Data Warehouse, ETL, Business Analytics
By blackthorn.ai
Client
Services
Project Description
Customer is a streaming platform that offers a wide variety of TV shows, movies, documentaries, and more on thousands of internet-connected devices.SolutionsTotal Cost of Ownership (TCO) was carefully considered by comparing the performance and costs of alternative services offered by a number of cloud providers. The most advantageous combination of cost, quality, and sustainability was chosen to meet customer requirements.Business requests were collected by interviewing numerous stakeholders. The requests served as a starting point for the BI dashboards, the DWH schema, and data pipelines design.The multidimensional OLAP cube (facts, dimensions, etc.) was built and extended piece-by-piece revealing one business request after another. The cloud warehouse consolidated large amounts of data from disparate on-premises sources.Convenient BI dashboards were designed and implemented to meet collected earlier business requests and their possible future variability. The end business users with no SQL skills could perform requests using straightforward BI dashboards, without DBA team involvement.Auto-scalable ETL workflows were designed and implemented as the business requests and DWH schema had been established. The pipelines extract, transform, and load data from the source databases (on-premises) into the target DWH (on the cloud).Backward historical consistency was achieved by introducing meta-tables and unique timestamps that indicated the time range when a source row or table was actual.ImpactUsing simple controls, business users can configure interactive dashboards and get required information immediately and independently, without the involvement of other departments.DBAs are now free from writing the exact same SQLs every day. Finally, they’ve got time to clean up and refactor on-premises data sources. Without fear of breaking analytics - because the cloud DWH securely stores all past states of the originals.
Customer is a streaming platform that offers a wide variety of TV shows, movies, documentaries, and more on thousands of internet-connected devices.SolutionsTotal Cost of Ownership (TCO) was carefully considered by comparing the performance and costs of alternative services offered by a number of cloud providers. The most advantageous combination of cost, quality, and sustainability was chosen to meet customer requirements.Business requests were collected by interviewing numerous stakeholders. The requests served as a starting point for the BI dashboards, the DWH schema, and data pipelines design.The multidimensional OLAP cube (facts, dimensions, etc.) was built and extended piece-by-piece revealing one business request after another. The cloud warehouse consolidated large amounts of data from disparate on-premises sources.Convenient BI dashboards were designed and implemented to meet collected earlier business requests and their possible future variability. The end business users with no SQL skills could perform requests using straightforward BI dashboards, without DBA team involvement.Auto-scalable ETL workflows were designed and implemented as the business requests and DWH schema had been established. The pipelines extract, transform, and load data from the source databases (on-premises) into the target DWH (on the cloud).Backward historical consistency was achieved by introducing meta-tables and unique timestamps that indicated the time range when a source row or table was actual.ImpactUsing simple controls, business users can configure interactive dashboards and get required information immediately and independently, without the involvement of other departments.DBAs are now free from writing the exact same SQLs every day. Finally, they’ve got time to clean up and refactor on-premises data sources. Without fear of breaking analytics - because the cloud DWH securely stores all past states of the originals.
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