In each project, we must exercise an appropriate degree of discipline. It is hard to prescribe a set of rules that always works for every project with the right dose of balance. A certain degree of freedom is necessary to learn-as-we-go and produce masterful analytic reports. At the same time, the process requires a well-defined plan, time-boxing and prioritizing of features. When I started writing this post, I was working on a consulting project that started out with the dataset and report in two separate files.
In theory, this is typically the right approach. The requirements for this project expanded and we found ourselves in a rapid iteration cycle, needing to make model, measures and report changes.
Kasper De Jonge walks through the process in this blog post from May, In cases where you have crisp requirements and a clear division of labor; you can start with two files to delineate the data model and the report. In cases where you are actively prototyping and working alone, developing a single PBIX file containing all the parts will allow you to move quickly. When it becomes more important to lock things down and manage releases than to keep iterating, move report pages to a separate PBIX file that is connected to the published dataset in the service.
This simple tool allows you to start a project with separate data model and report files and then to swap the live connection between copies of the model: either a dataset published to the Power BI Service or to a PBIX file on the local desktop using a local TCP port. Hot Swap is a community-developed external tool that is not officially supported by Microsoft but my experience in testing the tool thus far has been very good.
I used an earlier solution from Steve in a project about a year ago that worked-out quite well. It was a set of PowerShell scripts that rewired connects in much the same way.
Having this capability in a simple pop-up dialog is an even more convenient option. The new approach is simple: begin your project with separate data model and report files. You can publish an early version of the data model file to a development workspace in the service and connect to establish a live-connected report.
Then, run the Hot Swap tool and switch the connection to a local copy of the your data model to continue iterative development. Publish changes whenever you need to and then you can use Hot Swap to remove the local connection. Regardless, it is a simple process. For small, informal projects developed by one person or one developer at a time , keeping the data model and report in one file is convenient and easier to make changes as you go.
For moderate-scale and formalized Power BI projects, begin with two separate PBIX files or make it a point to separate the data model from the report file before you deploy to the Power BI service. Consider using the Hot Swap tool to help manage local mode development. Part 1 is here. We often have users of a business intelligence solution tell us that they have found a discrepancy between the numbers in a Power BI report and a report produced by their line-of-business LOB system, which they believe to be the correct information.
Using the LOB reports as a data source for Power BI is usually not ideal because at best, we would only reproduce the same results in a different report. We typically connect to raw data sources and transform that detail data, along with other data sources with historical information to analyze trends, comparisons and ratios to produce more insightful reports. However, if the LOB reports are really the north star for data validation, these can provide an effective means to certify that a BI semantic model and analytic reports are correct and reliable.
This is a pattern that I have applied for consulting customers. The data model is large and complex with sophisticated reports used monthly by hundreds of users. The dataset is setup with a scheduled refresh. I have one consulting client who use an automation tool to essentially open the LOB application, punch in the right keystrokes and export the report to a file at the end of every month.
The second data source for the validation report imports the validation files that were exported from the LOB report. This diagram depicts the validation solution at a high level. That data is processed through rigorous ETL pipelines and contains data extracted from multiple systems used by the corporation.
A lightweight DAX query imports summarized results from the main dataset into the validation dataset. Do not use DAX queries as a data source for your regular report models. This validation report is a rare and appropriate exception to the rule. The second data source for the validation solution is a set of files exported from reports produced by the LOB system.
These reports only contain summary information but can be compared to totals from our Power BI solution. Values from the two sources are compared and the validation report shows whether they match or fail validation.
Just a note about the decision to create a separate dataset. There were two reasons for this: 1. We could not control the format of the validation files. Today we do not have the ability to use a live connection to a Power BI dataset in a composite model along with other sources, but we will in the near future.
Although I am now pretty good at writing DAX queries by hand after a couple of these projects, the easier way to get started is to allow Power BI Desktop to generate the query for you. Here are the steps using the ContosoDW dataset as an example:. The DAX query is now in the Windows clipboard and can be used to run the same query that the designer produces to populate the table visual. To test and modify the query, do the following. This code can be simplified for our needs.
