Scenario: You deploy a model to a Power BI workspace. You assign users to Members and Viewers roles. Everyone is happy. You later added a row-level security role and republish the model. Admins, Contributors and Members continue to view reports connected to the dataset as usual. However, Viewers report an error like the one shown below (didn’t Microsoft do an outstanding job explaining what went wrong with all of these guids?):
Analysis: Users with Administrator, Member, and Contributor permissions bypass any row-level security policies even if they assigned as role members. However, viewers are refused access unless they are added to a role that grants them the appropriate permissions. So, the likely culprit here is that there are some viewers that are not assigned to a role.
If viewers should have unrestricted access to an RLS-enabled dataset, create an Open Access role and add them to the role. As a best practice, you should create a security group and grant the group membership to the workspace and RLS.
https://prologika.com/wp-content/uploads/2016/01/logo.png00Prologika - Teo Lachevhttps://prologika.com/wp-content/uploads/2016/01/logo.pngPrologika - Teo Lachev2021-03-18 21:01:042021-05-20 17:16:46When Something Goes Wrong (Unable to Load the Model)
Amidst the COVID pandemic, the Houston Health Department (HHD) had another predicament to tackle. With lab results accumulating rapidly at one million cases per month, the vendor system they used for capturing and analyzing COVID data couldn’t keep up. In this newsletter, you’ll learn how Prologika implemented a BI solution powered by SQL Server and Power BI to solve technology challenges, and deliver fast and reliable insights.
Business Challenges
The vendor SQL Server database had large tables with normalized name-value pairs for each question and answer received from the patient, and for each investigation result. To facilitate reporting, the vendor system had scheduled ETL processes to extract data from the OLTP tables into denormalized tables. However, locking conflicts and large data volumes would crash the system and fail the ETL processes.
As a result, business analysts were not able to get timely data for reporting. HHD and the vendor tried to isolate the reporting loads by replicating the data to a reporting database but the issue with populating the denormalized tables remained
Solution
A good solution starts with a solid foundation. After assessing the current state and objectives, Prologika recommended and implemented the following architecture:
The stand-alone SQL database was replaced with an AlwaysOn availability group. Not only did this provide high availability, but it also isolated operational from reporting workloads.
In the client’s own words “we have compared the cluster server to the report server and cluster is vastly superior with regard to performance for regular queries. One simple run was 4x faster on cluster than the current report server. A much more complex run took four minutes on cluster and I stopped the same run on the report server after 87 minutes.”
Previously, data analysts would produce reports using different tools, ranging from SQL, Python, to Power BI reports. With scarce resources, HHD found it difficult to continue that path. Instead, Prologika implemented a semantic model that was hosted in Power BI.
Benefits
The new architecture and semantic model delivered the following benefits to HHD:
A single version of truth – Strategic data and business calculations were centralized on one place.
Fast insights – The Power BI reports crunching millions of rows were instantaneous.
Isolation – Report loads were isolated from the operational loads because the data was cached in the semantic model.
Standardization and centralization – Power BI became the reporting platform for delivering insights across the organization.
Teo Lachev
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics
Amidst the pandemic, the Houston Health Department (HHD) had another predicament to tackle. With lab results accumulating rapidly at one million cases per month, the vendor system they used for capturing and analyzing COVID data couldn’t keep up. The SQL Server database had large tables with normalized name-value pairs for each question and answer received from the patient, and for each investigation result. Read our case study to learn how Prologika implemented a BI solution powered by SQL Server and Power BI to help HHD gain reliable and timely insights from COVID lab results.
https://prologika.com/wp-content/uploads/2016/01/logo.png00Prologika - Teo Lachevhttps://prologika.com/wp-content/uploads/2016/01/logo.pngPrologika - Teo Lachev2021-02-26 14:19:222021-02-26 14:26:19State Health Department Gains Reliable and Rapid COVID Insights
Please join us online for the next Atlanta MS BI and Power BI Group meeting on Monday, March 1st, at 6:30 PM. Your humble correspondent will discuss the business value of semantic models and implementation options for self-service BI and organizational BI. For more details, visit our group page.
A semantic model is a layer between the data source and end user. Data analysts create self-service semantic models with Power BI Desktop or Excel. BI developers implement organizational semantic models with SSDT, Tabular Editor, and PBI Desktop. Join this session to:
· Learn what is a semantic model and how to choose between the self-service and organizational paths.
· Understand the implementation options for self-service BI models and best modeling practices.
· Find how organizational semantic models can help you achieve the “Discipline at the core, Flexibility at the Edge” tenant
· Learn how to choose a hosting platform and tool for implementing organizational semantic models.
· Learn how data analysts can extend organizational semantic models.
