Success Story
Enabled Data Migration from Legacy to Cloud for a Wholesaler of Building Products
About the client
A wholesale distributor of building products
Challenges
- Consolidating data from diverse sources due to varying formats, schemas.
- Optimizing costs while balancing performance demands.
- Enable self-service analytics for users to analyze data with minimal IT or engineering dependence.
- Gaining a deep understanding of data to identify and implement data monetization opportunities.
- Enabling seamless collaboration among data engineers, scientists, and analysts.
Approach
- Implemented data compression techniques to minimize storage costs.
Utilized serverless computing for cost-effective processing. - Leveraged auto-scaling capabilities to match compute resources with workload demands.
- Optimized data transfer costs by reducing unnecessary data movements.
- Implemented data lifecycle management policies to tier storage based on usage patterns.
- Implemented cost monitoring and alerting systems to identify and address cost inefficiencies in real-time.
Solutions
Innover leveraged its data and analytics prowess to craft a holistic solution, seamlessly merging internal systems with external data, fostering team collaboration, empowering self-service analytics, and unlocking data monetization opportunities.
1.Data Modelling: Unified data model encompassing various entities like products, pricing, inventory, and orders to streamline data ingestion into the cloud. Leveraged Talend for seamless data ingestion.
2.Data Foundation: Comprehensive data lake and warehouse infrastructure using Azure services, including an aggregate layer to facilitate reporting and analytics consumption.
3.Data Cataloging: Unity Catalog to meticulously track all data elements across the organization, ensuring efficient data management and governance.
4.Data Processing: Databricks platform to drive advanced analytics use cases, complemented by PowerBI for comprehensive reporting capabilities.
Impact Delivered
Azure Data Warehouse became a Single Source
of Truth and reduced data redundancy
Multiple analytical use cases realized
such as Demand Planning, Price elasticity