You can disregard everything after that. Make the following changes to simplify the DAX query code:. This is the query I would use to populate a new table in my validation dataset. Then finally, paste this query code into the query window. Next step is to import the validation file s that were exported from the LOB report. In our validation solution, I used the SharePoint connector and imported all files with a predefined file name prefix. After the validation files are imported, make sure that the column data types are compatible, load the table and wire-up a relationship between the two tables.
I then used a KPI indicator icon in the table visual using conditional formatting to show if the differences were within an acceptable range. This screen capture from the validation page has the real data blurred out but you can see the grouping and comparisons resulting in either green or red indicators.
Each month, I receive an email from our user who is responsible for the report validation. He tells me that at a quick glance he is able to verify that the totals from their line-of-business application and Power BI solution match, and that the executive leaders and financial analysts who use these reports can be certain that they are accurate and reliable.
We are now setting up alerts using KPI tiles on a dashboard that will message key users when validation either succeeds or fails in key areas. As a result, the confidence in this solution has increased significantly and business leaders trust the information they get from these reports. Moving important business data into a data model for analytic reporting can often be a two-edge sword. Data retrieval is fast and can support all kinds of analytic trending and comparisons.
But, data in the model may be one or two layers away from the original source data, making it more challenging to compare with familiar user reports. I have been doing a lot of data validation work lately — both formally and informally. Informally : Validating calculation results from a Power BI data model is just a normal part of the development process.
Formally : After developing and delivering an entire business dashboard solution, a formal validation process is used to validate the ongoing results after future data refresh cycles and to certify reports so that business leaders know they can trust them.
Particularly for database developers, making the leap to using reports built on a tabular data model instead of the source database is a mental transition. The developer has to stop thinking in SQL and start thinking in model mechanics.
This can pose a challenge when needing to validate numbers in a report, which should be done using the data model and not only by running by a SQL query. When designing a new data model, this is typically the first thing I do… For every fact table and for each large dimension table, I create a measure that returns the record count for that table.
Users normally think about their data in business terms like sums, ratios and averages and not about how many records there are in a table.
Record counts are a convenient sanity check for record completeness and distribution across groups; and may also be a good indicator for model size and performance. At minimum, I will create the following measures with code similar to this:.
These measures allow you to quickly troubleshoot problems that might otherwise go unnoticed — with model relationships, key matching, orphaned records and missing records. Just drag-and-drop the measure and a field to group the values. For example, if I drag the Sales Row Count measure and Year from the Date table onto the report canvas, I can immediately see sales records across years.
If I do the same thing with the customer region field, I might be able to spot a gap in the region distribution of records over time. Easy peasy. Coming from an SSAS multidimensional background, I learned to define measures for every useful numeric metric. In Power BI and SSAS Tabular, you can simply use implicit measures — meaning you can just drag-and-drop any field and then indicate that you want to count each occurrence or sum up the numeric value of the field.
For quick-and-dirty Power BI reports, this is a convenient way to get simple aggregate results but not a best practice in my opinion because of inconsistent behavior and other limitations. There is a growing consensus in the Power BI community that using explicit measures should be the norm and not the exception. If you always use explicit measures rather than aggregating numeric columns, users and report developers will always have a consistent experience.
This is a simple example but it is not unusual. Reusing measures helps to separate reusable calculation logic but it can also create deep dependency chains. At some point, you will inevitably discover a calculation error that you will need to track-down and debug. This helps to break the problem down into manageable steps and components.
If you know that the first measure is always behaving as expected, move on to the next measure that depends on it. By isolating the problem, I can find and fix the issue every time. Keeping track of dependencies between layers of measures can be challenging. I keep a scratchpad on my desk to make lists of things I need to remember. I use a whiteboard for a similar purpose, diagramming dependencies between measures and other objects with boxes and lines. Just standing and diagramming on the whiteboard is a very effective problem-solving technique for me.
Electronic tools like OneNote and Microsoft Whiteboard can be just as effective if you are in the habit of using them. The Object Dependencies feature in Tabular Editor is pure magic and a tool I am using more frequently to test, debug and to document my data models. On a different but related topic… Create separate measures for calculations that will be used in visuals and reused by other measures but having too many measures can be difficult to manage.
