Dataflows for Microsoft Dataverse offer a flexible, user-friendly solution for importing and reshaping data from diverse sources into Dataverse. If you’re studying or preparing for the PL-900 Microsoft Power Platform Fundamentals exam, understanding the concept of dataflows becomes extremely crucial.
Dataflows in the Microsoft Power Platform
Dataflows are part of the Microsoft Power Platform’s data-integration capabilities. They’re designed to let you connect to, clean, transform, and load data from a wide range of sources. You can simplify and democratize the data importing process, making critical data accessible to business users, not just data engineers and analysts.
Dataflow Components
There are two main components of a dataflow:
- Connections: These are the sources of the data being imported. Connections can be made to many types of data sources, ranging from Excel workbooks, SharePoint lists to SQL Server databases, and other cloud-based data platforms like Salesforce.
- Tables: These are the structures into which the imported data is loaded within Dataverse. You can create new tables in Dataverse as part of your dataflow, or you can target existing tables, pending you have the necessary permissions.
The Role of Power Query in Dataflows
The Excellency of the dataflows lies not just in import but also reshaping and cleaning the data. The Power Query, a data connection technology that lets you refresh data from multiple sources in Excel, plays a crucial role in this context.
With Power Query, you can apply steps to transform your data as it is loading into Dataverse. This includes removing unnecessary columns, filtering rows, sorting and grouping data, and many other functions. If you are familiar with Power Query from Excel or Power BI, you will find the same essential capabilities available within dataflows.
The Process of Creating a Dataflow
Creating a dataflow in the Microsoft Dataverse consists of a few simple steps:
- Source selection. Connecting to the data source or sources that you want to import data from.
- Data transformation. Using Power Query transformations to clean-up or reshape data as it is imported.
- Destination selection. Choosing the existing table (or creating a new one) where the data will be stored.
- Mapping fields. Linking the columns in your source to the fields in the destination table.
- Refreshing settings. Setting up how often the dataflow should automatically refresh to pull in new data.
Dataflows vs. Traditional Data Import
While traditional data import is a more manual method of getting data into the Dataverse, you must prepare your data and ensure it meets all the required criteria. On the other hand, dataflows enhance and simplify this process as they allow users with less technical expertise to import and shape data, without the need for coding or complex transformations.
| Traditional Data Import | Dataflows | |
|---|---|---|
| Connection to Source | Must be manual | Can be automatic |
| Transformation | Requires external tools | Built-in (Power Query) |
| Automation | Requires scripting | Built-in |
| Mapping | Manual, more technical | Assisted, less technical |
| Refreshing | Must be manual | Can be automatic |
To sum up, a deep understanding of how dataflows work can contribute to better data integration and management within the Microsoft Power Platform. Whether you’re studying for the PL-900 exam or wanting to improve your grasp on the platform, recognizing the capabilities of dataflows in the Microsoft Dataverse is vital.
Practice Test
True or False: Microsoft Dataverse is a low-code data platform for Microsoft Power Platform.
- True
- False
Answer: True
Explanation: The Microsoft Dataverse is indeed a low-code data platform for Microsoft Power Platform for simplifying data management and integration across business applications.
In Microsoft Dataverse, what does dataflow do?
- A. Imports data
- B. Transforms data
- C. Exports data
- D. Both A and B
Answer: D. Both A and B
Explanation: In Microsoft Dataverse, dataflows are used to import, transform and load data.
True or False: Dataflows in Microsoft Dataverse can only import from on-premise data sources.
- True
- False
Answer: False
Explanation: Microsoft Dataverse dataflows can import data from various sources, including both on-premise and cloud-based.
Dataflows in Microsoft Dataverse allows ETL operations, what does ETL stand for?
- A. Extract, Transform, Load
- B. Enter, Transfer, Load
- C. Extract, Transfer, Light
- D. Enter, Transform, Light
Answer: A. Extract, Transform, Load
Explanation: In the context of data management, ETL refers to the process of Extracting, Transforming, and Loading data.
What are the main elements in Microsoft Dataverse?
- A. Tables
- B. Columns
- C. Rows
- D. All of the above
Answer: D. All of the above
Explanation: Tables, columns, and rows are the main elements in Microsoft Dataverse’s relational data store.
True or False: Microsoft Dataverse dataflows cannot be connected with Power BI.
