloads that benefit from it—this is not an all-or-nothing migration.
How SIT Guides Your Pilot
Throughout the process, SIT handles the details—setup, optimization, measurement, and reporting. Week one is all about setup and baselining; week two focuses on acceleration and validation; and if needed, week three is for fine-tuning and final analysis. You’ll have a clear, defensible result, ready to present to stakeholders and leadership.
Ready to Prove the Savings?
If you want to see real-world evidence—not just promises—about how a governed Dremio lakehouse can reduce your analytics costs and complexity, let’s schedule your pilot. SIT is ready to guide your team every step of the way.
