What are the different refresh options at Data Source(s) to overcome Power BI Refresh Limitation

To overcome Power BI refresh limitations at the data source level, you can employ various strategies to ensure that data is updated and available for analysis. Here are different refresh options at the data source(s) level:

1. Change Data Capture (CDC):
• Implement Change Data Capture mechanisms at the source database to capture and track changes to the data.
• Use CDC to identify new or modified data since the last refresh and only refresh the incremental changes in Power BI.
2. Scheduled Extract, Transform, Load (ETL) Jobs:
• Schedule periodic ETL jobs to extract data from source systems, transform it as needed, and load it into Power BI.
• Use ETL tools such as Azure Data Factory, SSIS, or custom scripts to automate data extraction and loading processes.
3. Data Replication:
• Replicate data from source systems to a staging or data warehouse environment using replication tools or techniques.
• Refresh data in Power BI from the staging or data warehouse environment, which can have more predictable and controlled refresh processes.
4. Streaming Data:
• Stream data directly from source systems using real-time data streaming technologies such as Kafka, Azure Event Hubs, or AWS Kinesis.
• Power BI can connect to streaming data sources to provide real-time insights without relying on batch refreshes.
5. DirectQuery Mode:
• Configure Power BI datasets to use DirectQuery mode, where queries are sent directly to the underlying data source in real-time.
• DirectQuery mode ensures that users always access the latest data without the need for scheduled refreshes.
6. API Integration:
• Integrate Power BI with source systems using APIs to retrieve data on-demand or at scheduled intervals.
• Implement custom connectors or use existing connectors provided by Power BI to connect to APIs and fetch data.
7. Web Scraping and Web Data Connector:
• Use web scraping techniques or Power BI's Web Data Connector to extract data from web sources and refresh it periodically.
• Web scraping can be useful for accessing data from websites or online databases that do not provide APIs.
8. Manual Data Import:
• Manually import data into Power BI using CSV or Excel files when automated refresh options are not feasible.
• Although less efficient, manual data import can be useful for occasional or one-time data refreshes.

By leveraging these refresh options at the data source(s) level, you can ensure that data in Power BI remains up-to-date and relevant for analysis, thereby overcoming refresh limitations imposed by Power BI licensing or resource constraints.

 

 

Data Refresh Limitations in Power BI

Data refresh in Power BI is subject to certain limitations, particularly in terms of frequency, duration, and the number of refreshes allowed per day. These limitations can vary depending on your Power BI licensing model. Here are some key considerations:

1. Data Refresh Frequency:
• Power BI Pro users typically have a limit on the number of daily refreshes they can perform. By default, this limit is 8 refreshes per day for datasets in shared capacity, and 48 refreshes per day for datasets in dedicated capacity (Premium).
• Power BI Premium users may have higher refresh frequency limits, depending on the capacity SKU.
2. Data Refresh Duration:
• Each data refresh operation is subject to a maximum duration, which can vary based on your licensing model and the underlying data source.
• Power BI Pro users may have shorter refresh durations compared to Premium users. For example, a single refresh operation might be limited to 30 minutes for Pro users, while Premium users may have longer refresh durations.
3. Concurrent Refreshes:
• Power BI limits the number of concurrent refresh operations that can be performed on a dataset. This limitation is more relevant in shared capacity environments where resources are shared among multiple users.
• Power BI Premium offers higher concurrency limits compared to Power BI Pro.
4. Scheduled Refresh:
• Scheduled refresh allows you to automate the refresh process at specific intervals (e.g., daily, weekly). The frequency of scheduled refreshes may be subject to the limitations mentioned above.
5. On-Demand Refresh:
• On-demand refresh allows users to manually trigger a refresh operation. The number of on-demand refreshes allowed per day may be subject to the same limitations as scheduled refreshes.
6. DirectQuery and Live Connections:
• DirectQuery and live connections to certain data sources may have additional limitations on data refresh, query complexity, and performance.
7. API Refresh:
• Power BI provides APIs for programmatically triggering data refresh operations. The frequency and limitations of API refresh may align with the constraints mentioned above.

It's essential to review the specific limitations and capabilities associated with your Power BI licensing model and plan your data refresh strategy accordingly. Regular monitoring of refresh operations and resource usage can help ensure that your datasets remain up-to-date and accessible to users.

Limitations in Power BI dataset

Power BI datasets have several limitations that users should be aware of when designing and working with their data models. Here are some of the key limitations:

1. Maximum Size: The size of a dataset in Power BI is limited, depending on the licensing model you're using:
• For Power BI Pro users, the maximum size is typically 1 GB per dataset.
• With Power BI Premium, the maximum size can range from 10 GB to 400 GB per dataset, depending on the Premium capacity SKU.
2. Data Refresh: Power BI has limitations on data refresh frequency and duration, especially for Power BI Pro users. Users are limited in the number of times they can refresh data per day, and the duration of each refresh is also capped.
3. Data Volume: While Power BI can handle large volumes of data, extremely large datasets can impact performance. It's essential to optimize your data model and consider partitioning strategies for better performance.
4. DirectQuery and Live Connections: When using DirectQuery or live connections to data sources, there are limitations on the complexity of queries and the size of the dataset that can be efficiently queried.
5. Data Types and Relationships: Power BI has limitations on the number of rows and columns in a dataset, as well as the number of relationships between tables. Understanding these limitations is crucial when designing your data model.
6. Data Sources: While Power BI supports a wide range of data sources, some data sources may have limitations or restrictions on connectivity and data retrieval.
7. Row-level Security: Power BI has limitations on the number of row-level security (RLS) rules that can be applied to a dataset. Users should be mindful of these limitations when implementing RLS in their reports.
8. Performance: Large datasets and complex data models can impact query performance and report rendering times. Users should optimize their data models and reports to ensure optimal performance.

It's important to review the latest documentation from Microsoft and consider the specific requirements of your project when working with Power BI datasets. Regular monitoring and optimization of your datasets can help mitigate potential limitations and ensure a smooth user experience.

 

What is the max dataset size a Power BI can handle

Consuming data from public websites and integrating it into Power BI involves a few steps, typically using Power BI's web scraping capabilities or leveraging other methods such as APIs or data connectors. Here's a general process:

1. Identify the Data Source:
• Determine which website(s) you want to scrape data from.
• Ensure that the website allows web scraping or provides an API for accessing the data.
2. Web Scraping with Power BI:
• Power BI has a built-in web scraping feature that allows you to extract data from HTML tables on web pages.
• In Power BI Desktop, you can use the "Get Data" option and select "Web" as the data source.
• Enter the URL of the webpage containing the data you want to scrape.
• Power BI will analyze the page structure and provide a preview of the data it can extract.
• You can then select the specific tables or elements you want to import into Power BI.
3. API Integration:
• If the website provides an API for accessing data, you can use Power BI's native connectors or custom connectors to connect to the API.
• In Power BI Desktop, use the "Get Data" option and select the appropriate API connector (e.g., Web API, REST API, JSON, etc.).
• Enter the API endpoint URL and any required authentication credentials.
• Power BI will retrieve data from the API and allow you to transform and visualize it within your reports.
4. Data Connectors:
• Power BI offers a wide range of data connectors for integrating with various data sources.
• Check if there is a specific connector available for the website you want to pull data from.
• If a connector is available, you can use it to establish a connection and import data directly into Power BI.
5. Data Transformation:
• Once you've imported the data into Power BI, you may need to perform data transformation and cleansing to prepare it for analysis.
• Power BI provides a range of transformation tools and functions for cleaning, shaping, and modeling your data.
6. Schedule Data Refresh (Optional):
• If you want to keep your Power BI reports up-to-date with the latest data from the website, you can schedule a data refresh.
• Configure a refresh schedule in the Power BI service to periodically retrieve and update the data.
7. Build Visualizations and Reports:
• With the data imported into Power BI, you can now create visualizations, dashboards, and reports to analyze and present the information.