Speaker:
Teo Lachev is a consultant, author, and mentor, with a focus on Microsoft BI. Through his Atlanta-based company Prologika (a Microsoft Gold Partner in Data Analytics and Data Platform) he designs and implements innovative solutions that bring tremendous value to his clients. Teo has authored and co-authored several books, and he has been leading the Atlanta Microsoft Business Intelligence group since he founded it in 2010. Teo is one of the few FastTrack Recognized Solution Architects by Microsoft for Power BI in the world. Microsoft has also acknowledged Teo’s expertise and contributions to the technical community by awarding him the Microsoft Most Valuable Professional (MVP) Data Platform status for 15 years.
Prototypes without pizza:
Power BI Latest
https://prologika.com/wp-content/uploads/2016/01/logo.png00Prologika - Teo Lachevhttps://prologika.com/wp-content/uploads/2016/01/logo.pngPrologika - Teo Lachev2021-02-21 19:49:192021-02-27 13:58:50Atlanta MS BI and Power BI Group Meeting on March 1st
Please join us online for the next Atlanta MS BI and Power BI Group meeting on Monday, February 1st, at 6:30 PM. Paul Turley (MVP) will show you how to use Power Query to shape and transform data. For more details, visit our group page.
Presentation:
Preparing, shaping & transforming Power BI source data
In a business intelligence solution, data must be shaped and transformed. Your source data is rarely, if ever, going to be in the right format for analytic reporting. It may need to be consolidated into related fact and dimension tables, summarized, grouped or just cleaned-up before tables can be imported into a data model for reporting.
· Where should I shape and transform data… At the source? In Power Query, or In the BI data model?
· Where and what is Power Query? Understand how to get the most from this amazing tool and how to use it most efficiently in your environment.
· Understand Query Folding and how this affects the way you prepare, connect and interact with your data sources – whether using files, unstructured storage, native SQL, views or stored procedures.
· Learn to use parameters to manage connections and make your solution portable. Tune and organize queries for efficiency and to make them maintainable.
Speaker:
Paul (Blog | LinkedIn | Twitter) is a Principal Consultant for 3Cloud Solutions (formerly Pragmatic Works), a Mentor and Microsoft Data Platform MVP. He consults, writes, speaks, teaches & blogs about business intelligence and reporting solutions. He works with companies around the world to model data, visualize and deliver critical information to make informed business decisions; using the Microsoft data platform and business analytics tools. He is a Director of the Oregon Data Community PASS chapter & user group, the author and lead author of Professional SQL Server 2016 Reporting Services and 14 other titles from Wrox & Microsoft Press. He holds several certifications including MCSE for the Data Platform and BI.
Prototypes without pizza:
Power BI Latest
https://prologika.com/wp-content/uploads/2016/01/logo.png00Prologika - Teo Lachevhttps://prologika.com/wp-content/uploads/2016/01/logo.pngPrologika - Teo Lachev2021-01-31 09:20:382021-01-31 09:34:36Atlanta MS BI and Power BI Group Meeting on February 1st
The moment you add a calculation group to your model, Power BI sets DiscourageImplicitMeasures = True on the model. Although this property can trick you to be believe that they are still supported, you can’t create implicit measures, such as by dragging a numeric field on the report to summarize that field. That’s because implicit measures are created as inline calculations which calculation groups don’t support.
Also, there is a current issue where when you add a column from a calculation group to a filter, “Require single selection” is set to on and it can’t be changed. Therefore, you won’t be able to filter multiple calculation items, such as to present t only MTD, QTD, and YTD from a list of many items in your calculation group. As a workaround, you add a calculated column that flags the desired values and filter on it. You can vote to expedite the fix here.
I’ve noticed severe performance degradation after refreshing a Power BI Desktop model with some five million rows. The Power BI Desktop process showed a sustained 50-60 % utilization for minutes in the Windows Task Manager. I did a profiler trace and I saw expensive DAX queries like these:
As it turned out, Power BI Desktop autogenerates these queries when building a Q&A index. The 100-size limit is because Power BI wants to keep the index small. In addition, values that are longer than 100 characters are unlikely to be asked by the user. Why not check thd the maximum column value and skip the column? Power BI wants to skip instances that are too long but still index the remaining instances of the column.
To avoid this performance degradation when modeling on the desktop you could disable the Q&A feature. This will also disable smart narratives because they depend on Q&A.
To do this, go to the File, Options and Settings, Options, and turn off the Q&A option.
If Power BI Desktop is connected to a remote model, such as a published Power BI dataset, you’ll see also an option to create a local index. This option was added because Power BI needs to ask user permission to query data from remote sources, build the data index, and store it on user’s machine. By default, it’s disabled until the user explicitly turns on Q&A. For import models, as the data is already on user’s machine, Power BI doesn’t need to ask the permission to query data anymore. That’s why the option to build a local index is not applicable to models with imported data.
