{"id":7126,"date":"2025-07-21T09:36:01","date_gmt":"2025-07-21T09:36:01","guid":{"rendered":"https:\/\/staging.elixirrdigital.com\/us\/case-studies\/taco-bell-2\/"},"modified":"2026-03-25T11:06:11","modified_gmt":"2026-03-25T11:06:11","slug":"taco-bell-2","status":"publish","type":"case_study","link":"https:\/\/staging.elixirrdigital.com\/us\/case-studies\/taco-bell-2\/","title":{"rendered":"Cutting Taco Bell\u2019s data costs by $25K a month with 60% faster reporting"},"content":{"rendered":"\n
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\n Back to Case Studies<\/a> <\/div>\n

\n Case Study<\/p>

\n Taco Bell<\/h1> <\/div>\n
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Optimizing Redshift workload and reporting performance for financial dashboards.<\/p>\n <\/div>\n <\/div>\n<\/div>\n

\n \"Outside <\/div>\n <\/div>\n <\/section>\n\n\n \n
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\n About the brand<\/h2> <\/div>\n
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Taco Bell is a large enterprise in the Quick Service Restaurant (QSR) industry that originated in the US and operates globally. Known for its fast-paced operations and high transaction volumes, the business depends on real-time data to keep things running smoothly.<\/p>\n

The challenge<\/h2>\n

In the QSR world, decisions are made daily (sometimes hourly) based on live data. Sales, revenue, labor costs, it all needs to be reported quickly and clearly.<\/p>\n

Taco Bell was using Amazon Redshift and Yellowfin BI to power the dashboards their managers and finance teams depended on. But as customer volumes grew and reporting demands increased, the system started to struggle. Dashboards slowed down during busy periods, creating blind spots just when teams needed clarity most. What’s more, costs kept climbing.<\/p>\n

The solution<\/h2>\n

We tackled the problem on two fronts: query performance and system architecture.<\/p>\n

First, we streamlined the underlying SQL powering the client\u2019s most critical dashboards. By replacing nested views and cross-joins with precomputed materialized tables, we significantly reduced the CPU and I\/O load.<\/p>\n

Then we redesigned the Redshift architecture. A parallel cluster setup was introduced \u2013 three independent Redshift clusters, each with its own Yellowfin connection. Reports were routed based on type and complexity, ensuring better workload distribution.<\/p>\n

Using report usage data, we fine-tuned the balance of traffic across connections. This segmentation reduced congestion and took full advantage of AWS\u2019s concurrency scaling policy, ultimately unlocking three additional free concurrency hours each day.<\/p>\n

The results<\/h2>\n

The changes delivered exactly what Taco Bell needed: faster, more reliable dashboards and a leaner, more cost-effective data environment.<\/p>\n