Strike a balance between code reuse and a more succinct, easier-to-manage portfolio of measures. If you have to jump through seven hoops e. The implementation of display folders in Power BI Desktop in my opinion is an unfinished feature. If you want to use display folders to organize measures within container tables, use Tabular Editor. This way, they all get sorted and grouped together in the field list. The theme here should be finding the right balance to organize your objects while keeping things simple.
This is a big topic and I have a learned some great techniques which will be covered in a follow-up post. How do users and business leaders know with certainty that they can trust the information in a Power BI report? There are a variety of factors that can affect data reliability. If the data source is known to be correct, there could be issues in the extraction, staging, loading, transformation or the calculations used to present the information in an analytical report.
If we had a report with a simple set of aggregate values read directly from the business source of truth alongside calculated values from the data model after it has been refreshed, this can provide a litmus test to check and instill congoing confidence.
This is what a validation report is all about. To set the stage for a longer post on this topic, think about this typical scenario: Every month we load fresh manufacturing production volume and forecast records into our data mart or data lake, and then into the Power BI data model. The results of the data model can be used as a data source for the validation report.
Using the XMLA endpoint, you can run a DAX query against the published data model to get the values shown in a report visuals complete with filters and slicers. For comparison, you can load data from a report produced by the business system, load those values into a the validation data model and compare the monthly totals. Creating a Validation Report Solution — Part 2. This will be the second edition of a book I co-authored ten years ago and this edition is also a collaborative effort with multiple contributors.
We have quite a lot of excellent material that is just starting to go through the process of editorial and technical review. Writing this blog series while juggling other commitments has brought me to two realizations: 1. Posting articles in the series will be sporadic, and 2. This brings me to the subject of this post: Paginated and Analytic reports. We could create a variety of charts and reports that allowed users to drill-down from summary to details.
We could create reports that allowed a user to drill-through, from one record or report item to another report that presented more details. With a little creativity, we could create reports that allowed users to click items that would filter other items on the same report. Today, after transforming data into the right format for a tabular data model, this type of interactive functionality just magically happens in Power BI with very little report design effort. But, Power BI is primarily a tool for analyst users to create their own reports and to get answers to business questions rather than for IT developers to create reports that print or export perfectly to a PDF or Excel.
Simple business processes and task often require simple reports to help manage them. Paginated reports, by definition, are reports that allow a lot of data to be presented on multiple pages. Paginated reports can be viewed online, printed or exported to a variety of file formats. I can cite many cases when using Power BI Desktop is clearly the right choice to create an analytic report, and cases where it would make more sense to create an operational report using Paginated Report Builder.
I think these are the easier decision points but I would like to explore those cases where we could argue either way. What are the pros and cons of using Power BI to create traditional reports?
When would it make more sense to replace a static, paginated report with an interactive report created with Power BI? The integration of Paginated Reports into the Power BI cloud service continues to march forward at a rapid pace. Just yesterday, Chris Finlan teased a blog post announcing new Power Automate integrations that will allow report to be distributed and exported using flows.
Whether you host operational reports on your own report server or in the cloud, Paginated Reports are an important part of most business reporting portfolios. Do you design reports differently than you would have 5 years ago?
Do your users prefer interactive, self-service reports now or do they still want made-to-order static reports? Please post them in the comments below this post and then I will extend this post with your input and my thoughts. In a business intelligence solution, data must be shaped and transformed. Your 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.
If source data is in a relational database, chances are that it is optimized for transactional processing or application development. The column names, data types and formats are likely not suitable nor user-friendly for simple reporting. If source data exists in files or spreadsheets extracted from business reports, without data preparation you are likely just to reproduce the existing reports without adding business value.
This important topic raises one of the most critical questions to consider when designing a Power BI project:. Data can be shaped and transformed in at least three different layers in the business intelligence process:. Your options for transforming data at the source depend on the capabilities of the data source. In general, a query running closer to a data source will be more efficient than one that must transfer a set of data, cache and manipulate it downstream.
The right side of the previous diagram depicts data transformed in the data model. Some data morphing can be performed in DAX using calculated columns or calculated tables. In rare cases, there may be good reasons to do this but if you can perform row-level calculations in a database view or in Power Query, you will be better off.
