DWH Framework
Data integration in Microsoft environment. No more unknown data flows, no more programmer creativity. Just metadata.
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The DWH Framework = standards + best practices in ETL
No more creativity in ETL
The DWH Framework unifies and streamlines data flows in your organization. No more creativity in coding ETL processes, no more unfamiliar data flows, no more coding handwriting.
Processes defined by metadata
The DWH Framework converts or sets up all ETL processes using predefined standardized routines defined by metadata.
Code standardization
With the Adastra DWH Framework, developers and administrators don’t write code directly, they just set the metadata and configure the process. This automatically standardizes the code.
When to use Adastra DWH Framework
To upgrade existing data warehouse built in Microsoft environment.
To develop new data warehouse in Microsoft environment.
To migrate existing data warehouse in Microsoft environment (to on-premise and/or Cloud).
For workflow and schedule for ETL execution, validation and notification.
For development and automated delivery of ETL in Azure DevOps.
For documentation, data lineage, administration and monitoring.
Metadata – the key to standardization and efficient development
All-in-one solution
You get a comprehensive tool to develop and manage ETL processes.
High reliability
Thanks to the rules you will have highquality ETL processes from the very beginning.
Rapid deployment
Within 3 months, you will have a reliable and functional solution without big effort.
High ROI
You will save more than 2000 man-days you would spend on mapping ETL processes.
Minimal costs
You will cut the time required to develop and manage a new solution.
Governance
On day 1, you will know what the general patterns and specific ETL processes should look like.
Case studies
Equa Bank clients were fully migrated to Raiffeisenbank in 12 hours
When Equa Bank was being mergedintoRaiffeisenbank in November 2022, we handled the migration of Equa Bank’s client data into Raiffeisenbank’s CRM system. We also ensured the client master data was propagated to the bank’s core systems.
ŠKODA AUTO: data transfers on analytics platform are comprehensively managed and monitored by Adastra’s Adoki
In 2018-19, Adastra built an on-premise Data Analytics Platform (DAP) at ŠKODA AUTO. Its purpose? To visualize data and use advanced analytics and artificial...
Automatic categorization for 98.5% of card transactions
With millions of clients conducting millions of operations every day, the bank needed to automatically assign a unique category to every banking transaction (card...
Just-in-time loan offers: a 10x higher conversion rate
Together with the bank, we used several years’ worth of transaction descriptions and a number of transactions of a specific type to identify a...
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