- True
- False
Answer: False
Explanation: Microsoft Dataverse dataflows can be integrated with Power BI, facilitating better data visualisation and analysis.
Where can you create and manage dataflows in Microsoft Dataverse for Microsoft Power Platform?
- A. Azure portal
- B. Power Apps portal
- C. Both A and B
- D. None of the above
Answer: B. Power Apps portal
Explanation: Dataflows in Microsoft Dataverse can be created and managed using the Power Apps portal.
True or False: Dataflows in Microsoft Dataverse support more than 200 public data source connectors.
- True
- False
Answer: True
Explanation: Microsoft Dataverse dataflows indeed supports a high number of public data source connectors for flexible data management.
What is not a step when creating a dataflow in Microsoft Dataverse?
- A. Connect to your data sources
- B. Transform and clean your data
- C. Schedule the data refresh frequency
- D. Email the data to your team
Answer: D. Email the data to your team
Explanation: While creating a dataflow, the primary steps include connecting to a data source, transforming the data, and scheduling the refresh frequency. There is no option to email the data in the process.
True or False: Dataflows in Microsoft Dataverse support real-time data processing.
- True
- False
Answer: False
Explanation: Microsoft Dataverse dataflows process data at scheduled time intervals, not in real-time.
Interview Questions
What is Microsoft Dataverse?
Microsoft Dataverse is a simplified, scalable, custom data service built on Microsoft Azure. It provides secure data storage and management, simplifying app development by integrating data silos.
What is the purpose of dataflows in Microsoft Dataverse?
Dataflows enable the extraction, transformation, and loading of data into Microsoft Dataverse from a variety of sources. They simplify the data integration process and help keep data up-to-date.
How can dataflows in Microsoft Dataverse be created?
Dataflows in Microsoft Dataverse can be created using Power Query, which allows you to extract, transform, and load data from a variety of sources.
What types of transformations are offered by Power Query for dataflows in Microsoft Dataverse?
Power Query offers transformations including row-level transformations, column-level transformations, and appending or merging queries.
What type of data can be loaded into Microsoft Dataverse using dataflows?
Dataflows can load data from wide variety of sources into Microsoft Dataverse, such as databases, Excel files, SharePoint folders, and many more.
Which Microsoft tool is typically used to process dataflows in Microsoft Dataverse?
Power Query is typically used to process dataflows in Microsoft Dataverse. It’s a data connection technology that allows you to discover, connect, combine, and refine data across a range of sources.
How can dataflows be scheduled in Microsoft Dataverse?
Dataflows can be scheduled within Microsoft Dataverse. The schedule can be set to automatically refresh the data from the original sources at specified times.
Can dataflows help in unifying data from multiple sources in Microsoft Dataverse?
Yes, dataflows can combine and unify data from multiple sources, allowing you to extract insights from a unified version of your data.
How does dataflow work with Microsoft Power Platform applications?
Once the data is loaded into Microsoft Dataverse via dataflows, it’s readily available for use across various Microsoft Power Platform applications like Power BI, Power Automate, and PowerApps.
Can transformation logic be applied to incoming data when using dataflows in Microsoft Dataverse?
Yes, transformation logic can be applied to incoming data when using dataflows, which means you don’t have to make any changes in source systems for the data to be right shape or format in Dataverse.
How many rows of data can dataflow process in Microsoft Dataverse?
In a single dataflow job, you can process up to 100 million rows with the standard timeout of 120 minutes.
How can dataflow improve performance in Microsoft Dataverse?
Dataflow can make use of Power Query’s query folding capability where parts of the Power Query transformations are pushed down to the source system, optimizing extraction process and improving overall performance.
Are there any storage limitations with Microsoft Dataverse and dataflows?
Yes, the storage is limited by your organization’s available capacity in Microsoft Dataverse. The actual quantity of data stored is also calculated as per Dataverse capacity usage metrics.
How does Microsoft ensure the security of data handled by dataflows in Dataverse?
Dataflows follow the same security model as the rest of Microsoft Power Platform and Dataverse. They also leverage Azure datacenters and all data is encrypted at rest and in transit.
Can you use Power Automate within Dataverse to automate the dataflows?
Yes, you can use Power Automate within Dataverse to automate actions such as running a dataflow when certain conditions are met.
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