A UK-based online travel provider delivers search engine and travel agency services with 100 million monthly users. Its users can search and book flights, hotels, and car rentals for trips anywhere in the world.
This data-driven organization has multiple departments relying on accurate data to develop insights and track KPIs. But various tools including Tableau and Excel were returning inconsistent data reports. The team needed a new tool to support multidimensional data analysis in the cloud, give business users direct access to the Delta Lake on Databricks, and ensure that consistent data is dispersed throughout all departments in the company.
Inconsistent data reports dilute the accuracy of data insights
This organization had a long-standing integration with SQL Server Analysis Services, but as the business scaled SSAS no longer suited the company’s needs. The most significant pain points for the company were data consistency and accuracy.
Business users from different departments within the organization use different BI tools, including Tableau and Excel. The business frequently encountered issues with the inconsistency of data query results across the platforms, diluting the accuracy of data insights and limiting cross-department collaboration and innovation.
Autonomous data engineering and a universal semantic layer
After a multi-year effort to de-commission SSAS, the team was looking for a tool that would provide business users with a multidimensional interface to support data analysis in the cloud.
It was also important for the new tool to provide direct access to the Delta Lake on Databricks — to fix the inconsistencies of data query results across various BI tools like Tableau and Excel. The company needed to resolve these inconsistencies while still allowing business users from different departments to operate on their BI tools of choice.
The data team partnered with AtScale and Databricks to build a universal semantic layer that provided a consistent and governed “diamond layer” of metrics and KPIs that could be accessed and used by anyone across the company regardless of the BI tool they were using. AtScale’s solution enabled them to automate and orchestrate aggregate creation while pushing all workloads to the Databricks Lakehouse, ultimately providing a highly scalable, low latency solution that decreased time-to-insights.
Consistent results across dashboards and faster time-to-insights
With AtScale, business users get consistent data query results regardless of their BI tool. Users can conduct pivot table analysis on data in Databricks, model once in AtScale, and ensure that query results and reports are consistent across tools. AtScale also enables the business to generate data insights faster, while increasing cross-departmental collaboration and innovation.