Muoro secures a $3.2M grant from Brownfield to expand Global Capability Centers and Centres of Excellence in tier-II cities, North India.•Value Engineering Partner for AI, Data & Modernization•Engineered, Operated and owned within explicit controlled boundaries•
Muoro secures a $3.2M grant from Brownfield to expand Global Capability Centers and Centres of Excellence in tier-II cities, North India.•Value Engineering Partner for AI, Data & Modernization•Engineered, Operated and owned within explicit controlled boundaries•
Muoro secures a $3.2M grant from Brownfield to expand Global Capability Centers and Centres of Excellence in tier-II cities, North India.•Value Engineering Partner for AI, Data & Modernization•Engineered, Operated and owned within explicit controlled boundaries•
Global Sportswear Brand ETL Re-Engineering
Muoro embedded a 3-person data engineering POD within 72 hours for a global sportswear brand, cutting pipeline failures by over 85% and reporting time by most of a day.
Business Outcomes
Business Outcomes
15 → <2
Monthly pipeline failures reduced
<1 hr
Reporting time reduced from 8–12 hours
~60%
Transformation time reduced72 hrs
POD ramp-up reduced from 4–6 weeksAbout Client
The client is a world-leading athletic and sportswear brand running supply chain and retail data engineering across ERP, CRM, e-commerce, and point-of-sale systems. Disparate legacy ETL processes and a lack of standardisation left the team reactive rather than proactive in managing its data operations.Retail
The Challenge
Pipeline failures ran at 15 per month with no centralised monitoring.
The Challenge
Pipeline failures ran at 15 per month with no centralised monitoring.
Slow Reporting
Reporting cycles took 8 to 12 hours with no standardisation.Long Ramp-Up
New engineers took 4 to 6 weeks to become productive.Inconsistent Ingestion
Ingestion from ERP, CRM, eComm, and POS was inconsistent across sources.Slow Reporting
Reporting cycles took 8 to 12 hours with no standardisation.Long Ramp-Up
New engineers took 4 to 6 weeks to become productive.Inconsistent Ingestion
Ingestion from ERP, CRM, eComm, and POS was inconsistent across sources.Slow Reporting
Reporting cycles took 8 to 12 hours with no standardisation.Long Ramp-Up
New engineers took 4 to 6 weeks to become productive.Inconsistent Ingestion
Ingestion from ERP, CRM, eComm, and POS was inconsistent across sources.Slow Reporting
Reporting cycles took 8 to 12 hours with no standardisation.Long Ramp-Up
New engineers took 4 to 6 weeks to become productive.Inconsistent Ingestion
Ingestion from ERP, CRM, eComm, and POS was inconsistent across sources.The Muoro Solution
A 3-person POD covering ETL, PySpark, and DataOps went live in 72 hours.
The Muoro Solution
A 3-person POD covering ETL, PySpark, and DataOps went live in 72 hours.
Rapid POD Deployment
A 3-person POD covering ETL, PySpark, and DataOps went live in 72 hours.Reusable Workflows
25+ ADF workflows were rebuilt on reusable, standardised templates.Transform Tuning
PySpark tuning and delta loading were paired with Synapse for heavy queries.Azure Data Factory
|PySpark / Databricks
|Azure Synapse
|ADLS Gen2
|GitHub Actions
|Azure Monitor
Impact & Results
Pipeline failures dropped from 15 per month to under 2.
Impact & Results
Pipeline failures dropped from 15 per month to under 2.
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Near Real-Time Reporting
Reporting moved from 8–12 hours to real-time or under an hour.60% Faster Transforms
Transformation time was reduced by approximately 60 percent.Faster Onboarding
TCO fell and engineer ramp-up dropped from weeks to 72 hours.Near Real-Time Reporting
Reporting moved from 8–12 hours to real-time or under an hour.60% Faster Transforms
Transformation time was reduced by approximately 60 percent.Faster Onboarding
TCO fell and engineer ramp-up dropped from weeks to 72 hours.Near Real-Time Reporting
Reporting moved from 8–12 hours to real-time or under an hour.60% Faster Transforms
Transformation time was reduced by approximately 60 percent.Faster Onboarding
TCO fell and engineer ramp-up dropped from weeks to 72 hours.Final Outcome
Pipeline failures from 15/month to under 2. Transforms 60% faster. POD live in 72 hours.
Final Outcome
Pipeline failures from 15/month to under 2. Transforms 60% faster. POD live in 72 hours.
Make your data pipelines faster and more reliable
Re-engineer fragile ETL workflows to reduce failures, accelerate reporting, and make your data operations easier to scale.
Make your data pipelines faster and more reliable
Re-engineer fragile ETL workflows to reduce failures, accelerate reporting, and make your data operations easier to scale.
No challenge is too complex for our team to solve
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