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Financial sector

MPSI - Setting up a Data Warehouse & BI ecosystem

croz team

Besides establishing the DWH/BI ecosystem, CROZ also helped Mercury in modernizing data architecture. Get in touch with our data engineering team!

Intro

CROZ has been partnering with Mercury for almost 10 years and has helped Mercury to set up a Data Warehouse / BI ecosystem and develop the reference data layer that serves as a basis for the reporting and analytics.

Intro

Case study

Mercury Processing Services International is a provider of payment solutions. MPSI’s core business area includes Authorization Services, Card Management, Transaction Processing, POS and ATM payments, and e-commerce for a number of banks and card providers.

The results of providing payments for numerous partners are various reporting requirements and a great amount of data that resides in heterogeneous Data stores, which impose development of an enterprise Data Warehouse – a collection of all organization’s data optimized for reporting. MPSI has engaged CROZ to make this possible. CROZ Data team helps transform raw data to meaningful information by integrating and consolidating data from various data sources to one centralized Data Warehouse (DWH) and creating visual representation of data – Business Intelligence (BI). This process includes source data analysis and profiling, dimensional model design, data integration (ETL – Extract, Transform, Load), and reporting (BI).

Data integration tool Informatica PowerCenter is used to perform ETL process: retrieving and integrating data from heterogeneous data sources, transforming extracted data to fit DWH structure, and loading the dimensional model. Depending on business requirements, ETL jobs are scheduled to run daily, monthly or quarterly.

Business Intelligence tool IBM Cognos is used to present data stored in DWH as valuable information in order to ensure better decision making for business users.

 

Besides establishing the DWH/BI ecosystem, CROZ also helped Mercury in modernizing data architecture through piloting Big Data technologies such as Hadoop, Spark, R, Python in different scenarios – Data Warehouse offload, batch processing optimization, data visualization, fraud prediction and others.

Technologies we used

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