7 tips and tricks for connecting power bi to obiee and oracle analytics cloud

Editor's Notes

• #2: Welcome to the Webinar. Today, we will talk about how to efficiently integrate Power BI to your existing OBIEE data warehouse.   Users who are adopting Power BI as the visual analysis tool quickly realize that connecting it to their OBIEE data is not so easy.   In this webinar, we will discuss the techniques followed by organizations that have successfully integrated Power BI with their OBIEE environment.
• #3: Welcome to the Webinar. Today, we will talk about how to efficiently integrate Power BI to your existing OBIEE data warehouse.   Users who are adopting Power BI as the visual analysis tool quickly realize that connecting it to their OBIEE data is not so easy.   In this webinar, we will discuss the techniques followed by organizations that have successfully integrated Power BI with their OBIEE environment.
• #4: Let us start by looking at the top reasons for connecting Power BI to OBIEE End users find Power BI much easier to use compared to OBIEE.   If they need to combine OBIEE data with other data sources, the time to visualization is much shorter. Sometimes, it could be simply because their company has chosen Power BI as the standard tool for visualization.   But, if OBIEE is the existing BI platform, users spend 4 to 5 hours per week on average to export, import, and wrangle the OBIEE data into Power BI. Conservatively, the productivity loss is $10,000 per year per user.
• #5: Let us start by looking at the top reasons for connecting Power BI to OBIEE End users find Power BI much easier to use compared to OBIEE.   If they need to combine OBIEE data with other data sources, the time to visualization is much shorter. Sometimes, it could be simply because their company has chosen Power BI as the standard tool for visualization.   But, if OBIEE is the existing BI platform, users spend 4 to 5 hours per week on average to export, import, and wrangle the OBIEE data into Power BI. Conservatively, the productivity loss is $10,000 per year per user.
• #6: Let us start by looking at the top reasons for connecting Power BI to OBIEE End users find Power BI much easier to use compared to OBIEE.   If they need to combine OBIEE data with other data sources, the time to visualization is much shorter. Sometimes, it could be simply because their company has chosen Power BI as the standard tool for visualization.   But, if OBIEE is the existing BI platform, users spend 4 to 5 hours per week on average to export, import, and wrangle the OBIEE data into Power BI. Conservatively, the productivity loss is $10,000 per year per user.
• #7: The IT teams, on the other hand, have invested millions of dollars in OBIEE.   If the IT teams have to duplicate the OBIEE semantic model, business logic, and aggregations in Power BI,  it would cost a minimum of 250,000 dollars for each subject area in time and material. So, reusing the data warehouse that you have already built is a prudent approach.
• #8: Let us start by looking at the top reasons for connecting Power BI to OBIEE End users find Power BI much easier to use compared to OBIEE.   If they need to combine OBIEE data with other data sources, the time to visualization is much shorter. Sometimes, it could be simply because their company has chosen Power BI as the standard tool for visualization.   But, if OBIEE is the existing BI platform, users spend 4 to 5 hours per week on average to export, import, and wrangle the OBIEE data into Power BI. Conservatively, the productivity loss is $10,000 per year per user.
• #9: When you connect from Power BI to OBIEE as a data source,  you will face some unique challenges. First of all, it is not fair to compare OBIEE to a data source such as Excel or SQL Server.    OBIEE is a lot more complex system.  It is a complete BI solution where the data volumes are huge.  It has a well defined data warehouse schema with complex joins and aggregations.  On top of that, hundreds of users could be running intensive reports. The query performance is often unpredictable and depends on the system load.
• #10: When you connect from Power BI to OBIEE as a data source,  you will face some unique challenges. First of all, it is not fair to compare OBIEE to a data source such as Excel or SQL Server.    OBIEE is a lot more complex system.  It is a complete BI solution where the data volumes are huge.  It has a well defined data warehouse schema with complex joins and aggregations.  On top of that, hundreds of users could be running intensive reports. The query performance is often unpredictable and depends on the system load.
• #11: Let us start by looking at the top reasons for connecting Power BI to OBIEE End users find Power BI much easier to use compared to OBIEE.   If they need to combine OBIEE data with other data sources, the time to visualization is much shorter. Sometimes, it could be simply because their company has chosen Power BI as the standard tool for visualization.   But, if OBIEE is the existing BI platform, users spend 4 to 5 hours per week on average to export, import, and wrangle the OBIEE data into Power BI. Conservatively, the productivity loss is $10,000 per year per user.
• #12: A quick introduction to BI Connector BI Connector was launched in 2015.  It was specifically designed for large and medium sized organizations to integrate Tableau, Power BI and Qlik to OBIEE and Oracle Analytics Cloud (OAC). We are based in San Francisco Bay Area. We are a technology partner of  Microsoft, Tableau, and Oracle and we work closely with the tool vendors in the ongoing development of BI Connector.
