You have taken the same industry leading data warehouse to a whole new level of performance and capabilities, new tools are available to analyze unstructured data, particularly given specific use case parameters, furthermore, interact with large volumes of data, create dynamic reports and mashups and gain insights from data visualizations, consequently, data virtualization allows you to integrate data from various sources, keeping the data in-place, so that you can generate reports and dashboards to create business value from the data.
A departure from traditional analytics systems which used to operate on data latency of a single day or more, all data in the compute layer is temporary, and only held as long as the virtual warehouse is active, also, it is designed for query and analysis rather than for transaction processing, and usually contains historical data derived from transaction data, and can include data from other sources.
For stored procedures that are used manage data flows and transformations, a tool that monitors data movements, a data modeling tool to build up analytic scenarios etc, bring people and information together to make confident and superior business decisions using your revolutionary data warehousing technology, particularly, gone are the pains associated with administering, managing, patching and manual tuning of data warehouses.
The emergence of adaptive, agile data warehousing platforms has simplified organizations ability to envision, develop and deploy business intelligence and analytics services, when trying to design a data warehouse, you often try to model the database on the operational data model, moreover, as the demand for data analytics grows so does the need for a technology or platform to process large amounts of different types of data in timely manner.
Some times you underestimate the time required to extract, clean, and load the data into the warehouse, akin nodes receive a command, the architecture breaks the data into pieces for each node, and all akin compute nodes operate over relevant data, also, once produced a data warehouse must be maintained (personnel, licensing costs, maintenance, etc.).
At its simplest, data warehouse is a system used for storing and reporting on data, be sure to use fast data comparison operation to compare data in a blink of an eye, also, load manager performs the operations required to extract and load the data into the database.
Unlike other dimensions where surrogate keys are just incremental numbers, date dimension surrogate key has a logic, most enterprises require a centralized data warehouse for the purpose of advanced analytics and reporting. Also, multiple data warehousing technologies are comprised of a hybrid data warehouse to ensure that the right workload is handled on the right platform.
Want to check how your Azure SQL Data Warehouse Processes are performing? You don’t know what you don’t know. Find out with our Azure SQL Data Warehouse Self Assessment Toolkit: