thanks for the videos ...! how can we decide the connectivity type (Import or Direct Query). I know Based on data size Ex. Fetching data from sql server. In this scenario How can we decide which mode is good to connect the source and PBI Desktop
Choosing Between Import and DirectQuery in Power BI Import: Suitable for smaller datasets, infrequent updates, and complex transformations. DirectQuery: Suitable for large datasets, frequent updates, and simple queries. Factors to Consider for SQL Server Data: Data Volume: DirectQuery for large datasets. Query Complexity: Import for complex queries. Data Refresh Frequency: DirectQuery for frequent updates. Performance: Consider database optimization and indexes.
madam I have scenario,Excel data is coming with day frequency,sharepoint is with weekly and sql with monthly ,Interviwer asked me how do you fetch that dta into power BI,How do you refresh,all data you need to fetch for a one report
Fetching Data from Excel, SharePoint, and SQL into Power BI with Different Frequencies Understanding the Scenario: Excel: Daily frequency SharePoint: Weekly frequency SQL: Monthly frequency Fetching Data into Power BI: Connect to Data Sources: Excel: Use the Excel connector in Power BI to connect to your Excel file. SharePoint: Use the SharePoint Online connector to connect to your SharePoint list or library. SQL: Use the SQL Server connector to connect to your SQL database. Create Data Models: Import or DirectQuery: Decide whether to import the data into Power BI or use DirectQuery. Import is suitable for smaller datasets, while DirectQuery is better for larger datasets or when real-time data is required. Relationships: If you have data from multiple sources, create relationships between the tables to enable data blending and analysis. Refreshing Data: Scheduled Refresh: Set up scheduled refreshes for each data source to automatically update the data in your Power BI report. You can schedule refreshes daily, weekly, or monthly based on the frequency of your data sources. Manual Refresh: You can also manually refresh the data by going to the "Refresh" section in the Power BI service or desktop application. Fetching All Data for a Report: Dataflows: Consider using Power BI dataflows to create and manage data pipelines. Dataflows can extract, transform, and load data from various sources, including Excel, SharePoint, and SQL. You can then use the dataflows in your Power BI reports. Querying Data: Use DAX (Data Analysis Expressions) to query and transform the data in your Power BI model. You can create calculated columns, measures, and tables to derive insights from your data. Additional Considerations: Data Volume: If you're dealing with large datasets, optimize your data model and queries to improve performance. Data Quality: Ensure that the data in your sources is clean and consistent before importing it into Power BI. Data Security: Implement appropriate security measures to protect your data, such as row-level security and data sensitivity labels.
Thank you 🙏
Please send different scenarios for practice before interview.
sure will do more
Thank you.
You're welcome!
thanks for the videos ...!
how can we decide the connectivity type (Import or Direct Query). I know Based on data size Ex. Fetching data from sql server. In this scenario How can we decide which mode is good to connect the source and PBI Desktop
Choosing Between Import and DirectQuery in Power BI
Import: Suitable for smaller datasets, infrequent updates, and complex transformations.
DirectQuery: Suitable for large datasets, frequent updates, and simple queries.
Factors to Consider for SQL Server Data:
Data Volume: DirectQuery for large datasets.
Query Complexity: Import for complex queries.
Data Refresh Frequency: DirectQuery for frequent updates.
Performance: Consider database optimization and indexes.
Please make Client round interview please
sure, its available in our channel
Can you please make on complex dax queries and cross filter and many to many relationship in real time scenarios
sure, will do on that, Thank you so much, Please subscribe our channel for regular updates
madam I have scenario,Excel data is coming with day frequency,sharepoint is with weekly and sql with monthly ,Interviwer asked me how do you fetch that dta into power BI,How do you refresh,all data you need to fetch for a one report
Fetching Data from Excel, SharePoint, and SQL into Power BI with Different Frequencies
Understanding the Scenario:
Excel: Daily frequency
SharePoint: Weekly frequency
SQL: Monthly frequency
Fetching Data into Power BI:
Connect to Data Sources:
Excel: Use the Excel connector in Power BI to connect to your Excel file.
SharePoint: Use the SharePoint Online connector to connect to your SharePoint list or library.
SQL: Use the SQL Server connector to connect to your SQL database.
Create Data Models:
Import or DirectQuery: Decide whether to import the data into Power BI or use DirectQuery. Import is suitable for smaller datasets, while DirectQuery is better for larger datasets or when real-time data is required.
Relationships: If you have data from multiple sources, create relationships between the tables to enable data blending and analysis.
Refreshing Data:
Scheduled Refresh: Set up scheduled refreshes for each data source to automatically update the data in your Power BI report. You can schedule refreshes daily, weekly, or monthly based on the frequency of your data sources.
Manual Refresh: You can also manually refresh the data by going to the "Refresh" section in the Power BI service or desktop application.
Fetching All Data for a Report:
Dataflows: Consider using Power BI dataflows to create and manage data pipelines. Dataflows can extract, transform, and load data from various sources, including Excel, SharePoint, and SQL. You can then use the dataflows in your Power BI reports.
Querying Data: Use DAX (Data Analysis Expressions) to query and transform the data in your Power BI model. You can create calculated columns, measures, and tables to derive insights from your data.
Additional Considerations:
Data Volume: If you're dealing with large datasets, optimize your data model and queries to improve performance.
Data Quality: Ensure that the data in your sources is clean and consistent before importing it into Power BI.
Data Security: Implement appropriate security measures to protect your data, such as row-level security and data sensitivity labels.