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Modern Data Estate / Modern Data Warehouse

It’s More About Culture Than Technology

An organizations data today is as valuable as oil during the oil boom.  As an organization, it is your most valuable currency!  Data is often a significant driver of digital transformation and developing strategies for digital disruption.  Businesses globally are working diligently and feverishly to build a robust, modern, data infrastructure to support how they mine the rich data they have accumulated and making it available to the business and their customers.  Thus, the notion of the “modern data estate”.

Having a modern, contemporary data estate is critical to the modern business landscape.  Just consider how raw data within an organization and the growth of that expands by an order of magnitude on an almost daily basis.  The good news is that given the maturity of technologies available today, especially in the cloud, companies can now build out their progressive data estate.

Organizations growth and success, as well as financial health, will be largely dependent upon the overall maturity of their data infrastructure.  Organizations will thrive according to the maturity of their data infrastructure. In a competitive environment where data can make or break a businesses’ competitive advantage, corporate success might very well be measured by the maturity of its enterprise data estate and data program. Building the modern data estate is not as difficult as one might imagine.

Why a Modern Data Warehouse?

A modern data warehouse as the foundation of an effective analytics strategy. As business decisions across teams become more data driven, this puts more load on the data warehouse and data marts. It has to deal with far more concurrency and mixed workloads with real-time data. 

Sample Modern Data WarehouseAbove

This architecture uses Azure Data Lake Storage at the center of the solution for a modern data warehouse.  Integration Services is replaced by Azure Data Factory to ingest data into the Data Lake from a business application. This is the source for the predictive model that is built into Azure Databricks.

PolyBase is used to transfer the historical data into a big data relational format that is held in Azure SQL Data Warehouse, which also stores the results of the trained model from Databricks. Azure Analysis Services provides the caching capability for SQL Data Warehouse to service many users and to present the data through Power BI reports.

Contact the Data and Analytics Team at Oakwood Today!