01 · Streaming KPI inputs
PySpark streaming jobs on AWS EMR prepared raw and aggregate data for current operating measures.
QSR application case study · Quick-service restaurants
A QSR executive cockpit reduced KPI latency from 30 minutes to about one minute
A multi-brand QSR operator relied on manual spreadsheet pivots and distributed reports for daily measures. BluePi built streaming and batch data paths, Snowflake reconciliation, Java APIs, and a custom React cockpit that brought KPI visibility to approximately one-minute latency.
Near-real-time executive intelligence for multi-brand QSR operations
A multi-brand, multi-country QSR operator relied on spreadsheet pivots and reports spread across locations, with at least 30 minutes of latency and no central cockpit for daily measures. BluePi built streaming, batch, reconciliation, API, and React application paths for current KPI visibility.
02 · Reconciled warehouse state
Snowflake archival and reconciliation procedures supported consistency before measures reached the application.
03 · Executive cockpit delivery
More than 100 Java APIs and a custom React cockpit supported dashboard, administration, and operating views with approximately one-minute KPI latency.
From store data to an executive cockpit
BluePi processed raw and aggregate data through PySpark streaming on AWS EMR, handled Python batch ETL on Kubernetes, reconciled and archived data in Snowflake, served measures through Java APIs, and exposed them in a custom React cockpit.
Prepare raw and aggregate measures
Streaming and batch paths turned operating data into current KPI inputs.
Reconcile the warehouse state
Snowflake procedures supported archival and consistency before measures reached the application.
Serve one executive cockpit
Java APIs and React connected dashboard, administration, and operating views.
Where this pattern fits
This case is relevant when QSR operations and leadership teams depend on manually assembled reports and cannot access current measures during the operating day.
Before a first conversationQuestions that show whether this case fits your situation
Use these questions to judge whether the case fits your operating problem and what a first deployment should prove.
- Which business measures require near-real-time visibility?
- Which source events and batch records establish each KPI?
- Where must archival and reconciliation occur before the application serves a measure?
- Which dashboard and administration behaviors belong behind application APIs?
- What store scale, peak-load, concurrency, and latency conditions define acceptance?
A practical first step
A first engagement can connect one operating measure from source processing through reconciliation, API delivery, and a working cockpit view, then validate its latency and peak-load behavior.
Questions and answers
Direct answers drawn from the case record on this page.
What problem did the customer need to solve?
A multi-brand QSR operator relied on manual spreadsheet pivots and distributed reports for daily measures. BluePi built streaming and batch data paths, Snowflake reconciliation, Java APIs, and a custom React cockpit that brought KPI visibility to approximately one-minute latency.
What did BluePi deliver?
A multi-brand, multi-country QSR operator relied on spreadsheet pivots and reports spread across locations, with at least 30 minutes of latency and no central cockpit for daily measures. BluePi built streaming, batch, reconciliation, API, and React application paths for current KPI visibility.
What results did the project produce?
KPI visibility improved from at least 30 minutes to approximately one minute, while the application and infrastructure supported larger operating conditions. Measured results: 10,000 stores supported; 200 concurrent application users.
Which client, industry, and platform does this case involve?
Industry: Quick-service restaurants. Platform: AWS EMR, PySpark, Snowflake, Java, React, and Kubernetes.
Where should a team with a similar problem start?
A first engagement can connect one operating measure from source processing through reconciliation, API delivery, and a working cockpit view, then validate its latency and peak-load behavior.
Case details
Open a section to review the customer problem, implementation, business change, and architecture.
01Starting point
Operations and leadership teams waited at least 30 minutes to derive current insights. Reports lived in several locations, spreadsheet pivots remained manual, and no central executive cockpit connected the measures required for daily action.
02What BluePi built
BluePi connected streaming and batch data preparation, warehouse reconciliation, application APIs, administration, and one executive interface.
- PySpark streaming jobs for raw and aggregate data
- AWS EMR processing
- Snowflake archival and reconciliation procedures
- Python batch ETL on Kubernetes
- More than 100 Java dashboard and administration APIs
- Custom React executive BI cockpit
03Measured results
KPI visibility improved from at least 30 minutes to approximately one minute, while the application and infrastructure supported larger operating conditions.
- New KPIs could be developed quickly
- Supported expansion across stores, brands, and countries
- Supported scale of up to 10,000 stores
- Handled approximately five times normal load on high-demand days
- Supported 200 concurrent application users
04Planned next capabilities
Configurable KPIs and real-time KPI alerts and notifications are planned as the next capabilities.
Similar customer work
Cases that share this industry or platform, for comparison during your evaluation.
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