Optimizing Power BI Performance for Enterprise Dashboards

[datapal04]

Hello,

My team and I have a project to create a suite of dashboards for our department. We're delivering these suite of dashboards as a data product through Power BI's app feature. 

The data that is used for these dashboards is held in Azure. It is extremely large - about 200K+ rows of data for 1 day (expecting 4-5 years of data). What techniques do you recommend we use to deliver these enterprise reports without performance issues, timeouts, etc.?

FYI, the data held in Azure is raw. We will hold interviews with stakeholders to gather requirements. After that, the data will be processed, aggregated, and shaped in a way to meet those requirements. Do you recommend we use Power BI features to accomplish this?

Also what workflows can we use that utilizes Microsoft Fabric?

[johnbasha33 · Accepted solution]

@datapal04 

Delivering enterprise reports with large datasets in Power BI requires careful planning and consideration of performance optimization techniques. Here are some recommendations to ensure smooth performance and reliability:

1. Data Modeling and Aggregation:
- Use data modeling techniques such as star schema or snowflake schema to optimize query performance.
- Aggregate and summarize the data at appropriate levels to reduce the number of rows and improve report responsiveness.
- Utilize Power BI's data modeling capabilities such as calculated columns, measures, and hierarchies to enhance data analysis and visualization.

2. Incremental Data Loading:
- Implement incremental data loading strategies to load only new or updated data into Power BI, reducing the overall data refresh time.
- Utilize Azure Data Factory or Azure Synapse Pipelines to orchestrate the data loading process and schedule incremental data refreshes.

3. DirectQuery vs. Import Mode:
- Evaluate whether to use DirectQuery or Import mode based on the data volume, refresh frequency, and performance requirements.
- DirectQuery allows Power BI to query the data source in real-time, while Import mode loads data into Power BI's internal data model for faster analysis.
- Consider a hybrid approach where summary/aggregated data is imported, while detailed data is queried directly from the source.

4. Data Partitioning and Compression:
- Partition large tables into smaller segments based on time periods (e.g., monthly or quarterly partitions) to improve query performance.
- Utilize table and column compression techniques to reduce storage space and optimize query execution.

5. Power BI Premium and Dedicated Capacity:
- Consider deploying the Power BI solution on Power BI Premium or dedicated capacity to take advantage of enhanced performance, larger datasets, and advanced features.
- Power BI Premium offers dedicated resources, larger dataset sizes, and enhanced refresh rates, which can improve overall performance for enterprise-scale reports.

Regarding Microsoft Fabric, it can be leveraged to automate and streamline various aspects of the Power BI development lifecycle, including data preparation, modeling, and deployment. Some potential workflows utilizing Microsoft Fabric may include:
- Automated data preprocessing and cleansing using Azure Data Factory or Azure Databricks.
- Automated data modeling and aggregation using Azure Synapse Analytics.
- Automated deployment and lifecycle management of Power BI reports and dashboards using Azure DevOps pipelines.
- Integration with Azure Monitor and Azure Log Analytics for monitoring and performance optimization of Power BI workloads.

By implementing these techniques and workflows, you can ensure that your Power BI reports deliver high performance, scalability, and reliability for your enterprise users. Additionally, conducting regular performance tuning and optimization reviews will help identify areas for improvement and ensure continued success of your Power BI solution.

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[sergej_og · Accepted solution]

Hey @datapal04 ,
sounds you have to handle IoT Data... 200k+ rows p.d.

If you would like to present these Data directly via PBI you will need to aggregate this stuff and remove useless content...and maybe disable MDX (but be careful regarding usage in Excel of those columns).
Keep your DAX simple.

Maybe using ADX (Kusto) inside of Fabric could be a good idea to solve your requirements.
So you can create PBI reports directly in Fabric (Kusto database as a source) via DirectLake mode.

Just a few ideas.
Since we don`t know your requirements.

Regards

[johnbasha33 · Accepted solution]

@datapal04 

Delivering enterprise reports with large datasets in Power BI requires careful planning and consideration of performance optimization techniques. Here are some recommendations to ensure smooth performance and reliability:

1. Data Modeling and Aggregation:
- Use data modeling techniques such as star schema or snowflake schema to optimize query performance.
- Aggregate and summarize the data at appropriate levels to reduce the number of rows and improve report responsiveness.
- Utilize Power BI's data modeling capabilities such as calculated columns, measures, and hierarchies to enhance data analysis and visualization.

2. Incremental Data Loading:
- Implement incremental data loading strategies to load only new or updated data into Power BI, reducing the overall data refresh time.
- Utilize Azure Data Factory or Azure Synapse Pipelines to orchestrate the data loading process and schedule incremental data refreshes.

3. DirectQuery vs. Import Mode:
- Evaluate whether to use DirectQuery or Import mode based on the data volume, refresh frequency, and performance requirements.
- DirectQuery allows Power BI to query the data source in real-time, while Import mode loads data into Power BI's internal data model for faster analysis.
- Consider a hybrid approach where summary/aggregated data is imported, while detailed data is queried directly from the source.

4. Data Partitioning and Compression:
- Partition large tables into smaller segments based on time periods (e.g., monthly or quarterly partitions) to improve query performance.
- Utilize table and column compression techniques to reduce storage space and optimize query execution.

5. Power BI Premium and Dedicated Capacity:
- Consider deploying the Power BI solution on Power BI Premium or dedicated capacity to take advantage of enhanced performance, larger datasets, and advanced features.
- Power BI Premium offers dedicated resources, larger dataset sizes, and enhanced refresh rates, which can improve overall performance for enterprise-scale reports.

Regarding Microsoft Fabric, it can be leveraged to automate and streamline various aspects of the Power BI development lifecycle, including data preparation, modeling, and deployment. Some potential workflows utilizing Microsoft Fabric may include:
- Automated data preprocessing and cleansing using Azure Data Factory or Azure Databricks.
- Automated data modeling and aggregation using Azure Synapse Analytics.
- Automated deployment and lifecycle management of Power BI reports and dashboards using Azure DevOps pipelines.
- Integration with Azure Monitor and Azure Log Analytics for monitoring and performance optimization of Power BI workloads.

By implementing these techniques and workflows, you can ensure that your Power BI reports deliver high performance, scalability, and reliability for your enterprise users. Additionally, conducting regular performance tuning and optimization reviews will help identify areas for improvement and ensure continued success of your Power BI solution.

Did I answer your question? Mark my post as a solution! Appreciate your Kudos !!