By following these steps, you can effectively consume data from public websites and integrate it into Power BI for analysis and reporting. Make sure to respect the terms of service and data usage policies of the websites you're scraping data from, and be mindful of any rate limits or access restrictions they may impose.

 

How to consume data from public website(s) to Power BI

Consuming data from public websites and integrating it into Power BI involves a few steps, typically using Power BI's web scraping capabilities or leveraging other methods such as APIs or data connectors. Here's a general process:

1. Identify the Data Source:
• Determine which website(s) you want to scrape data from.
• Ensure that the website allows web scraping or provides an API for accessing the data.
2. Web Scraping with Power BI:
• Power BI has a built-in web scraping feature that allows you to extract data from HTML tables on web pages.
• In Power BI Desktop, you can use the "Get Data" option and select "Web" as the data source.
• Enter the URL of the webpage containing the data you want to scrape.
• Power BI will analyze the page structure and provide a preview of the data it can extract.
• You can then select the specific tables or elements you want to import into Power BI.
3. API Integration:
• If the website provides an API for accessing data, you can use Power BI's native connectors or custom connectors to connect to the API.
• In Power BI Desktop, use the "Get Data" option and select the appropriate API connector (e.g., Web API, REST API, JSON, etc.).
• Enter the API endpoint URL and any required authentication credentials.
• Power BI will retrieve data from the API and allow you to transform and visualize it within your reports.
4. Data Connectors:
• Power BI offers a wide range of data connectors for integrating with various data sources.
• Check if there is a specific connector available for the website you want to pull data from.
• If a connector is available, you can use it to establish a connection and import data directly into Power BI.
5. Data Transformation:
• Once you've imported the data into Power BI, you may need to perform data transformation and cleansing to prepare it for analysis.
• Power BI provides a range of transformation tools and functions for cleaning, shaping, and modeling your data.
6. Schedule Data Refresh (Optional):
• If you want to keep your Power BI reports up-to-date with the latest data from the website, you can schedule a data refresh.
• Configure a refresh schedule in the Power BI service to periodically retrieve and update the data.
7. Build Visualizations and Reports:
• With the data imported into Power BI, you can now create visualizations, dashboards, and reports to analyze and present the information.

By following these steps, you can effectively consume data from public websites and integrate it into Power BI for analysis and reporting. Make sure to respect the terms of service and data usage policies of the websites you're scraping data from, and be mindful of any rate limits or access restrictions they may impose.

How to give access to external user on Power BI Report

To give access to an external user in Power BI, you typically have a few options, depending on your requirements and the level of access you want to grant:

1. Power BI Sharing:
• You can share individual reports, dashboards, or entire apps directly from Power BI.
• To share, open the report/dashboard you want to share, click on the "Share" button, and enter the email address of the external user.
• You can specify whether the external user should have edit or view-only access.
• The external user will receive an email invitation with a link to access the shared content.
2. Publish to Web:
• If you want to make a report publicly accessible without requiring authentication, you can use the "Publish to web" feature.
• This generates a public URL that anyone with the link can access.
• Be cautious when using this feature as it makes your data publicly accessible and should only be used for non-sensitive information.
3. Power BI Embedded:
• If you want to embed Power BI content into a custom application or website and give access to external users, you can use Power BI Embedded.
• With Power BI Embedded, you have more control over the user experience and can manage access programmatically.
• This option requires more technical expertise and involves additional development effort.
4. Power BI Apps:
• You can package related dashboards, reports, and datasets into an "app" and share it with users within your organization or externally.
• Power BI apps allow you to group related content together and manage access more efficiently.

When sharing content with external users, consider the sensitivity of the data and the security implications. Make sure to follow best practices for data privacy and compliance. Additionally, ensure that external users understand any limitations or restrictions on the data they're accessing.