Over the past couple of months, I have started losing my ability to talk without a mask. One of the effects of a disease, ALS, is that I am losing the ability to breathe without support which also impacts my ability to talk without my ventilator. This past weekend I did my first speaking engagement at Derby City Data Days in Louisville, KY. As the saying goes, “It takes a village.”
As many of you know, I have been producing a video series called Fabric 5 which contains five minutes snippets about various Microsoft Fabric architectural topics. I was going to use some of that content to create a slide deck about medallion architectures in Fabric. As the day got closer, I was concerned I would not be able to maintain the ability to speak during an entire presentation in my condition. My wife and I thought that we could use my videos for the presentation, so we recruited my son, Alex, who does video editing, to put together the videos for the presentation. That only left the intro slides and the ending slides to be covered by me or someone else.
My daughter, Kristyna, was presenting right before me in the same room. She and my wife managed most of the introduction including the sponsor slides and the user groups. I was able to kick off with a microphone that was provided by the venue through Redgate. Then we kicked off the video and took questions during the session. I was able to answer questions with the help of my wife and using the mic provided in the room. We wrapped up with a final Q&A and a couple of references to help people in Fabric.
A quick video from the session
Overall, this was a wonderful experience for all of us and I appreciated the patience of everybody in the room as we worked through this process for the first time. Here are the Fabric 5 videos that were used during the presentation so you can follow up with it later and references are down below from the end of the presentation. I would like to thank the organizers – John Morehouse, Josh Higginbotham, Matt Gordon – once again for the support and the help as I presented masked up for the first time!
During the after party my ventilator ran out of power and my car charger did not work. This presented a problem because the trip home was over an hour. When we went out to the car, we had a moment of panic because the ventilator ran completely out of power. We went back to the bar where we were at for the after party to plug in, but the charger wouldn’t work. I was without the ventilator for 15 to 20 minutes while they troubleshot the issue. We started to think that they may have to call 911! However, this issue was resolved due to a loose connection. I must thank all of individuals there that helped including John Morehouse who went to get a battery backup system to make sure I could make it home and to my son-in-law who went to our house to bring us our backup battery generator as well to help us get home.
Working with ALS is not always easy and all the help from everyone around me is genuinely appreciated!
It was awesome to see the Kentucky data community come out for the first Derby City Data Days in Louisville, KY! Bringing together communities from Ohio, Tennessee, and Kentucky, the Derby City Data Days event was an excellent follow-up to Data Tune in March and deepened relationships and networks made at the Nashville event. In case you missed it, below are my notes from the sessions I attended as well as the resources for my session. Be sure to check out these speakers if you see them speaking at a conference near you!
Building Self-Service Data Models in Power BI by John Ecken
What and why: we need to get out of the way of business insights. If you try to build one size fits all, it fits none. Make sure you keep your data models simple and streamlined.
Security is paramount to a self-service data model. RLS is a great option so folks only see their own data. You can provide access to the underlying data model for read and build which enables them to create their own reports off the data they have access to. If you give your user contributor access, then RLS will go away for that user. Keep in mind, business users need pro license OR need to be in a premium workspace.
One really great option is for people to use the analyze in Excel option to interact with the most popular BI tool – Excel. This allows them to build pivot tables that can refresh whenever needed. You can also directly connect to Power BI datasets from their organization! You can set up the display field option as well to get information about the record you connect to. Pretty slick! With this, security still applies from RLS which is awesome.
Data modeling basics – clean up your model by hiding or removing unnecessary columns (ie sorting columns). Relationships matter. Configure your data types intentionally. Appropriate naming is vital to business user success. Keep in mind where to do your transformations – SQL vs DAX (think Roche’s Maxum). Be sure to default your aggregations logically as well (year shouldn’t be summed up).
Power BI Measures – creations, quick create, measure context, time-based functions. Whenever possible, make explicit measures (using DAX) and hide the column that it was created off of so people utilize the measure you intended. Make sure you add descriptions, synonyms (for Copilot and QA), featured tables, and folders of measures. The functionality of featured tables makes it wise to use folders of measures within your fact tables.
John likes to use LOOKUP to pull dims back into the fact table so he ends up with as few tables as possible. There are drawbacks to this such as slower performance and model bloat, but the goal is for end users who don’t have data modeling experience or understanding. Not sure I agree with this method since it’s not scalable at all and destroys the purpose of a data model. Make sure you hide columns you don’t want end users to interact with.
To turn on feature table, go to the model view then go to Properties pane and toggle that is featured table button. It will require a description, the label that will populate, and the key column (cannot be hidden) that the user will put in excel as a reference for the business user to call records off of.
