Modern Data Warehouse Design and Implementation


Traditionally, a Data Warehouse has been a central data repository aiming to integrate and store the, mostly structured, data of a corporation, in order to facilitate Business Intelligence, or to put it simply, the reporting and analysis of large amounts of historical data. The Data Warehouse was mostly thought as a relational database sitting on highly optimized, expensive hardware. That statement has always been misplaced as the Data Warehouse is a set of technologies, tools and processes to manage data and all aspects around data such as integrity, security, sharing and collaboration and many others.


There have been many disruptions around data in the last years, the amount of data that can be produced and stored has exploded (Volume), data need to be processed and acted on in real time (Velocity) and advanced data formats, like JSON, XML, or even binary, are used more and more in modern applications (Variety). New technologies have risen to deal with the new challenges, like Big Data, Python frameworks, Cloud Services, etc.

Lastly, Artificial Intelligence and Machine Learning technologies are more mature than ever, and have proved themselves capable of solving complex real-world problems, shifting data analysis from traditional Business Intelligence to Advanced and Predictive Analytics. A modern Data Warehouse has to offer a comprehensive set of tools to deal with the constantly rising challenges.

Azure Synapse Analytics

Azure Synapse Analytics is an analytical data warehousing and big data engine, offering native data integration capabilities, that can scale horizontally in seconds to cover demanding workloads. It is backed by Azure Data Lake for storing large volumes of structured, semi-structured and unstructured data that can be queried directly. Serverless and dedicated pools can process and query data using SQL and Spark. Services like Azure Event Hubs and Azure Event Grids can write data coming in real time directly to Data Lake and Synapse. Azure Machine Learning can be used to develop and run advanced analytics models. It is clear that Azure offers an extensive set of services, tightly integrated between them to cover all needs and every unique corporate environment.

Join Office Line

Join the large number of customers have trusted us over the last years to design and build their modern Data Warehouse on Azure. Together we will identify, deploy and configure all those services required to drive a successful data estate, to enable your end users make the right decisions at the right time. We will provide you guidance on defining the target architecture to cover for your data and business needs, cost requirements, collaboration and sharing needs and security and access constraints.

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