Solving the OLAP + OLTP Workload Problem: How Merch Jar Achieves Sub-Second Analytics on Transactional Data

Learn how Merch Jar powers complex, multi-temporal analytics at scale — and why Firebolt’s architecture on AWS is redefining what’s possible for real-time workloads.

Modern analytics teams face a brutal choice: analytical speed or operational flexibility. When every query competes between new data and updates, most technologies break — either slowing down, producing inconsistent results, or exploding in cost.

In this live webinar, we’ll walk through how Merch Jar, an Amazon ads optimization platform, overcame this “oil and water” problem using Firebolt. You’ll learn:

• Why multi-temporal queries (e.g., comparing 30-day vs lifetime performance) cripple most databases
• How Firebolt’s first-class upserts, vectorized execution, and aggressive pruning deliver sub-second responses even under mixed workloads
• The architectural innovations that reduce CPU cycles and costs — enabling predictable linear scaling
• How these same principles apply across AdTech, MarTech, Gaming, and FinTech

Join us to see why sub-second queries aren’t a luxury, they’re the key to unlocking analytical momentum and real-time decision-making.