The operational sequence for moving a live batch or streaming pipeline off Databricks compute and onto Haevek's Falcon Compute Platform, without an outage.
Deciding to move production compute off the JVM is the easy part. The hard part is the sequencing: how you get a live, business-critical pipeline from "running on Databricks" to "running on Falcon" without an outage, without a correctness regression, and without a rollback story you have to invent under pressure.
This is the playbook. It assumes you've already worked out why production compute is where your Databricks bill actually lives — if you haven't, start with Slash Your Databricks Compute Bill by over 80 Percent Without Migrating Anything.
You need a specific workload, not a general intention. Run the system.billing.usage query in the Prioritizing Workloads section of this article against a 30-day window, sort descending by total DBUs, and pull your top candidates. Three categories are typically worth targeting: long-running batch ETL, 24/7 streaming ingestion pipelines that never scale to zero, and high-throughput analytics or AI/ML inference jobs.
One thing does not change at any point in this sequence: your data stays where it is. Falcon reads from and writes to your existing Delta Lake or Iceberg tables, and Unity Catalog remains the governance layer.
There is no data migration, no catalog re-registration, and no change to access policies or table contracts. What you are changing is the engine, not the lakehouse.
Cutting over a live pipeline to a new compute layer without downtime requires sequencing. The patterns below lay out that sequence from first validation to full production.
Shadow Mode
Run Falcon alongside your existing pipeline without routing any production traffic to it. Both systems process the same input so you can compare outputs, catch semantic differences early, and benchmark actual runtime and cost against your baseline before anything shifts.
Full Cutover
Once shadow mode confirms output parity and performance targets are met, route all production traffic to Falcon and decommission the old pipeline. Keep the rollback path live for a period of time as a precaution.
Optional — Canary and Parallel Validation
For API-driven workloads where incremental traffic shifting is possible, route a small slice of production traffic (5–10%) to Falcon while the rest continues through your existing stack. Monitor output consistency, latency, and error rates, then increase Falcon's share incrementally until you're confident to cut over fully. This step is less applicable to batch job workloads and can be skipped where shadow mode results are sufficient.
The Falcon Test Flight compresses this entire sequence into a structured 2–4 week engagement. Security and compliance reviews run from week one, so production deployment and cutover can follow immediately once the engagement closes.
Pull the top three results from your system.billing.usage query and pick the highest-spend batch or streaming job that isn't a single point of failure for a customer-facing pipeline. You want a meaningful savings signal and a real benchmark, not maximum risk on your first run.
Haevek installs Falcon into your EKS, AKS, or GKE environment in your own cloud account or your variant of kubernetes in your on-premise environment. No new cloud accounts, no new storage layers, and no changes to Unity Catalog. The deployment uses narrowly scoped permissions: Falcon needs access to read your source data and write to a designated output path. Implementation across two pipelines has run in as few as 7 days to get into production.
Both the Databricks job and the Falcon job consume the same source data and write to separate output paths. At the end of each run, compare output parity: schema, row counts, and any business-logic assertions your test suite already covers. Collect infrastructure metrics via Prometheus and Grafana in parallel. Run in parallel long enough to give you enough samples to smooth out throughput variance and establish a reliable compute-cost comparison.
Once output parity is confirmed and benchmark results are in, deprecate the Databricks job for that workload. Keep Databricks running for interactive SQL, notebook development, and Unity Catalog governance — those workloads stay where they belong. Then return to your system.billing.usage shortlist and repeat the process for the next workload.
The compounding loop
Customers who've done this report expanding their Falcon footprint 4x within 90 days.
Each successful migration unlocks the budget for the next and increases long term cost savings. The first production customer recovered their full annual license cost from TCO savings within 73 days of cutover.
The measurement framework is the same one used during the Test Flight benchmark, and it should stay in place through cutover:
Output correctness is the gate that decides whether you advance. Runtime and cost tell you whether the move was worth making; parity tells you whether you're allowed to make it. Do not collapse the two.
If you haven't run a benchmark yet, the Falcon Test Flight runs up to three of your production workloads side-by-side against your live Databricks jobs, in your environment, on your actual data, using your own infrastructure metrics. Success criteria are defined upfront and tailored to your specific workloads.
Get started
Email info@haevek.com with the subject line "Test Flight."
Keep reading
Slash Your Databricks Compute Bill by over 80 Percent Without Migrating Anything
Why 70–80% of your bill sits in production compute, and what a 93% infrastructure reduction actually looks like.
Replacing Databricks Compute with Falcon: Frequently Asked Questions
Unity Catalog, Photon, Kubernetes support, benchmark access scope, and what stays on Databricks.