India’s E-Com Leader Modernized Data Pipelines in 90 Days
Ecomm Company partnered with Muoro to modernize its data pipelines using Databricks Lakehouse, achieving faster processing, lower compute costs, and real-time analytics at scale.
Business Goals
Mission
Modernize data infrastructure to handle high-volume transactional data, automate ETL workflows, and build a scalable, real-time Lakehouse platform for analytics and reporting.
Challenge
Transaction and clickstream data arrived continuously from multiple sources. Existing Hive-based pipelines couldn’t handle upserts, deduplication, or compaction efficiently, resulting in performance bottlenecks and delayed analytics.
Need
The client needed a scalable data platform to process real-time transactions, unify deduplication and merging, migrate from Hive to Delta, and boost performance while cutting costs.
About Client
Our client is one of India’s largest fashion e-commerce platforms, offering apparel, accessories, footwear, and lifestyle products from leading brands. The company manages massive datasets generated from transactions, clickstream activity, and customer interactions. They required a modern, cloud-native data infrastructure to ensure reliability, scalability, and faster insights.
The platform was designed to:
- Stream transaction data in real time using Kafka and Databricks
- Perform ETL and deduplication automatically using Delta Lake
- Store and manage data within Azure Data Lake with ACID guarantees
- Deliver faster analytical queries via Databricks SQL and ZORDER optimization
The client had a robust data vision but needed external expertise to build this modern architecture end-to-end.
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.
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 Databricks Lakehouse
All data pipelines were migrated to Databricks, leveraging Delta Lake for ACID transactions, schema enforcement, and metadata scalability.
Automated ETL Pipelines
Muoro built parameterized automation scripts for ingestion, transformation, and optimization, integrating deduplication, compaction, and merging into a single process.
Streaming Analytics
By implementing Spark Structured Streaming with foreachBatch, Muoro enabled continuous ingestion and CDC (Change Data Capture) logic for real-time insights.
All Technologies Used
Impact & Results
We always deliver on the promises we make to our clients.
Faster Data Processing
Migrated pipelines now process and compact data in real time with exactly-once semantics.
Reduced Compute Costs
Optimized architecture reduced the number of active clusters required for compaction and transformation.
Improved Query Performance
Partitioning and Delta optimization significantly sped up query execution and analytics workloads.
Unified Batch + Streaming
The same framework now supports both historical data reprocessing and live streaming updates.
Higher Developer Productivity
Automation freed data engineers from manual monitoring, allowing them to focus on analysis and feature development.
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.
