Data warehouse architecture

This example scenario demonstrates a data pipeline that integrates large amounts of data from multiple sources into a unified analytics platform in Azure. This specific scenario is based on a sales and marketing solution, but the design patterns are relevant for many industries requiring advanced analytics of large datasets such as e-commerce, retail, and healthcare. Architecture Download a Dataflow The data flows through the solution as follows: • For each data source, any updates are exported periodically into a staging area in Azure Data Lake Storage. • Azure Data Factory incrementally loads the data from Azure Data Lake Storage into staging tables in Azure Synapse Analytics. The data is cleansed and transformed during this process. PolyBase can parallelize the process for large datasets. • After loading a new batch of data into the warehouse, a previously created Azure Analysis Services tabular model is refreshed. This semantic model simplifies the analysis of business data and relationships. • Business analysts use Microsoft Power BI to analyze warehoused data via the Analysis Services semantic model. Components The company has data sources on many different platforms: • SQL Server on-premises • Oracle on-premises • Azure SQL Database • Azure table storage • Azure Cosmos DB Data is loaded from these different data sources using several Azure components: • • • • • • Alternatives • The example pipeline includes several different kinds of data sources. This architecture ...

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• • • • • • • • • • Before diving into architectural details, it’s important to understand the purpose of a data warehouse. Data warehouses are unique in that they store both live and archived data in one location. This means that the same data is available both as a result of business processes being completed or operations being performed, and also because it is simply existing data stored in one place. The data warehouse is a repository of data that has been extracted from a variety of sources, including data reported by users, manufacturers, and third-party vendors. It has been organized into tables and other databases to make it more accessible and easier to use. Although the term “data warehouse” may conjure up images of large, detailed repositories with high storage requirements, many modern data warehouses are optimized for speed and accessibility so that they can be used efficiently by businesses of all sizes. This article covers everything you need to know about designing a data warehouse architecture. We explain why data warehouses are necessary and how they can be implemented; we discuss the primary types of architectures available; and we highlight factors to consider when deciding between various options. Data Warehouse Architecture The single tier Data Warehouse architecture is composed of a single hardware layer. This hardware layer is composed of a single hardware layer. There are three approaches to creating a data warehouse layer: Single tier, two-tier, ...

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In this article This article targets IT professionals and IT managers. You'll learn about BI solution architecture in the COE and the different technologies employed. Technologies include Azure, Power BI, and Excel. Together, they can be leveraged to deliver a scalable and data-driven cloud BI platform. Designing a robust BI platform is somewhat like building a bridge; a bridge that connects transformed and enriched source data to data consumers. The design of such a complex structure requires an engineering mindset, though it can be one of the most creative and rewarding IT architectures you could design. In a large organization, a BI solution architecture can consist of: • Data sources • Data ingestion • Big data / data preparation • Data warehouse • BI semantic models • Reports The platform must support specific demands. Specifically, it must scale and perform to meet the expectations of business services and data consumers. At the same time, it must be secure from the ground up. And, it must be sufficiently resilient to adapt to change—because it's a certainty that in time new data and subject areas must be brought online. Frameworks At Microsoft, from the outset we adopted a systems-like approach by investing in framework development. Technical and business process frameworks increase the reuse of design and logic and provide a consistent outcome. They also offer flexibility in architecture leveraging many technologies, and they streamline and reduce engineering overh...

The staging area tends to be one of the more overlooked components of a data warehouse architecture, and yet it is an integral part of the ETL component design. Learn why it is best to design the staging layer right the first time, enabling support of various ETL processes and related methodology, recoverability and scalability. In any data warehousing initiative, there are several common components to the architecture. There are the data sources and targets, ETL framework, infrastructure, application layer and the data staging area. The staging area, in my experience has to be one of the more overlooked and underestimated components of a data warehouse architecture. I think mostly this is due to a lack of understanding as to what exactly it is. If a quick search is made through a number of websites, many definitions will include the fact the data staging area is simply a temporary workspace used to transform and enrich data before it flows into the operational data store (ODS) and the data warehouse. This is a good fundamental definition of the data staging area. However, it is so much more. How much more do you ask? Well in reality, the data staging area is an information hub that facilitates the enriching stages that data goes through in order to populate an ODS and/or data warehouse. It is the essential ingredient in the development of an approach and/or methodology for creating a comprehensive data-centric solution for any data warehousing project. If we really think ...