• #13: Let us start by looking at the top reasons for connecting Power BI to OBIEE End users find Power BI much easier to use compared to OBIEE.   If they need to combine OBIEE data with other data sources, the time to visualization is much shorter. Sometimes, it could be simply because their company has chosen Power BI as the standard tool for visualization.   But, if OBIEE is the existing BI platform, users spend 4 to 5 hours per week on average to export, import, and wrangle the OBIEE data into Power BI. Conservatively, the productivity loss is $10,000 per year per user.
• #14: So what tips and tricks you can use to efficiently connect Power BI to OBIEE?   Based on our extensive experience in working with a number of enterprise customers who are using OBIEE with modern visualization tools,  we found 7 simple tips and tricks that will make it easy for you to visualize OBIEE data in Power BI. The seven tips and tricks are: Know your OBIEE Start with Reports Choose the right connection type Use Filters Use GZID for Subject Areas Fine tune for long running and large queries Implement data security in cloud
• #15: Let us start with the first basic tip - Know your OBIEE A common problem that we have seen is that the Power BI users who are most eager to access OBIEE data often have limited to no knowledge of what OBIEE is, how it works, what the subject areas do, and how OBIEE performs. While BI Connector has made it simple to access OBIEE subject areas and reports, the complexity of OBIEE is still very relevant. Just dragging and dropping columns in Power BI without a clear understanding of the subject areas could easily lead to frustration. So, what we recommend is that the initial set of users who are connecting Power BI with OBIEE should be super users of both OBIEE and Power BI.  They need to know the OBIEE model and the data that they are planning to access. We have published a white paper: How to get started with BI Connector Evaluation that discusses the need for forming a core team that identifies the right use cases for integrating Power BI to OBIEE.  I strongly recommend that you review that white paper before you embark on this journey. Once you identify your core team and use cases, for the first few Power BI reports, you should keep the Power BI and OBIEE side by side.   Run the report or the analysis first in OBIEE.  Understand the subject area, folders, columns, filters and total number of records that are in the report.  Note down the query performance when fetching the entire data for the report, not just the first 75 rows displayed in the first page of OBIEE.   Next, connect to that report/analysis in the same environment from Power BI using BI Connector.  Create the visualizations and compare the results in Power BI with OBIEE report.
• #16: The second tip is to start with OBIEE reports.  When you connect from Power BI to OBIEE, you have two options: Connect to OBIEE Reports Connect to Subject Areas. For Power BI, we recommend starting with OBIEE reports since it is the easiest of two.  You would be reusing the existing OBIEE reports and there is no need to rebuild the aggregations in Power BI.  Even if a dataset that you need doesn’t exist as a report in OBIEE, we recommend creating it first in OBIEE, then connect to it from Power BI and access it as a table.   If you are wondering why we are not recommending connecting to Subject Areas directly, hold on for a few minutes and we will discuss it later in this presentation. 
• #17: The third tip is to choose the right connection type for your analysis.   When you use BI Connector, there are two connection types: DirectQuery and Import. If your OBIEE queries and reports perform well, that is, data is fetched within a reasonable time period, and if you are looking to minimize the data duplicated into Power BI, we recommend that you use DirectQuery.  DirectQuery offers a live online query access to OBIEE data. On the other hand, if your OBIEE queries and reports are slow and if you would like to use Power BI’s in-memory database for faster visualization, you should consider import option.  This will bring the OBIEE data into Power BI and allow you to analyze the data offline. In either case, when the connection type for your OBIEE data source is different from the connection type for your other data sources, and if you are bringing them together in a single report, be sure to verify that the composite model option works for you.  Otherwise, you have to use the same connection type for all your data sources. 
• #18: The fourth tip is “Use filters” This is a very important topic. If you don’t manage the volume of data that you are querying from OBIEE, your reports could be running for several minutes, and sometimes your queries could also bring the OBIEE system to its knees. So, if you are using DirectQuery, it is important to use filters before you start visualizing the data, ie., before you drag and drop the fields into the report canvas.     On the other hand, if you are using Import, it is important to apply filters before you load the data into Power BI.  Otherwise, you could loading millions of records into Power BI and your query could be running for several minutes to hours.   So, always use filters such as time filters or other dimension filters. 