Disabling the Q&A in Power BI Desktop affects the local file only. When you publish the model, you reenable Q&A from the data settings if you want end users to use Q&A features. For remote models, if you leave the first option, “Turn on Q&A to ask …”, on, but disable the second option, “Create a local index….”, and publish the model to the service, then Q&A will be enabled in the service by default. That is, you don’t have to go to dataset settings to enable Q&A for that model. For import models, you have to disable the first option, and then after publishing the model to the service, you have to go to dataset settings to enable Q&A there.
Please join us online for the next Atlanta MS BI and Power BI Group meeting on Monday, January 4th, at 6:30 PM. James Serra (Big Data/Data Warehouse Evangelist at Microsoft) will share best practices around staging data in an organizational data lake. For more details, visit our group page and don’t forget to RSVP (fill in the RSVP survey if you’re planning to attend).
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Speaker:
James Serra is a big data and data warehousing solution architect at Microsoft. He is a thought leader in the use and application of Big Data and advanced analytics. Previously, James was an independent consultant working as a Data Warehouse/Business Intelligence architect and developer. He is a prior SQL Server MVP with over 35 years of IT experience. James is a popular blogger (JamesSerra.com) and speaker. He is the author of the book “Reporting with Microsoft SQL Server 2012”.
Prototypes without pizza:
Power BI Latest
https://prologika.com/wp-content/uploads/2016/01/logo.png00Prologika - Teo Lachevhttps://prologika.com/wp-content/uploads/2016/01/logo.pngPrologika - Teo Lachev2020-12-31 08:47:562021-02-16 01:50:24Atlanta MS BI and Power BI Group Meeting on January 4th
Please join us online for the next Atlanta MS BI and Power BI Group meeting on Monday, December 7th, at 6:30 PM. Patrick LeBlanc (A Guy in the Cube) will share techniques to optimize your Power BI data models. For more details, visit our group page and don’t forget to RSVP (fill in the RSVP survey if you’re planning to attend).
When working with your Power BI Data Model/Dataset there are certain that can be done to optimize the size of the model. With that, there are certain thing that can be done that wreaks havoc on your Data Model. In this session we will walk you through several things that can be done to ensure that your data model is optimize for the best performance. We will discuss and demonstration how items such as data types, model properties, and DAX calculations and adversely affect the size of the model. That’s just a small list of items, join the meeting to learn all the tips and tricks.
Speaker:
Patrick LeBlanc is a currently a Principal Program Manager at Microsoft and a contributing partner to Guy in a Cube. Along with his 15+ years’ experience in IT he holds a Masters of Science degree from Louisiana State University. He is the author and co-author of five SQL Server books. Prior to joining Microsoft he was awarded Microsoft MVP award for his contributions to the community. Patrick is a regular speaker at many SQL Server Conferences and Community events.
Prototypes without pizza:
“Power BI Latest” by Teo Lachev
https://prologika.com/wp-content/uploads/2016/01/logo.png00Prologika - Teo Lachevhttps://prologika.com/wp-content/uploads/2016/01/logo.pngPrologika - Teo Lachev2020-12-03 14:43:442021-02-17 01:01:43Atlanta MS BI and Power BI Group Meeting on December 7th
In its early days, Power BI introduced an endpoint to support Analyze in Excel (AIXL). Later, the “Power BI datasets” connector relied on this endpoint to support connecting to published datasets. The AIXL endpoint was never intended to support other clients. It has a few limitations, such as it doesn’t support long running requests and write operations. Also, it doesn’t support importing data as you’ve probably found when connecting to published datasets.
Later, Power BI Premium added the XMLA endpoint to support external clients connected to datasets in a premium capacity. If you’re on Premium, you should use the XMLA endpoint to connect external clients by using the Azure Analysis Services connector. Unfortunately, if you do so in PBI Desktop, “Get Data” won’t give you the nice UX that shows you which datasets are certified.
It will be nice if Microsoft adds a mechanism in the future where the client libraries will automatically take the new style of connection string (workspace + dataset name) and redirect to the AIXL endpoint if the dataset is hosted in a shared capacity or the XMLA endpoint if the dataset is in Premium. Or even better, support the XMLA endpoint for both shared and premium capacities.
In summary:
AIXL and PBI Desktop use the AIXL connections.
You should never use AIXL connection strings directly.
Business users should continue using the “Power BI datasets” connector.
Other external clients should use the XMLA endpoint to connect to a dataset in a premium capacity.
https://prologika.com/wp-content/uploads/2016/01/logo.png00Prologika - Teo Lachevhttps://prologika.com/wp-content/uploads/2016/01/logo.pngPrologika - Teo Lachev2020-11-29 09:54:212020-11-29 09:54:21Understanding Power BI Endpoints