Be sure to look at his GitHub for the awesome source code!
Come to Lousiville on May 9th to see a presentation on JSON and TSQL.
The goal of this is to avoid FULL OUTER JOINs. This is extremely unscalable since maintenance would be terrible. We will avoid this by using pivot. Pivot means less rows, more columns. PIVOT promotes data to column headers.
You get to decide how the tuple that’s created on the pivot is aggregated (count, min, max, sum, avg, etc.). Exactly one aggregate can be applied, one column can be aggregates, and one columns values can be promoted into the column header.
PIVOT ( SUM(Col1) FOR [ID] IN ([ID_value_1], [ID_value_2], etc.) SUM = the aggregate, ID = the column that will become more columns, the IN values = the column values from ID that will be promoted into the column header.
Time for a 3 column pivot. For this, we are doing a two column pivot and ignoring one of the fields. You can even pivot on computed fields but make sure you include the values in that inclusion clause. Be careful about adding unnecessary data.
How do you manage the VTCs (the column values that end up as column headers)? Option 1 – don’t. Option 2 – explicitly request the ones of interest and provision for future extras. Option 3 – use dynamic SQL! You can use cursor, XML, etc. Check out his ppt deck from github for code samples!
An n-column PIVOT works by essentially creating a 2-column pivot (at the end of the day it’s only two columns that ever get pivoted) and knowing which you want split into new columns.
Ugly side of PIVOT = lookups. The more fields you need to add in from additional tables, the worse performance will be. Your best option there would be to do a group by, the pivot. Another limitation is you can’t use a function in your pivot aggregation (SUM() vs SUM() *10). Get your raw data clean then pivot.
Time for UNPIVOT!
Unpivot = convert horizontal data to vertical. Less columns, more rows. Unpivot demotes column headers back into data.
Be very very careful with your data type your about to create. Remember lowest common denominator, all the values must be able to fit in one common datatype without overflow, truncation, or collation.
UNPIVOT( newColPropertyValue FOR newColPropertyName IN ([originalCol1], [originalCol2],etc.)
You need to make sure all your original columns have the same datatype. NULLs get automatically dropped out. If they are needed, you can convert them using an ISNULL function to a string value or int value depending on your need.
There’s also an option for XML-based unpivot.
Cross Apply = acquires a subset of data for each row found by the outer query. Behaves like an inner join, if no subset is found then the outer row disappears from the result set. Outer Apply is similar but it’s more like a left join. Cross Apply does keep your NULL values. You can also use a STRING_SPLIT with cross apply.
Multi-Unpivot normalizes hard-core denormalized data. You just add more UNPIVOT lines after your initial FROM statement! Make sure you have a WHERE statement to drop any records that don’t align at a column level. Something like WHERE LEFT(element1,8) = LEFT(element2, 8).
This session was awesome! We were able to watch a series of Fabric 5 minute videos and had an amazing discussion between them about options for building out a Fabric infrastructure using medallion techniques. Check out Steve’s YouTube channel for his Fabric 5 playlist and to learn more about his experience working with ALS.
The 14th annual Ability Summit is a global event that I attended a few weeks ago. It is hosted by Microsoft, and it presents the latest technology innovations and best practices for accessibility and inclusion. The event has three main session tracks: Imagine, Build, and Include. Each track examines different aspects of how technology can enable people with disabilities and make the world more inclusive. The event is free, and anyone can register online to attend. All sessions are recorded and can be watched at any time on demand.
Ability Summit 2024 Highlights
As we think about our enduring commitment and goal at Microsoft, which is to build that culture of accessibility and embed it into everything we do, grounded always by the insights of people with disabilities. – Jenny Lay-Flurrie
In the first keynote, Microsoft CEO Satya Nadella and Chief Accessibility Officer Jenny Lay-Flurrie talked about how AI can remove obstacles and create more accessible experiences, while also addressing the challenges and concerns of responsible AI. The keynote showed several examples of how AI can help people with disabilities, such as voice banking for people with ALS, descriptive audio for people with low vision, and Copilot for people with diverse speech patterns. It was very impressive to see Team Gleason featured as a partner with Microsoft to work on AI to help the ALS community preserve their voice.
Team Gleason and Microsoft Team Up to Give an ALS Person His Voice Back
As a platform company, we have to absolutely lean into that and make sure that everything we’re doing, whether it’s Copilot and Copilot Extensibility or the Copilot stack in Azure is all ultimately helping our ISVs, our customers, our partners, all achieve their own goals around innovation, around accessibility. – Satya Nadella
Build Session: Bridging the Disability Divide with AI
The conference had many sessions and keynotes, but this one about the disability divide and AI was very interesting to me. These are three main points I learned from this session: 1) how people with disabilities are benefiting from AI in their personal and professional lives; 2) advice on how to begin and advance the AI journey with accessibility as a priority; 3) the significance of accessibility as a basic value for developing technologies that enable everyone.
