Overview

Union Meridian Bank, a US-based bank, had 15 years of historical data spread across XLS files and legacy on-premise systems, with duplicate records, inconsistent data formats and rising storage costs making it harder to manage. The growing volume of historical data also made it difficult to maintain consistent records and access information when needed. The project focused on centralizing the data and making historical records easier to access and maintain. Bacancy Technology migrated 100 TB of data to AWS and organized it in Amazon S3 with deduplication and standardized formats. As an extension of the engagement, we implemented data validation checks and role-based access controls to support more reliable historical reporting and audits.

Technologies Used

AWS
openpyXL
Python
Pandas

Project Highlights

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Successfully transferred over 100 TB of data to the cloud.

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Improved data accessibility and reduced retrieval time by 70%.

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Reduced storage costs for Union Meridian Bank by 35%.

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Improved data quality and consistency across historical records.

Challenges & Solutions

1

Over 15 years, Union Meridian Bank accumulated terabytes of historical data in XLS files that needed to be efficiently processed, organized, and migrated to the cloud. The volume and age of the data made it important to handle the migration without losing critical historical information.

2

Client was adamant about maintaining data accuracy & consistency. But, different file formats, duplicate records, and variations in historical data made it difficult to establish a consistent dataset and determine whether records were complete and reliable.

3

The sensitive nature of banking records added another layer of complexity to the migration. Data had to move from legacy systems to the cloud without exposing confidential information, while access needed to remain restricted to authorized users throughout the process.

4

Moving years of historical records from legacy systems to the cloud required careful planning to preserve data structure and maintain its integrity. The migration also needed proper validation to confirm that records were complete and ready for future reporting and audits.

Solutions by Bacancy

1

Our data engineers built a Python and Pandas-based pipeline to extract data from the bank’s XLS files and convert it into CSV format for cloud migration. The process also standardized the data and made it easier to handle the large volume of historical records efficiently while maintaining a consistent structure.

2

We implemented data validation and cleansing checks as part of our data migration service to identify any duplicate records, inconsistent formats, and incomplete information before migration. This helped standardize the historical data and improve its consistency before moving it to the cloud.

3

We used data encryption and role-based access controls to protect sensitive banking records throughout the migration. These measures helped limit access to authorized users and maintain appropriate security controls while the data was being transferred and stored in the cloud.

4

Our AWS migration experts used AWS DMS to move the data to Amazon S3, followed by data transformation and loading into cloud data warehouses for analysis and reporting. This approach helped maintain the structure of historical records while making the data more accessible for ongoing reporting and audits.

Core Features

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Comprehensive assessment of data assets, formats, and locations.

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Identification of data quality issues and migration risks.

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Development of a tailored migration plan with ETL processes.

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Establishment of data governance policies, standards, and roles.

No. of Resource

06

No. of Resource

Time Frame

August 2023 – Ongoing

Time Frame

Project Snapshot

Union Meridian bank Dashboard

Outcomes

100 TB of historical data successfully migrated to AWS and organized in Amazon S3.

Union Meridian Bank’s 15 years of scattered historical records was brought together in a centralized cloud environment.

Duplicate records and inconsistent formats cleaned up through data standardization and deduplication.

Manual data checks were replaced with structured validation processes to improve data accuracy.

Historical records that were difficult to access became readily available for reporting and audits.

Sensitive data moved from legacy systems with role-based access controls estabilished in place.

Technical Stack

Cloud & infrastructure AWS
Data migration AWS DMS
Data storage Amazon S3
Data processing & transformation PythonPandasOpenPyXL
File format conversion XLS/XLSX to CSV
Security & access control AWS IAMRole-Based Access Control (RBAC)

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