Faster date processing
Increase in data volume
Annual cost saving
Source of truth for all data
About the Client
A technology solutions provider in the banking, commerce, and retail industries.
The solution involved migrating a legacy data platform to Azure Synapse Analytics. It resulted in efficiency, scalability, and cost efficiency of data processing with user adoption increase and data accuracy. The transfer restructured data architecture, allowing for greater innovation and growth.
Industry: Banking and Financial Service, Fintech, Commerce and Retail
Technology: Azure Data Lake, Synapse Analytics, Tableau
Service Domain: Modern Data Warehouse & BI, Cloud, Data Lake, Legacy Data Platform Migration
Background and Challenge Story
Figure 1. The high-level Architecture Design
Our customer decided to update their data architecture to overcome scalability issues and streamline operations. Their commitment to innovation and adoption of advanced analytics led them to collaborate with CMC Global team and begin the transformational migration to Azure Synapse Analytics.
The client’s legacy data platform could not handle the growing volume of data, making it challenging to gain insights from the data in a timely manner. Furthermore, the platform was not scalable, making it difficult for the client to add new users or data. The platform also had data quality issues, which made it hard to trust the data.
CMC’s Approach and Solution
Our customer decided to implement Azure Synapse Analytics, a cloud-based data management service to deal with the explosive growth of data, scale easily, and provide high-quality data. The migration to Azure Synapse Analytics enabled our clients to upgrade their data infrastructure and gain the insights needed to make better decisions.
We offered our client a transformative data management solution through the transfer effort to Azure Synapse Analytics. Significant advancements in data processing, scalability, cost-effectiveness, and data quality resulted from our complete strategy, which solved problems and unlocked new opportunities.
Figure 2. Full Data Flow for the new Enterprise Data Platform on Cloud
What our team did
Evaluated our client’s existing data infrastructure to define their specific needs and objectives.
Adjusted our solution to the customer’s specific needs, considering their budget, timeline, and expected outcome.
Collaborated closely with the client to enable a smooth movement of their data to Azure Synapse Analytics.
Implemented sophisticated data cleansing and normalization processes to improve data quality.
Cautiously configured Azure Synapse Analytics to optimize performance and scalability.
Specified resources, established secure connectivity, and ensured systems compatibility.
Extensively validated data and used efficient transfer techniques to minimize downtime and disruption during migration.
Tested, optimized, and fine-tuned queries and workflows to maximize data processing speed and efficiency.
Provided comprehensive training to the client’s teams to leverage advanced analytics capabilities.
Offered ongoing support and monitoring to ensure a smooth transition and continued success.
The Azure Synapse Analytics migration project had a significant influence on our client’s data management capabilities, delivering tangible benefits in key areas:
Improved Data Processing Speed: Achieved a 50% reduction in data processing time, allowing for real-time analytics and faster decision-making.
Improved Scalability: Scalability increased threefold, supporting over 6000 users at once and handling exponential data expansion.
Cost Savings: A $1 million annual savings resulted from eliminating hardware infrastructure and reducing maintenance and licensing costs.
Improved Data Quality and Accuracy: Automated validation and cleansing processes improved data accuracy by 95%.
High User Adoption and Satisfaction: Achieved 90% user adoption in the first month, with users praising the user-friendly interfaces and extensive analytical features.
Thanks to the transfer to Azure Synapse Analytics, our customers improved data processing speed, scalability, cost savings, data quality, and user satisfaction. This transformed their data management capabilities and empowered them to drive industry innovation and long-term growth.