This session also provided some resources and opportunities for us to learn more about AI and accessibility, such as the Accessibility Bot, which is a chatbot that can answer questions about Microsoft’s products and services regarding accessibility topics; the AI Studio, which is a platform that allows users to explore and build AI applications using various cognitive services and SDKs; and the AI Platform Team, which is a group of developers and researchers who work on making AI more accessible and inclusive.
In Real Life
I belong to the ALS community (I have ALS), and I rely on a lot of accessible technology both hardware and software to accomplish work. I used a combination of Voice Access in Windows 11, a Stream Deck foot pedal, a foot pedal joystick on my wheelchair and Microsoft 365 Copilot to write this blog post. Voice Access helps me with dictation and specific commands like selecting paragraphs or capitalization. A Stream Deck allows me to do backspace and deletes. A foot pedal joystick acts as a mouse. Copilot assists me with summarizing and rewriting content. As you can tell, we need a whole set of tools to suit our needs, and there is no single tool or method that works for us. I’m excited to see how AI will enhance accessibility for all of us. My goal is to keep sharing the tools and techniques I use to live and work with ALS through my blog and YouTube channel.
Had an absolutely amazing time at SQLBits this year! It was lovely to see all my data friends again and had the opportunity to introduce my husband to everyone as well! In case you missed a session, or are curious about what I learn at these conferences, below are my notes from the various sessions I attended.
What’s new in Power Query in Power BI, Fabric and Excel? by Chris Webb
Power query online is coming to PBI desktop and will include diagram & schema views, the query plan, and step folding indicators! Currently in private preview, coming to public preview later this year.
Power query in Excel. Nested data types now work (very cool, check this out if you don’t know what data types are in Excel). Python in excel can reference power query queries now which means we don’t have to load data into Excel! That will allow analysis of even more data because you can transform it upstream of the worksheet itself. Very cool.
Example: import seaborn as sns
mysales = xl(“Sales”)
sns.barplot(mysales, x=”Product”, y = “sales”, ci=None)
Limitations – can’t use data in the current worksheet.
Power query is in Excel online! Later this year, you will be able to connect to data sources that require authentication (like SQL Server!).
Power query can be exported as a template! This will create a .pqt (power query template). You can’t import a template file in Excel, but you can in Dataflows Gen2 in Fabric.
Fabric Dataflow Gen2 – data is not stored in the dataflow itself. We now have Power Query copilot! It’s in preview, but it’s coming. You will need a F64 to use it.
Paginated reports are getting power query!! It will be released to public preview very soon. All data sources will be available that are in power bi power query. This will allow snowflake and big query connections annnddd excel! You can even pass parameters to the power query get data by creating parameters within the power query editor. Then you go to the paginated report parameters and create the parameter there. Finally go to dataset properties and add a parameter statement that sets those two equal to each other.
Rapid PBI Dev with ChatGPT, Tips & Tricks by Pedro Reis
Scenario 1: DAX with SVG & CSS. SVG – Problem is to create a SVG that can have conditional formatting that doesn’t fill the entire cell. ChatGPT does best with some working code, so give it a sample of the SVG you want and as ChatGPT to make it dynamic. Demo uses GPT4 so we can include images if needed. He uses Kerry’s repo to get the code sample that we put into GPT (KerryKolosko.com/portfolio/progress-callout/). CSS – uses HTML Content (lite) visual (it’s a certified visual). Prompt asks gpt to update the measure to increase and decrease the size of the visual every 2 seconds. Pretty awesome looking. He’s also asking it to move certain directions for certain soccer players (Ronaldo right and Neymar zigzag).
Scenario 2: Data Modeling and analysis. He uses Vertabelo to visualize a datamodel and it creates a SQL script to generate the model. He passes the screenshot of the datamodel schema to ChatGPT and it was able to identify that a PK was missing and a data type was incorrect (int for company name instead of varchar). Super neat. The image does need to be a good resolution. He also put data from excel into GPT to have it identify issues like blanks and data quality issues (mismatched data types in the same column). It could grab invalid entries and some outliers but it’s not very reliable now.
Scenario 3: Report Design Assessment. He grabbed four screenshots from a Power BI challenge and asked GPT to judge the submissions with a 1-10 rating based on the screenshots and create the criterion to evaluate the reports. Very neat! Then he asked it to suggest improvements. It wasn’t very specific at first so he asked for specfic items like alternatives to donut charts and other issues that lowered the score.
