Executive Summary:
A fitness membership platform needed business reporting without adding analytical load to its live application. Rapyder built a four-layer Amazon Redshift warehouse, fed by scheduled AWS Glue jobs, with Tableau dashboards across five KPI areas. Business and operations teams gained self-serve insight into membership activity and growth, with less reliance on engineering for routine reports.
About Customer:
Founded in 2015, the customer is a fitness membership platform offering access to more than 1,250 gyms and studios through a single subscription. Its network covers yoga, Zumba, Pilates, aerobics, kickboxing and martial arts across a major metro region, alongside personalised nutrition support.
Problem Statement:
Reporting depended on queries against the production Amazon RDS for PostgreSQL database, putting the member-facing application at risk of slowdowns. Cohort, transaction and corporate utilisation data sat in disconnected views, while routine reports needed engineering support. Business teams lacked a shared, self-serve view of engagement and network growth.
Industry:
Industries, Retail / E-commerce
Offering:
Offerings, Data Analytics
Business Challenges:
- Production reporting load: Analytics queries on Amazon RDS for PostgreSQL risked slowing the live membership application.
- Disconnected KPIs: Cohort behaviour, transactions and corporate account utilisation sat in separate views without a shared source of truth.
- Manual reporting: Tracking engagement and network growth required ad hoc reports and repeated engineering support.
- Limited self-service: Business stakeholders lacked dashboards for day-to-day network growth and corporate account activity.
- Untested scale: The new analytics environment needed validation for query performance, scalability and cost before rollout.
Solution Implemented:
- Scheduled ETL: Rapyder deployed scheduled AWS Glue jobs to extract data from Amazon RDS for PostgreSQL into a dedicated Amazon Redshift warehouse.
- Four-layer warehouse: Raw, transformed, aggregated and presentation layers organised data for analysis, separating reporting queries from the production database.
- KPI aggregate tables: Rapyder modelled Redshift aggregate tables around the customer’s core business metrics.
- Tableau dashboards: Interactive dashboards on the presentation layer covered cohort analysis, transactions, corporate utilisation, user engagement and network growth.
- Pre-rollout validation: Rapyder benchmarked query performance, scalability and cost, then validated dashboards and KPI definitions with customer stakeholders.
Technology Stack:
- Amazon Redshift
- AWS Glue
- Amazon S3
- Amazon EC2
- Amazon VPC
- AWS CloudWatch
- Tableau
Business Benefits:
- Reduced production reporting load – Analytical queries moved to Redshift rather than running directly on the live application database, reducing reporting-related performance risk.
- Consistent business KPIs – A central warehouse brought cohort, transaction and corporate utilisation metrics into a shared reporting environment.
- Self-serve reporting – Business and operations teams gained Tableau dashboards for routine reporting, reducing their dependency on engineering.
- Clearer operational insight – Leadership could review cohort, engagement and growth metrics together through dashboards refreshed by scheduled data jobs.
- Validated before rollout – Query performance, scalability and cost were benchmarked before go-live, with KPI definitions checked by customer stakeholders.
Is routine reporting putting pressure on your live application?
Give business teams self-serve insight with an AWS data warehouse.
Key Takeaways:
- Separate reporting workloads – Query the warehouse for analytics rather than adding dashboard traffic directly to the production database.
- Structure data for reuse – Four warehouse layers and KPI aggregate tables provide a consistent path from source records to dashboards.
- Validate with business users – Check KPI definitions and dashboard behaviour with stakeholders before rollout.
Frequently Asked Questions
The platform needed business reporting without adding heavy analytical queries to its live membership database. Rapyder separated reporting into Amazon Redshift, allowing Tableau dashboards to query the warehouse while the operational database continued serving the application.
The platform needed a repeatable path from operational records to business metrics. Raw, transformed, aggregated and presentation layers organised data before it reached Tableau, with KPI aggregate tables supporting consistent dashboard definitions.
The platform’s reporting depended on scheduled AWS Glue ETL jobs. Timestamp-based incremental logic picked up new or changed records, reducing manual data preparation; dashboard freshness depended on the refresh schedule rather than a real-time stream.
Business and operations teams needed a shared view of membership activity and growth. Tableau dashboards covered cohort analysis, transactions, corporate utilisation, user engagement and network growth, giving stakeholders self-serve access to these core metrics.
