Data Migration for a Renowned National Bank
Overview
Our client, an established bank known for its reliability and commitment to customer satisfaction, aimed to update its IT systems and make data more readily available. The bank faced issues with duplicated data, storage inefficiencies, and the complexities of handling the large amounts of data stored mainly in XLS files. Our objective was to transfer this data to cloud storage to streamline storage processes, enhance data accessibility, and maintain the accuracy of the information.
Technical Stack
Industry
Banking and Financial Services
Region
United States
Project Size
Non- Disclosable
Highlights
Successful migration of over 100TB of data
Improved data accessibility and retrieval time by 70%.
Reduced storage costs by 35%
Enhanced data quality and consistency
Challenges & Solutions
The bank had accumulated 15 years of data in .XLS format, amounting to terabytes of information.
- Solution: We developed a robust data extraction and transformation pipeline using Python and Pandas to convert XLS files to CSV format for efficient processing consistency.
The bank was adamant about ensuring data accuracy and consistency, which was crucial for the bank's operations.
- Solution: We implemented rigorous data cleaning and validation processes to identify and rectify errors, inconsistencies, and duplicates.
The bank wanted to implement strict security measures.
- Solution: We followed security measures, such as encrypting data managing access controls, and meeting requirements.
Careful preparation and precise implementation were essential for transferring and merging volumes of data into the cloud.
- Solution: We leveraged AWS DMS to efficiently migrate data to Amazon S3, followed by data transformation and loading into cloud data warehouses for analysis and reporting.
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Years of Business
Experience
Happy
Customers
Countries with
Happy Customers
Agile enabled
employees
Core Features
- Comprehensive assessment of data assets, formats, and locations.
- Identification and remediation of data inconsistencies and errors.
- Development of a tailored migration plan, including (ETL) processes.
- Establishment of data governance policies, standards, and roles.
- Implemented rigorous data cleaning and validation processes.
- Optimized data formats and storage tiers for cloud-based storage and indexing.
No. of Developers
04
Time Frame
August 2023 – February 2024
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