Scenario 4: troubleshooting. He asks it to act as a power bi expert. It was able to take questions he found on the forum, but sometimes it hallucinate some answers and it’s not trained on visual calculations yet.
Scenario 5: Knowledge improvement. He asked GPT to create questions to test his knowledge of Power BI models. It even confirmed what the correct answers. It even created a word doc for a shareable document that can be used to test others in the future.
Streamlining Power BI with Powershell by Sander Stad and Linda Torrang
There’s a module called MIcrosoftPowerBIMgmt that is used with a service account to wrap around the rest api and allow easier calls of those APIs through powershell. When running PowerShell, to see the things in a grid use the code ” | Out-GridView” at the end of your command and it creates a very nice view of the data. All demos are available at github. The first demo is to grab all the orphaned workspaces then assign an admin. Their module includes more information like the number of users, reports, datasets, etc. They also have a much easier module that will grab all the activity events from a given number of days (it will handle the loop for you).
Another great use case will be moving workspaces from a P SKU capacity to a F SKU capacity automatically. The admin management module doesn’t work for this, but the PSPowerBITools module can! To see help – use “get-help” in front of the command to get details and examples. For more details, add “-Detailed” at the end to see that. Adding “-ShowWindow” at the end of a script will pop open a separate window to see the response (note- doesn’t work with -Detailed but does contain the details). To export use this script: $workspaces | Export-Csv -NoVolbber -NoTypeInformation -Path C:\Temp\export_workspaces.csv.
The goal is to use the module for more ad hoc admin tasks like remove unused reports, personal workspaces, data sources, and licenses.
ALL: removes all the filters from expanded table/columns specified. REMOVEFILTERS: like ALL (it’s the same function). We use this for code readability. ALLEXCEPT: removes filters from expanded table in the first argument, keeping the filter in the following table/columns arguments.
The following produce the same result: ALLEXCEPT(customer, customer[state], customer[country]) REMOVEFILTERS(customer[customerkey], customer[name], customer[city]) The nice thing about ALLEXCEPT is that it’s future proof against additional fields being added to the table.
But is it the correct tool?
Demo – goal is to get % of continent sales by getting country or state or city sales / total continent sales
To get that total continent sales, we need sales amount in a different filter context. The simplest way to do this is using ALLEXCEPT since we always only want the continent field from the customer table. CALCULATE([Sales Amount], ALLEXCEPT( customer, customer[continent]))
This works if we include fields from the same table – customer. If we pull in fields from another table, it won’t work as we want because it will be filtered by a different table’s filter context. To get rid of that, you can add REMOVEFILTERS(‘table’) for each OR we can change the ALLEXPECT to use the fact table. CALCULATE([Sales Amount], ALLEXCEPT( Sales, customer[continent]))
One problem is if a user drops the continent from the visual, the filter context no longer includes continent so the ALLEXCEPT will no longer work properly as it will function as an ALL.
To fix this, use remove filters and values. Using values adds in the continent value in the current filter context, then removes the filter context on customer. Lastly, it applies the value back into the filter context so we end up with what we want which is just continent is filtered. This works quickly because we know each country will only have one continent.
Performance difference? Yes – ALLEXCEPT will be faster, but the REMOVEFILTERS and VALUES should not be too much slower.
Analytics at the speed of direct lake by Phillip Seamark and Patrick LeBlanc
Phil did a 100 minute version of this session on Wednesday.
What is direct lake? We will evaluate over three categories – time to import data, model size, and query speed. Direct lake mode takes care of the bad things from both import and direct query modes. Direct lake only works with one data source. Data is paged into the semantic model on demand triggered by query. Tables can have resident and non-resident columns. Column data can get evicted for many reasons. Direct Lake fallback to SQL server for suitable sub-queries. “Framing” for data consistency in Power BI reports. Direct lake model starts life with no data in memory. Data is only pulled in as triggered by the DAX query. Data stays in that model once pulled for hours. We only grab the columns we need which saves a ton of memory.
Limitations – no calculated columns or tables. No composite models. Calc groups & field parameters are allowed. Can’t be used with views, can only be used with security defined in the semantic model (you can have RLS on the semantic model itself). Not all data types are supported. If any of these are true, Power BI will fall back to Direct Query mode.
SKU requirements: only PBI premium P and F skus. No PPU nor pro license.
