Customer profile

  • sweet.tv is a streaming platform that offers a wide variety of TV shows, movies, documentaries, and more on thousands of internet-connected devices.

Business Goals

  • Design and implement centralized analytical Enterprise Data Warehouse (EDW) hosted on the cloud.
  • Design and develop business intelligence (BI) applications on top of the EDW.
  • Collect and process required data for the BI from a range of on-premises sources.
  • Store processed data for analytics in the cloud EDW.
  • Cloud EDW must keep track of changes of on-premises databases, including row updates, schema changes, and deletion of the source tables. Any changes in the sources, including deletion, must not affect the operating of the BI services.
  • BI applications must be capable of showing historical information modified or removed from the sources.
  • Free the DBA team from typing SQLs to handle BI requests.
  • Provide access to the BI tools to a wide range of end-users so they can do the analytics on their own, without the involvement of DBAs.
  • Minimize Total Cost of Ownership (TCO) of the services.

Solution

  • Total 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).
  • An incremental index was introduced to large source tables to upload only updates, not complete tables.
  • 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.

Impact

  • Using 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.
Roadmap
7 months
Discovery
Solution Architect
Cloud Services Setup
DevOps
VPN Connection Setup
DevOps
Collecting Business Requests
Data Architect
BI Dashboards Development
Data Analyst
DWH Schema Development
Data Architect
ETL Development
Data Engineer
Deployment and Integration
DevOps
Infrastructure Provisioning Automation
DevOps
Documentation
Knowledge Transfer
Release

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    Roadmap
    7 months
    Discovery
    Solution Architect
    Cloud Services Setup
    DevOps
    VPN Connection Setup
    DevOps
    Collecting Business Requests
    Data Architect
    BI Dashboards Development
    Data Analyst
    DWH Schema Development
    Data Architect
    ETL Development
    Data Engineer
    Deployment and Integration
    DevOps
    Infrastructure Provisioning Automation
    DevOps
    Documentation
    Knowledge Transfer
    Release

    Industry

    Keywords

    • ETL
    • Business Analytics
    • Business Intelligence
    • Cloud Migration
    • Infrastructure Development

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