• #19: We saw how to connect to OBIEE Reports. What if you would like to create a new report from scratch directly from OBIEE Subject Areas?   You can definitely do that, but,  because of the way Power BI works, connecting to Subject Area requires a few extra steps. We recommend this approach only for OBIEE super users who understand the subject areas very well. So, here is what you need to do:  If you are using DirectQuery, you need to create a relationship between the fact table and the dimension tables using the field GZID, which is virtual id created by BI Connector.  Defining this simple relationship does a few things: One: You don’t need to recreate the OBIEE joins in Power BI, but rather reuse the existing joins Two:  You don’t need to expose any primary key - foreign key relationships in the OBIEE presentation layer. Three: Power BI will send a single query to Fact & dimension tables instead of sending multiple queries to the Subject area.   When you create the relationship, you should always set the cardinality to Many to One from Fact to Dimension table.   And, set the cross filter direction to Single.  
• #20: Next, If you are using Import instead of DirectQuery, you need to merge the fact table and the dimension tables into a single flat table before you load the data. If you fail to do this, Power BI will send separate queries to fact and dimension tables and you would not be able to join the results in Power BI. So, you need to use Merge Queries feature in Power Query to merge the tables into a single table.  Use GZID as the joining field. Set the join kind to Left Outer. We also recommend that you apply filters and also remove the fields that you really don’t for your report.  This will reduce the data volume significantly. Once again, keep in mind, we recommend this approach for OBIEE supers users only.  
• #21: Now, let’s discuss how to handle challenging reports which are either long running or fetch large data volumes. First one is long running queries.  For example, if you are fetching financial data or support ticket data for multiple years,  depending on the load on your OBIEE system, the query could sometimes take more than 10 minutes to complete.   If you notice that the query returns without any data after running for several minutes, then the culprit could be the default time-outs.  The first step is to run the report in OBIEE and check the time it takes to complete. If it is more than 8 minutes, then adjust the http time out parameter in BI Connector settings to a higher number, say 30 minutes.   Even after adjusting this parameter, you notice “Query cancelled” message in the BI Connector log file, then you should check with your OBIEE administrator for query time-out settings in OBIEE.  It is quite likely that there are some default values in OBIEE that is automatically cancelling the query after a certain period of time. The next scenario is if you are querying large volumes of data.  Data volume is a combination of number of fields and number of rows.  Your OBIEE system is sized to export a certain volume of data. If the data being queried exceeds this value, OBIEE will automatically error out.   So, if you see a C-runtime error in OBIEE query log or in BI Connector log file, you have a few options: Reduce the number of fields that you are querying.  Pick only those fields that you absolutely need. Reduce the number of records per fetch so that BI Connector fetches fewer records per fetch and makes multiple fetches to complete the report.   Verify the maximum number of records per report. You should also check the memory allocation in your OBIEE system.     You need to configure these values based on your requirement and OBIEE environment.
• #22: We have come to the final best practice which involves data governance in the Power BI service.   When you publish OBIEE data to Power BI Service in the cloud, the OBIEE security model is not automatically carried over to the data connection or the published dataset.  Power BI currently doesn’t support a Cloud user to login to OBIEE using their own OBIEE credential. Therefore, dataset is published using a common service account, and, just like any other Power BI data set, you should define security and governance model for who can see this data.
• #23: That wraps up the list of tips and tricks.  Let us do a quick recap: Know your OBIEE.  Form a core team consisting of OBIEE super users and Tableau super users. Connect to reports first.  this will jumpstart your visualization Choose the right connection type, whether it is direct query or import, for the right use case. Always use filters to manage data volume When connecting to Subject areas, Use GZID to create relationship between fact and dimension tables If your query fails to return any dataset, check query timings and data volume.  Adjust BI Connector and OBIEE parameters based on your requirement. Finally, when publishing OBIEE data to the cloud, be sure to define data security and governance model just like any other Power BI dataset. 
• #24: Let us start by looking at the top reasons for connecting Power BI to OBIEE End users find Power BI much easier to use compared to OBIEE.   If they need to combine OBIEE data with other data sources, the time to visualization is much shorter. Sometimes, it could be simply because their company has chosen Power BI as the standard tool for visualization.   But, if OBIEE is the existing BI platform, users spend 4 to 5 hours per week on average to export, import, and wrangle the OBIEE data into Power BI. Conservatively, the productivity loss is $10,000 per year per user.
• #25: By following these simple best practices, you can simplify your transition from OBIEE to Power BI for visualization.   The important benefit is that you can eliminate hours and days of manual, error-prone export-import of OBIEE data into Power BI.  
• #26: If you are an IT manager, you save time and money by reusing OBIEE subject areas and reports. Connecting Power BI to OBIEE eliminates the need for costly, time-consuming re-engineering and testing. Overall, both end users and IT benefit from the productivity and cost savings of connecting Power BI to OBIEE.
• #27: Thank you very much for attending the webinar. We’ll send over your 45 day free trial license key via email. As mentioned in the beginning, we’ll share the link to the webinar in email for your future reference. If you have any more questions please contact us through the BI Connector Q&A forum. If you are interested in a free trial download, please visit https://biconnector.com.