Sql Web Server Business Intelligence Advancement Workshop Quotes – Power BI, more than any other Microsoft product that I can remember, offers more options and choices for building and delivering a solution. Without compromise, Power BI can be effectively used to do anything from creating a simple chart with an Excel spreadsheet, to enterprise reporting and analysis in the massive data warehouse of Fortune 100. At the end of this post, I will share a comprehensive list of resources and insights by Matthew Roche, Program Manager of the Power BI Customer Advisory Team (CAT). To tease that series, I’ll start with this quote from Matthews’ blog:
Succeeding with a tool like Power BI is easy – self-service BI tools let more users do more things with data more easily, and can help reduce the reporting burden on IT teams. Succeeding at scale with a tool like Power BI is not easy. It is very difficult, not because of the technology, but because of the context in which the technology is used. Organizations are adopting self-service BI tools because their existing approaches to working with data are no longer successful – and because the cost and pain of change have become outweighed by the cost and pain of staying the course. Matthew Roche, Building a data culture – BI Polar (ssbipolar.com)
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When should you use dataflows versus regular Power Query? I didn’t jump on the data feeds bandwagon and struggled to adopt them at first. Honestly, Power Query is easier to use. The browser-based dataflow designer is pretty impressive, but it’s not as responsive and convenient as the desktop app, so this is a bit of a trade-off. The power and value of data flows becomes evident when the business reaches a certain stage of data culture maturity.
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Before we can address the question of whether to use Power BI Dataflows, conventional Power BI queries, or any other approach to sourcing and transforming data; we need to briefly review different options for orchestrating a Business Intelligence solution in the Microsoft cloud ecosystem.
On a scale of one to ten, with ten being the most formalized, tightly controlled and complex corporate reporting platform; the Power BI self-service option could range from one to four.
For the self-service data analyst, working entirely in Power BI Desktop, data can be imported and transformed using Power Query. Tables are modeled, calculations are defined and data is visualized. This mode is simple and works well for small to moderate-scale solutions with less emphasis on data governance and centralized control.
Even using this simple approach, data models can be developed separately from reports, certified and shared with multiple report developers and self-service report authors. So, to some extent, business data can be managed and controlled – but the queries in the Power BI solution read directly from source systems or files that are not maintained for analytical reporting.
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The “single version of the truth” or “golden record” repository, data warehouse (or smaller scale “data store”) is the ideal solution for storing and managing reliable corporate information. The challenge with creating a central data warehouse to manage centrally-controlled organizational data is that it is expensive and time-consuming, however the trade-off is that self-service data models can be imprecise and outdated. When business leaders need answers quickly, it’s not always feasible to add more data sources to a data warehouse quickly.
On the complexity scale from one to ten, versions of this option could be from seven to ten.
A conventional DW/BI solution typically uses on-demand data transformation tools like SSIS to stage and transform source data into a central data warehouse built with a relational database product like SQL Server. Although viable for on-premises systems, this old-school architecture model does not embrace scalable and cost-effective cloud technologies.
The first generation of the modern Microsoft cloud data warehouse can use several different Azure services. The components in the following example are easily equated to the conventional data storage solution in the previous example. Azure Data Lake services as the staging environment typically using text files and structured file storage as a cheap landing gear for consumed source data. Azure Data Factory is used to orchestrate and transform files and data streams into and out of the data lake – and the data warehouse. Depending on the need for scale and size, Azure SQL Database or Azure Data Warehouse (now called Azure Synapse) can be used for data storage.
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If your organization has a large data warehouse to serve all or most of the data needed for analytical reporting, this is probably the best fit for a Power BI solution in your business environment.
Building an enterprise data warehouse solution is no trivial endeavor, often involving as much effort to negotiate business process challenges as the technology development to implement the solution.
The newest generation of Azure’s modern data warehouse is a best-in-class collection of tightly integrated cloud services called Azure Synapse Analytics. Compared to the previous set of independent Azure services, Synapse Analytics provided a unified development and management interface. Apache Spark and other industry standard technologies designed for data science and platform-agnostic analytics provide the open source data preparation engine. Azure Synapse is the evolution of Azure Data Warehouse, Microsoft’s read-optimized, scalable massively parallel-processing (MPP) SQL-based database engine.
Dataflows can fill an important gap between purely self-service data preparation and formal data storage solutions. If you don’t have a large data warehouse to meet your analytical reporting requirements, but you need to provide more data quality control over standardized entities, incorporating data flows could be the ticket.
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In its simplest form, data flows provide reusable transformation logic (queries) that can be shared by multiple Power BI data models. Using dataflows deployed to a workspace can save data modeling developers from repeating the same transformation steps in multiple datasets. But these are more than just Power Query scripts stored in the cloud.
A long list of capabilities is enabled by using data streams. They can provide integrity and standard entity definitions maintained in Dataverse (formerly known as the Common Data Model) to enforce standard naming, data types, and schema conformance among other features.
In Premium Capacity, data stream results can be persisted in Azure Data Lake Gen2 storage. This essentially allows you to use data streams to create a moderate-scale data warehouse without a large investment. Entities can be linked to related entities that create virtual joins and reference constraints. Other Premium features include DirectQuery, Computed entities and Incremental refresh – all managed in the data stream rather than for each dataset. Integrations with Azure AI, Machine Learning and Cognitive Services allow you to use AI functions without writing code. For example, in a recent project, we used AutoML on a data stream containing high school data to predict graduation outcomes.
Dataflows start with an M query, just like the queries in Power BI Desktop before adding the additional capabilities mentioned earlier. Queries are written entirely in the browser but migrating from Power Query to Power BI Desktop is quite easy. Start with a Power BI solution (PBIX file) in Desktop and open a query in the Advanced Query Editor. You can create a new data flow in the browser and then copy and paste the existing query M-code from Desktop into the data flow designer. You do have to copy each question individually and there are only a few matching differences but for the most part, it should be a one-to-one transfer.
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Matthew Roche from the Power BI Customer Advisory Team has an excellent 17-part blog series on Building a data culture. Data flows sit at the crossroads between business process, data management and technology. The industry has been throwing technology and software at data governance and quality issues for decades, with marginal success. It is much easier for data practitioners to acknowledge that these are multifaceted business culture challenges than to formulate a plan to succeed. If anyone can effectively carry and deliver this message, it is Matthew. In this video series, he offers prescriptive guidance for recruiting executive sponsorship, working with business stakeholders, and navigating the landmines of a business landscape toward a successful data culture transition.
To be honest, I’ve only been catching up on this series in bits and pieces over the past year and now that I’ve caught the vision, I plan to watch the entire series from start to finish. It is so good. Think of it as Game of Thrones with data.
Matthew also provides a comprehensive list of Power BI Dataflows resources here. Matthew recently presented to our 3Cloud Power BI and Analytics development team on using data streams to promote a data-driven culture. This presentation was an epiphany for me that helped better understand how data flows fit into the BI solution puzzle – that’s when the meter metaphor popped into my head. I encourage you to watch and perhaps you will have a similar moment of reckoning.
The Power BI Adoption Framework is a set of presentations from Microsoft that can serve as an overview of important tasks and areas that should be covered in any Power BI implementation, large and small. These decks are also a great tool for adopting and sharing your organization’s BI and Analytics strategy with business leaders and stakeholders. You can use
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