Microsoft’s cloud data warehouse, Azure SQL Data Warehouse, provides enterprises with significant advantages for processing and analysing data for business intelligence. It leverages massively parallel processing (MPP) to quickly run complex queries across petabytes of data. From high performance analytics, scalable technology and reduced costs, Azure data warehousing easily outperforms traditional on-premises data warehouse solutions.
But an enterprise data warehouse doesn’t exist in isolation. It is part of a complete analytics solution including the ‘pipelines’ that extract, transform, and load data (ETL), often from many disparate sources, as well as staging and reporting layers. Relying on “good ols” manual coding to build and maintain such solutions is not just costly and time-consuming, but on the long-run may even diminish the flexibility of your solution. Being agile, enabling continuous integration (CI) and continuous deployment in analytical data management is no longer an option for BI & Big Data practitioners.
This is where tools for analytical data management automation such as biGENiUS provide value both for developers and business users. As a certified automation partner, biGENiUS brings virtually out-of-the-box the benefits of metadata-driven automation to accelerate and streamline the development and lifecycle management of SQL DW solutions. Read more in the blog of Anum Jang Sher, Program Management at Microsoft.
