---
title: Accelerating Real-Time Analytics with Amazon QuickSight and SPICE
client: TargetBay
description: Configured Amazon QuickSight with SPICE in-memory engine to deliver near real-time campaign analytics, eliminating reporting lag and reducing Aurora database overhead.
url: https://www.factualminds.com/case-study/amazon-quicksight-spice/
category: Analytics
publishDate: 2025-07-22
updateDate: 2025-07-22
---

# Accelerating Real-Time Analytics with Amazon QuickSight and SPICE

> Configured Amazon QuickSight with SPICE in-memory engine to deliver near real-time campaign analytics, eliminating reporting lag and reducing Aurora database overhead.

## Challenge: Slow Ad-Hoc Reporting and Aurora Database Overload from Analytics Queries

TargetBay's BayEngage platform powers campaign delivery for over 4,500 e-commerce merchants. The platform captures billions of campaign events — opens, clicks, bounces, and conversions — within Amazon Aurora MySQL clusters. Internal teams rely on real-time insights to monitor campaign performance, detect failures, and drive product decisions. Three critical issues were undermining this capability:

- **Slow, Costly Ad-Hoc Reporting:** Direct SQL queries against large Aurora tables caused multi-minute response times, production database CPU spikes, and risk to transactional workloads. Complex analytical queries competed for resources with real-time campaign processing.
- **Fragmented Data Architecture:** Customer and event data was distributed across multiple Aurora clusters, preventing unified reporting. Teams could not easily join tenant, campaign, and engagement data across cluster boundaries for cross-cutting analysis.
- **Delayed Error Visibility:** Campaign or batch job failures were typically discovered only during next-day reporting cycles, meaning issues that could have been caught in minutes went undetected for hours, impacting merchant campaigns and eroding platform reliability.

## Solution: Deploying Amazon QuickSight with SPICE In-Memory Engine for Near Real-Time Analytics

FactualMinds deployed Amazon QuickSight with the SPICE in-memory engine to decouple analytics from production databases, providing near real-time reporting without impacting transactional workloads.

**Data Ingestion and SPICE Configuration:**

- Configured QuickSight to ingest data from each Aurora MySQL cluster independently
- Scheduled incremental SPICE refreshes every 5 to 15 minutes, ensuring dashboards reflect current state without continuous database queries
- Stored key tables — tenants, campaigns, batches, and events — in SPICE for rapid in-memory access

**Unified Cross-Cluster Reporting:**

- Joined tenant, batch, and campaign tables from multiple SPICE data sources within QuickSight
- Created unified dashboards spanning tenants and regions, providing a single-pane view that was previously impossible with direct database queries

**Live Dashboards and Monitoring:**

- Built dashboards covering campaign engagement metrics (CTR, open rate, bounce rate), batch processing success and failure rates, and top-performing tenants
- Configured threshold-based alerts and anomaly detection directly within QuickSight, enabling immediate notification when metrics deviate from expected patterns

**Self-Service Analytics:**

- Empowered business teams to build ad-hoc visualizations — funnels, heat maps, time series, and pivot tables — without requiring engineering involvement or database access

## AWS Services Used for QuickSight SPICE Analytics

- **Amazon QuickSight with SPICE** — In-memory analytics engine with scheduled incremental refresh
- **Amazon Aurora MySQL** — Source database clusters for campaign and tenant data
- **Amazon CloudWatch** — Infrastructure monitoring and alerting
- **AWS IAM** — Access control for QuickSight dashboards and data sources

## Results: Near Real-Time Analytics with 5–15 Min QuickSight SPICE Refresh Cycles

The QuickSight deployment transformed TargetBay's analytics capabilities from reactive and fragmented to near real-time and self-service:

- **Near real-time analytics with 5 to 15 minute refresh cycles** replacing multi-minute ad-hoc queries against production databases
- **Eliminated reporting lag** by surfacing campaign failures, anomalies, and performance degradation within minutes rather than the following day
- **Reduced Aurora database overhead** by offloading analytical workloads to SPICE, freeing production clusters to focus on transactional processing
- **Self-service analytics for business users** enabling product, marketing, and operations teams to explore data independently without engineering support
- **Instant anomaly detection and alerts** through threshold-based monitoring that proactively flags issues before they escalate to merchant-facing impact

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For more on [Amazon QuickSight consulting and real-time analytics on AWS](/services/amazon-q-for-quicksight/), see our analytics service page.

## Results

- **Analytics Refresh**: 5-15 Min
- **Reporting Lag**: Near-Zero (was Hours)
- **Aurora CPU Load**: Significantly Reduced
- **Self-Service Analytics**: Enabled for Business Teams

## AWS Services Used

- amazon-q-for-quicksight

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*Source: https://www.factualminds.com/case-study/amazon-quicksight-spice/*