India's Fashion E-Commerce Giant
Muoro rebuilt a top-3 Indian fashion e-commerce platform's entire data foundation on Databricks Lakehouse in 90 days, migrating eight years of history with zero data loss.
Business Outcomes
Business Outcomes
90 days
Full migration timeline on schedule
100M+
Daily events processed at scale
2014
Historical data fully migrated, zero lossAbout Client
The client is one of India's top-three fashion e-commerce platforms, processing over 100 million events daily across product, inventory, and customer activity. The business has scaled rapidly on a legacy Hive-based data stack that struggled to keep pace with growth, making a modern, governed data foundation essential to supporting real-time recommendations and future AI workloads.Despite a mature data team, the client faced several technical obstacles in achieving a unified, high-performance data platform.Small-file compaction was inflating compute costs across the legacy Hive environment.Complex upsert and deduplication logic failed to hold up at scale.No schema enforcement or time travel meant data integrity couldn't be guaranteed.Eight years of ORC-format historical data needed migration to Delta without loss.Retail
The Challenge
Despite a mature data team, the client faced several technical obstacles in achieving a unified, high-performance data platform.
The Challenge
Despite a mature data team, the client faced several technical obstacles in achieving a unified, high-performance data platform.
Small File & Compaction Issues
The team struggled with numerous small files, leading to increased compute costs and longer ETL times.Complex Upsert Transactions
Incoming transactional data arrived in an upsert format that required real-time deduplication and merging without data loss.Legacy Hive Performance
Existing Hive-based pipelines were slow, inflexible, and lacked features like schema enforcement and time travel.Inefficient Data Migration
The migration from ORC to Delta format required handling massive historical data loads from 2014 onward with minimal downtime.Complex Upsert Transactions
Incoming transactional data arrived in an upsert format that required real-time deduplication and merging without data loss.Legacy Hive Performance
Existing Hive-based pipelines were slow, inflexible, and lacked features like schema enforcement and time travel.Inefficient Data Migration
The migration from ORC to Delta format required handling massive historical data loads from 2014 onward with minimal downtime.Small File & Compaction Issues
The team struggled with numerous small files, leading to increased compute costs and longer ETL times.Complex Upsert Transactions
Incoming transactional data arrived in an upsert format that required real-time deduplication and merging without data loss.Legacy Hive Performance
Existing Hive-based pipelines were slow, inflexible, and lacked features like schema enforcement and time travel.Inefficient Data Migration
The migration from ORC to Delta format required handling massive historical data loads from 2014 onward with minimal downtime.Small File & Compaction Issues
The team struggled with numerous small files, leading to increased compute costs and longer ETL times.Complex Upsert Transactions
Incoming transactional data arrived in an upsert format that required real-time deduplication and merging without data loss.Legacy Hive Performance
Existing Hive-based pipelines were slow, inflexible, and lacked features like schema enforcement and time travel.The Muoro Solution
Muoro provided a data engineering team to rebuild data processing system using Databricks Lakehouse architecture, ensuring scalable, automated, and optimized pipelines.
The Muoro Solution
Muoro provided a data engineering team to rebuild data processing system using Databricks Lakehouse architecture, ensuring scalable, automated, and optimized pipelines.
Unified Lakehouse
A unified Databricks Lakehouse with Delta Lake ACID guarantees replaced the fragmented Hive setup.Streaming CDC
Spark Structured Streaming with foreachBatch CDC enabled real-time, exactly-once processing.Parameterised ETL
A parameterised ETL pipeline handled ingest, dedupe, compact, and merge as one governed flow.Apache Spark (Scala)
|Databricks
|Delta Lake
|Azure ADLS
|Kafka
|Hive Metastore
Impact & Results
We always deliver on the promises we make to our clients.
Impact & Results
We always deliver on the promises we make to our clients.
Exactly-Once Processing
Real-time processing now runs with exactly-once semantics across the platform.Lower Compute Cost
Active cluster count was reduced, cutting compute cost materially.Unified Platform
Batch and streaming workloads now run on one governed platform.Engineering Time Freed
Engineers were freed from manual monitoring permanently.Exactly-Once Processing
Real-time processing now runs with exactly-once semantics across the platform.Lower Compute Cost
Active cluster count was reduced, cutting compute cost materially.Unified Platform
Batch and streaming workloads now run on one governed platform.Engineering Time Freed
Engineers were freed from manual monitoring permanently.Exactly-Once Processing
Real-time processing now runs with exactly-once semantics across the platform.Lower Compute Cost
Active cluster count was reduced, cutting compute cost materially.Unified Platform
Batch and streaming workloads now run on one governed platform.Engineering Time Freed
Engineers were freed from manual monitoring permanently.Final Outcome
Muoro built a scalable Databricks Lakehouse platform for Myntra that unified streaming, batch, and transactional workloads. The new system powers faster analytics, lower compute costs, and greater data reliability, enabling Myntra’s data teams to deliver insights at scale.
Final Outcome
Muoro built a scalable Databricks Lakehouse platform for Myntra that unified streaming, batch, and transactional workloads. The new system powers faster analytics, lower compute costs, and greater data reliability, enabling Myntra’s data teams to deliver insights at scale.
Need to Modernize Your Data Infrastructure?
If your organization handles large-scale transactional or clickstream data and needs robust ETL pipelines or Lakehouse migration, Muoro’s data engineers can help you build real-time, scalable systems that drive analytics and business growth. Let’s talk.
Need to Modernize Your Data Infrastructure?
If your organization handles large-scale transactional or clickstream data and needs robust ETL pipelines or Lakehouse migration, Muoro’s data engineers can help you build real-time, scalable systems that drive analytics and business growth. Let’s talk.
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