It is also easier to maintain the solution if you can keep all that query logic in the same layer so you are only using one design tool. This is the approach used in traditional data warehouse solutions, typically under the control of an IT-managed data warehouse development group.
Back in the day, the conventional approach was to move data from sources into a staging database, and then to move it again into a dimensional database that was structured to support analytic reporting. I say this in past-tense because that was the way we always did it years ago. Now, in the age of self-service analytics, it has become more common to build reporting projects on-the-fly and to transform data without moving all the data from one location to another before it arrives in the BI data model.
However, there are limits to just how much data we can transform and how much complex transformation can be done in an ad-hoc fashion. The water here is muddy and it is difficult to say that one approach is always preferable to another but one thing is very clear: a Data Warehouse is very necessary in cases where data governance, high volume and centralized management are required. If requirements are well defined and unlikely to change, building a data warehouse or dimensional data mart might be the ideal approach.
However, data warehouse projects can get stuck in long cycles of over-planning and requirement gathering which has led many organizations to think about about BI as an extension of business rather than IT processes. Most organizations have come to grips with the reality that the vast majority of BI projects are conducted after databases and data warehouses have been completed.
Each of these goals can be achievable but are potential rabbit holes. The data lake has become a viable option for staging and storing data before moving it into a structured model because relational storage can be expensive.
Data lakes provide a low-cost means to store a lot of data in the cloud. Synapse, Snowflake or Cosmos DB to store a large-scale data warehouse. Bringing to discussion back to focus on Power BI queries, data models and reports; let me put a bottom line on this multifaceted topic.
I believe that there are three data transformation scenarios related to Power BI design:. It is a journey that requires iterative thinking and management. Accepting and embracing this reality will save everyone a lot of angst. Power Query is the data transformation technology built into Power BI. The Power BI service includes an online implementation of Power Query called Dataflows which allows queries and entities to be share between Analysts creating different data models.
Learn to leverage Power Query to perform data transformation efficiently to perform all row-level transformation and calculations. This is a critical concept to understand about Power Query. For supported data sources and whenever possible, queries you create in the Power BI query editor will be converted to native queries to be executed at the source. This means that if you are using SQL Server, rather that transferring all the data from the database engine to the Power BI service before performing transformations, steps may be converted to an optimized TSQL query.
This is an ideal scenario for high-volume fact tables. Query folding only works when Power Query can select records from a single database object like a database table, view or table-valued user-defined function.
For relational database professionals, there are a few realities about Power BI that can be a shock to their pattern for solving data problems. Without getting into all the details about why things are the way they are, it is very important to understand and embrace these points:. Selectable database objects can be folded — If you can use a single database table or create a view in the database, particularly for high-volume fact tables, these queries will be faster and will consume fewer server resources.
I have successfully used stored procs as data sources, at the insistence of DBAs, but this is not a recommended design practice. Queries for large tables should be designed with filters so you can easily control the records imported — both at design time and when you need to populate the data model with a full set of records. The example shown here conforms to the requirements needed to implement the incremental refresh feature if Power BI:.
The RangeStart and RangeEnd parameters are then used in a Filter Rows step in the query to limit the imported records within the selected date range. One of the perks of using a date range filter is that it enables incremental refresh. This is one of the coolest features to come along in a long while and now it works with both Premium and share capacity licensing. If queries support query folding and use a set of date range filters to limit the records into a table in the data model, Power BI will automatically generate table partitions.
For example, if I were to partition a table by month, each partition includes records for only one month and data in each monthly partition is is only loaded when new records are available or when data has changed.
I design my fact tables to support incremental refresh as a standard design pattern whether we have a requirement to implement it or not. This gives me the flexibility to only load a small set of data I need for development. Publishing a smaller PBIX file is faster and then more data can be loaded after the dataset has been published to the service. The presence of every single column in your data model comes at a cost.
