Data virtualization is not a commonly known data technique, and in my opinion, it’s one of the most useful techniques in a time when the volume and velocity of data are growing exponentially. I believe that data virtualization solves larger data challenges, so I decided to explore the process of implementing data virtualization within Azure and Fabric ecosystems.
But first, what is data virtualization? Data virtualization is a technique that allows users to access and analyze data from different sources without moving or copying the data. Data virtualization creates a logical layer that abstracts the physical location, format, and structure of the data, and provides a unified view of the data to the users. Data virtualization enables users to query and manipulate data from multiple sources as if they were a single source. This is paramount, as data sources do not move from where they are; they are not copied nor replicated to be used.
The benefits of data virtualization are many, but a few that standout are:
- Reducing data movement and duplication, which can save time, cost, and storage space.
- Improving data quality and consistency, by applying common rules and standards to the data.
- Enhancing data security and governance, by applying centralized policies and controls to the data.
- Increasing data agility and flexibility, by enabling users to access and combine data from various sources on demand.
How to Perform Data Virtualization in Azure or Fabric
Microsoft Azure and Microsoft Fabric offer various solutions and services that support data virtualization. Some of the examples are:
- Azure SQL Managed Instance: Azure SQL Managed Instance is a fully managed SQL Server instance in the cloud that supports data virtualization through external tables and PolyBase. External tables allow users to query data from Azure Blob Storage, Azure Data Lake Storage, or other SQL Server instances as if they were local tables. PolyBase allows users to query data from Hadoop or Azure Synapse Analytics using T-SQL.
- Azure Synapse Analytics: Azure Synapse Analytics is a cloud-based analytics service that integrates data warehousing, big data processing, and data integration. Azure Synapse Analytics supports data virtualization through Synapse SQL and Synapse Spark. Synapse SQL allows users to query data from Azure Storage, Azure Cosmos DB, or other SQL Server instances using T-SQL. Users can also create external tables in Synapse SQL or Synapse Spark to access data from other sources.
- Fabric supports data virtualization through Warehouse and Lakehouse: Warehouse enables users to query data from any source using T-SQL. Lakehouse allows to query any source using either T-SQL or Spark SQL. It is also possible to create shortcuts in OneLake either from any Azure storage solution or from any other cloud vendor storage.
Advantages of Data Virtualization
Data virtualization in Microsoft Azure or Fabric can bring many advantages to users who want to access and analyze data from different sources.
For users, the advantages of data virtualization can enable them to query and manipulate data from various sources using familiar tools and languages, such as T-SQL, Spark SQL, or Power BI. Also, it ensures that the same data quality rules and principles are maintained, as there are no multiple versions of the same data source.
For organizations, the promise of data virtualization can enable them to reduce the complexity and cost of their data estate, by eliminating the need for data movement and duplication. Data virtualization can also enable them to improve the quality and security of their data, by applying common rules and standards to the data. Data virtualization can also enable them to increase the agility and flexibility of their data, by enabling them to access and combine data from various sources on demand.
Final Thoughts
Data virtualization is a powerful technique that can help users and organizations access and analyze data from different sources without moving or copying the data, provide users with a unified and simplified experience for data lake and data warehouse workloads, and enable them to query and manipulate data from any source. In other words, everybody benefits from data virtualization.
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