Power BI Dataflows and Azure Data Lake Storage Gen2 Integration Preview
In November, we announced Power BI’s self-service data preparation capabilities with dataflows, making it possible for business analysts and BI professionals to author and manage complex data prep tasks using familiar self-service tools. Dataflow data can be easily shared across Power BI, allowing business analysts and BI professionals to save time and resources by building on each other’s work, instead of duplicating it, leading to more unified, less siloed data. Learn more about Power BI data prep capabilities here.
Today, we’re excited to announce integration between Power BI dataflows and Azure Data Lake Storage Gen2 (preview), empowering organizations to unify data across Power BI and Azure data services. With this integration, business analysts and BI professionals working in Power BI can easily collaborate with data analysts, engineers, and scientists working in Azure. These new features free valuable time and resources previously spent extracting and unifying data from different sources, so your team can focus on turning data into insights.
Breaking data silos with Azure Data Lake Storage Gen2 and Common Data Model folders
Data is a company’s most valuable asset. Business analysts and data professionals spend a great deal of time and effort extracting data from different sources and getting semantic information about the data, which is often trapped in the business logic that created it, or stored away from the data, making collaboration harder and time to insights longer.
To address these challenges, Power BI and Azure data services have teamed up to leverage Common Data Model (CDM) folders as the standard to store and describe data, with Azure Data Lake Storage as the shared storage layer. CDM folders contain schematized data and metadata in a standardized format, to facilitate data exchange and to enable full interoperability across services that produce or consume data stored in an organization’s Azure Data Lake Storage account.
One of the compelling features of dataflows is the ease with which any authorized Power BI user can build semantic models on top of their data. Because dataflows already store data in CDM folders, the integration between Power BI and Azure Data Lake makes it possible for any authorized person or service to easily leverage dataflow data, using CDM folders as a shared standard.
Furthermore, with the introduction of the CDM folder standard and developer resources, authorized services and people can not only read, but also create and store CDM folders in their organization’s Azure Data Lake Storage account. Once a CDM folder has been created in an organization’s Data Lake Storage account, it can be added to Power BI as a dataflow, so you can build sematic models on top of the data in Power BI, further enrich it, or process it from other dataflows.
The diagram below showcases a range of services contributing to and leveraging data from CDM folders in a data lake.
Power BI and Azure Data Lake Storage Gen2 Integration features
Power BI customers can now:
Connect an Azure Data Lake Storage Gen2 account to Power BI
Configure workspaces to store dataflow definition and data files in CDM folders in Azure Data Lake
Attach CDM folders created by other services to Power BI as dataflows
Create datasets, reports, dashboards, and apps using dataflows created from CDM folders in Azure Data Lake
These new Power BI capabilities are available today for Power BI Pro, Power BI Premium and Power BI Embedded customers. All you need to get started is an Azure Data Storage account.
Store dataflow data in your organization’s Azure Data Lake Storage
Before you can start storing Power BI dataflows in your organization’s Azure Data Lake Storage account, your administrator needs to connect an Azure Data Lake Storage account to Power BI. Once connected, Power BI administrators can allow Power BI users to configure their workspaces to use the Azure storage account for dataflow storage.
Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed in https://powerbi.microsoft.com/en-us/blog/power-bi-dataflows-and-azure-data-lake-storage-gen2-integration-preview/