It uses memory, storage space and can potentially impact performance. If you or you users change their minds, columns can easily be added. Before you ask, this is not a picture of my garage but according to my wife, it could be. The difference between the cluttered garage analogy and reality of Power BI design is that you immediately benefit by getting rid of unneeded data right now and it costs you little or nothing to go back and add one of those columns later, after your business stakeholders decide they need it.
In the future, use the gear icon next to the generated step to make changes to the column selection. Please read this aloud and commit it to memory: Rename all columns that will be visible in the data model using concise, self-describing, meaningful and user-friendly names. Not only is this good practice in BI solutions, but there is a psychology to the way business users perceive what they see in reports.
When they see cryptic labels and field names, they become uncomfortable and may distrust the information. Your data model should be designed for users and not for developers to design reports and consume. Even if technical professionals are designing reports, field names are going to show up as labels and titles. Consider the following examples:. Technical naming conventions are deeply engrained into the culture of application and database design and for good reason. For developers and database professionals, certain naming patterns like Camel Case camelCase and capitalized word abbreviations separated by underscore characters are almost a right of passage; but object names in this format should not exist in a data model for analytic reporting.
Table and fields should be named so they are familiar and easy to understand. When you or a user designing a self-service report drags and drops a field or measure into a report visual, these names are used as labels and titles that should also be meaningful and self-explanatory. Field names should also completely describe the object. Rename the steps in Power Query so that they are self-describing like the following example.
Your future self will thank you when you revisit the project in six months. This is done by right-clicking the step and choosing Rename from the menu. A new feature in Power Query displays a tooltip with annotations you add to the M script or the text you enter in the Description property of the step. Variables that contain spaces or special characters are proceeded with and are encapsulated with double quotes. A recent suggestion from one of the blog readers is to use underscores rather than spaces in these step names.
This allows you to double-click the variable name and copy it to the clipboard. Building queries using linear steps helps break-down complicated logic but it can become inefficient as the project evolves. In the course of query design, you are likely to repeat steps like those in the second example below where I removed three different columns after using them in other steps.
I also changed the data type each time I created a custom or duplicated column. If you select an existing step and then perform the same action on a different column or columns, Power Query will add the new transformation to the selected step. In the query on the left, I consolidated all these steps, removing columns and changing data types only once. The optimized query on the left runs in about one-third the time is the original query on the right.
When starting a Power BI project, you have many choices to make. Decisions like how to source your data, where and how to create queries to cleanse, transform and reshape the data; where and how to create calculations and the nuances of modeling are just the tip of the iceberg.
Most Power BI solutions progress through a few stages in their lifespan, which include:. This is a hard lesson but one that will pay off by helping our teams, sponsors and business stakeholders understand that we can proceed after laying down solid bricks based on well-defined functional and technical requirements to build the rest of the solution. If you are using Power BI to create reporting solutions for business users, your circumstances are likely similar to one of these scenarios:.
The data will need to be massaged and shaped before you can design insightful, interactive visuals. These projects tend to have a lot of business impact right out of the gate. Business report users will be delighted with what you can do in a short time with Power BI and they will want more. It is almost inevitable that you will experience two common challenges:. Building a solid, foundational data model is challenging in under these circumstances. The typical outcome is to start over with a new project where you will be well-prepared to use a cleansed data source and design to a well-defined set of requirements within the new project scope.
An experienced BI practitioner will set this expectation with the business and either carefully evolve certain elements into the final project and toss the rest, or use the first attempt as a throw-away prototype and start over.
Just know that this is a natural part of the process. If you work in an software development group in an IT organization with a formal application lifecycle management discipline, you are probably accustomed to using team development a code repository, version control and differencing tools to manage a shared code base.
The main components of Power BI came from a long heritage of IT-centric development so at its core, objects like tables, fields and measures have properties and metadata descriptors that can be versioned, differenced, merged and scripted. But what has made Power BI so successful is that is is primarily designed for self-service reporting.
The good news is that the community has rallied to develop tools to meet the needs of IT developers who work in teams to develop, test and deploy formal, enterprise-scale BI reporting solutions. Power BI and the rest of the Microsoft BI platform — can be scripted and automated to create custom deployments. BI reporting projects took many months to design, develop and deploy. In this form, the file contains data transformation queries, an in-memory data model with measures and other calculations, and report pages that include the interactive visuals.
