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 large 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.
Data has become the strategic asset used to transform businesses to uncover new insights. Traditionally, data has been gathered in an enterprise data warehouse where it serves as the central version of the truth. However, the world of data is rapidly evolving in ways that are transforming the industry and motivating enterprises to consider new approaches of gaining insights.
Beyond the traditional sources from transactional systems, ERP, CRM, and LOB applications, new types of data sources are driving analytics that are transformative to the business. And it is coming from data generated by everything around us like social media apps, websites and connected devices. Collectively, IDC projects that this explosion of data will result in a 40 Zetabyte digital universe by 2020. (Microsoft, 2016)
The challenge for IT organizations is their traditional enterprise data warehouse was never designed to incorporate this explosion of new types of data at this volume and velocity. To solve for this will require dramatic changes so much so that Gartner reports, “Data warehousing has reached the most significant tipping point since its inception. The biggest, possibly most elaborate data management system in IT is
changing.“ To drive the business forward, the modern enterprise needs to evolve their enterprise data warehouse so that it can take advantage of big data and do so in real time. Once all data has been incorporated, this lets business analysts and data scientists uncover new insights that impact the business. To do this, the traditional data warehouse needs to evolve into a modern data warehouse. (Microsoft, 2016) (CLICK HERE to read the full White Paper)
Sample Modern Data Warehouse – Above
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.
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