Years of rapid growth had left a celebrated real-money gaming company’s Redshift environment sprawling: redundant data, aging clusters, and rising costs across production, analytics, and regulatory reporting. The client needed to retire the platform on a defined timeline without putting any of the workloads that depended on it at risk.

Lovelytics embedded a team of Redshift and Databricks specialists directly alongside the client’s platform group to run a phased retirement: archiving data, decommissioning environments, rightsizing compute, and migrating workloads to Databricks in a sequence that protected uptime at every step.

The client first brought Lovelytics in to stabilize and optimize its Redshift environment: tuning queries, fixing data layout issues, and getting a handle on performance problems that had built up over years of growth. That early work surfaced the bigger question underneath it. Redshift wasn’t just underperforming, it was becoming the wrong platform altogether. The real-money gaming company decided to retire it and move its data estate to Databricks.

Scaling into a full retirement program

Retiring a platform that production analytics, regulatory reporting, and real-time trading systems all depend on isn’t something you do in one push. Lovelytics and the client built a phased program instead, with four moving parts running in parallel: archiving data that still needed to be retained, decommissioning environments that were no longer in use, rightsizing the compute that remained, and migrating live workloads over to Databricks.

Much of this ran on automation Lovelytics built for the engagement. A stale-table detection pipeline flagged tables with no activity in six months, renamed them to break any lingering dependencies, and gave teams a window to speak up before anything was dropped. A companion vacuum pipeline cleaned up Delta Lake tables on a rolling basis, and one early run alone reclaimed 1.4 petabytes of storage across more than 13,000 tables.

By mid-2026, the results were compounding. Production Redshift infrastructure was downsized from 24 nodes to 11, three clusters were fully decommissioned, and the client was saving roughly $150,000 a month in infrastructure costs, with more still on the table as remaining workloads move over.

The client chose Lovelytics for a track record of running large, live-production Redshift-to-Databricks migrations without downtime, and for a willingness to embed engineers directly inside its team rather than working at arm’s length. That proximity let the team move fast on both the migration work and the automation tooling that made it scale.

Not everything went smoothly, and that’s part of the story too. The first run of the vacuum cleanup pipeline took 32 hours to complete, too slow to run on the cadence the retirement needed. The team went back in, optimized the code, and got subsequent runs down to a fraction of that time. Separately, when an AWS outage caused data loss in one of the client’s models, the backfill effort surfaced schema mismatches between the recovered files and the target tables. Rather than pushing through, the team paused, coordinated directly with the client’s data owners, and confirmed the correct source data before resuming, protecting data quality over speed.

The real-money gaming company now runs a materially smaller, lower-cost Redshift footprint while its most critical workloads run on Databricks with no loss of performance or data quality. The retirement program continues to compound savings each month as more of the legacy environment comes offline.

• $150K/month in infrastructure savings from Redshift downsizing
• Production cluster reduced 100%, from 24 to 0 nodes over the course of the engagement
• Successfully managed migration of key assets during key business volume & performance demands such as the Super Bowl and the World Cup
• 1.4+ petabytes of stale data reclaimed through automated cleanup pipelines

The client and Lovelytics are continuing the retirement into the rest of 2026, working through the remaining Redshift workloads and legacy reporting tools still in transition. We’ll update this case study with a link to the full published version once that next phase wraps.