This design typically meets the needs of small and intermediate sized projects. One of the first questions to consider is whether the solution you intend to create with Power BI is a quick informal project or a formal project — or may become a formal project in the future. They are limited in scope and typically meet a very specific and short-term need. Formal projects are built on best practice design standards to endure a certain degree of expanding scope and are designed for longevity.
They have a business sponsor or champion who drives the business requirements and at least one developer who is responsible for designing a solution that meets those requirements.
Formal projects are typically testing, signed-off an then deployed in accordance with an established development discipline or application lifecycle process so they can be supported by the business. If your organization has a process for delivering and supporting company-sanctioned applications and reports, then you will need a roadmap to transition from an informal project to a solution that the business is prepared to support.
A few common questions that come up in these discussions are:. Personal BI projects, where a data analyst imports data, creates reports and then publishes a report for their own use serves an important need in business. These reports can easily be promoted to a departmental or business area solution by sharing the report with others. As the scope of the project continues to expand, there are several important considerations that define the threshold between an informal project, designed for quick development and convenience, and a formal project; intended for longevity and long-term business support.
For each project component, I start with a typical informal project scenario and then provide recommended practices to to plan for futureproofing. Designing transformation queries, data model and reports in one PBIX file is convenient and offers quick troubleshooting, debugging and redesign.
However, it also limits development to one person at a time and promotes a monolithic solution. Separating the data model from the report files helps to promote self-service and focused report design.
The certified or promoted dataset is deployed, secured and managed as a separate object. Reports are connected to a Power BI dataset live connection and any number of reports can connect to a published dataset. This pattern supports both fully IT-managed projects with multiple developers and it supports self-service report design using certified and promoted datasets. After the data model design is stabilized and locked-down, report designers simply focus on report creation and data analysis by connecting to the secured dataset.
Spreadsheets and files extracted from reports, manually manipulated using Excel or other line-of-business applications have limited utility as reliable data sources. Manually preparing spreadsheets and source data files is only feasible for small sets of data and one-time or manual data loads. An analyst user can easily remove, rename or add a column or formula to a sheet that will break or negate the import process.
Futureproofing : Data from source systems can be loaded into a managed database or data lake before using Power Query to load the data model. Data from files if carefully protected and maintained may be transformed, cleansed and validated before it is ready to be loaded. Reduce the number and complexity of query steps and apply the following recommendations for Power Query design optimization. Use parameters in Power Query to manage any variable information such as database servers, database names e.
In smaller, informal projects we typically designed for a limited set of data and fixed time-frame. Futureproofing : When working with large data volumes, a subset of data should loaded into the desktop dataset for development.
Query parameters are used to expand the number of records usually using a date range loaded after the dataset is deployed for production use. Using the robust Power Query design user interface ribbon and menu options , you can build sophisticated and complex transformations that work well with moderate data volumes. Futureproofing : Tables and fields should be meticulously renamed to use standard friendly names, categorized and optimized with appropriate data types.
Unused columns are removed to save space and improve performance. Spend extra time to get the field names right before creating dependent steps. Consolidate redundant query steps — like those used to rename columns, remove columns and change data types — into a single step in the query. Attribute fields needed for grouping and filtering should be moved to separate dimension tables.
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DDL trigger can be used to observe and control actions performed on the server, and to audit these operations. DDL triggers can be used to manage administrative tasks such as auditing and regulating database operations.
I will discuss CLR trigger later. This event is raised when a user session is being established with SQL Server that is made after the authentication phase finishes, but before the user session is actually established.
Hence, all messages that we define in the trigger such as error messages, will be redirected to the SQL Server error log. Logon triggers do not fire if authentication fails. We can use these triggers to audit and control server sessions, such as to track login activity or limit the number of sessions for a specific login. It should conform to the rules for identifiers in Sql Server.
This option specifies, the security context under which the trigger is executed. AFTER triggers cannot be defined on views. One of these keywords or any combination of these keywords in any order can be used.
AS After this, we specify the actions and condition that the trigger performs. The trigger actions specified in the T-SQL statements. Note The name of a trigger should follow the rules for identifiers.
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