Why parquet? column-oriented format is optimised for data storage and retrieval. Efficient data compression and encoding for data in bulk. Parquet is also available in languages including rust, java, c++, python, etc. Is lingua franca for data storage format. Enhanced with v-order inside of Fabric which gives you extra compression. Analysis services can read and even prefers that form of compression so we don’t need to burn compute on the compression. V-Order is still within open source standards so this still doesn’t lock you into Fabric only. Microsoft has been working on V-Order since 2009 as part of Analysis services. Row group within parquet files corresponds directly to the partitions/segments in a semantic model. Power BI only keeps one dictionary, but parquet has a different dictionary id for each row group/partition. When calling direct query, low cardinality is key because most of the compute will go to the dictionary values that need to be called across the various partitions. This is why it’s VITAL to only keep data you need for example, drop or separate times from date times and limit decimals to 2 places whenever possible.
You can only use a warehouse OR a data lake. So if you need tables from both, make a shortcut from the data lake to the warehouse and just use the warehouse. You can only create a direct lake model in the online service model viewer or in Tabular editor. You cannot create it in PBI desktop today.
Microsoft data demo. One file was 880 GB in CSV > 268 GB in Parquet > 84 GB with Parquet with V-ORDER.
Sempty is a python library that allows you to interact with semantic models in python.
In the demo, Phil is using sempty and semantic-link libraries. Lots of advantages to using the online service for building the semantic model – no loading of data onto our machines which makes authoring much faster. After building this, you can run a DMV in DAX studio. Next, create a measure/visual that has a simple max of a column. That will create a query plane to reach out to parquet and get the entire column of data needed for that action then filter as needed. After that, the column will now be in memory. Unlike direct query, direct lake will load data into model and keep it for a bit until eviction. The direct lake will also evict pages of columns as needed when running other fields. You can run DAX queries against the model using python notebook and you can see via DMV what happened.
Framing. Framing is a point in time way of tracking what data can be queried by direct lake. Why is this important? data consistency for PBI reports, delta-lake data is transient for many reasons. ETL Process – ingest data to delta lake tables. What this does is creates one version of truth of the data at that point of time. You can do time traveling because of this, but keep in mind that for default models it frames pretty regularly. If you have a big important report to manage, you need to make sure to include reframing in the ETL process so it reloads the data properly. This can be triggered via notebook, service, api, etc. You can also do this in TMSL via SSMS which gives you more granular table control.
DAX to SQL Fallback. Each capacity has guard rails around it. if you have more than a certain number of rows in a table, then it will fall back to direct query automatically. Optimization of parquet files will be key to stop it from falling back.
Deep (Sky)diving into Data Factory in Microsoft Fabric by Jeroen Luitwieler
Data Pipeline Performance – How to copy scales.
Pipeline processing – less memory & less total duration. No need to load everything into memory then right. Read and Write are done in parallel.
Producer and consumer design. Data is partitioned for multiple concurrent connections (even for single large files). Full node utlization. Data can be partitioned differently between source and sink to avoid any starving/idle connections.
Partitions come from a variety of sources including physical partitions on the DB side, dynamic partitions from different queries, multiple files, and multiple parts from a single file.
Copy Parallelism concepts: ITO (Intelligent Throughput Optimization), Parallel copy, max concurrent connections. ITO – A measure that represents the power used which is a combo of CPU, memory, and network resource allocation used for a single copy activity. Parallel copy – max number of threads within the copy activity that read from source and write to sink in parallel. Max concurrent connections – the upper limit of concurrent connections established to the data store during the activity run (helps avoid throttling).
Metadata-driven copy activity. Build large-scale copy pipelines with metadata-driven approach in copy inside Fabric Data pipeline. You can’t parameterize pipelines yet, but it is coming. Use case is to copy data from multiple SQL tables and land as CSV files in Fabric. Control Table will have the name of the objects (database, schema, table name, destination file name, etc). Design has a lookup to get this control table and pass that through a foreach loop and iterate through these to copy the tables into Fabric.
Dataflows Gen2 – performance optimization and AI infused experiences. 4 performance principles – delegate to the most capable resource, sometimes you have to do the most expensive thing first, divide and conquer, be lazy do as little work as possible.
Query folding – known as query delegation, push down, remote/distributed query evaluation. Wherever possible, the script in Power Query Editor should be translated to a native query. The native query is then executed by the underlying data source.
Staging – load data into Fabric storage (staging lakehouse) as a first step. The staged data can be referenced by downstream queries that benefit from SQL compute over the staged data. The staging data is referenced using the SQL endpoint on the lakehouse. Staging tips – data sources without query folding like files are great candidates for staging. For faster authoring, have a different dataflow for staging and another for a transformations.
Partitioning – a way to break down a big query into smaller pieces and run in parallel. Fast copy does this in the background.
Lazy evaluation – power query only evaluates and executes the necessary steps to get the final output. It checks step dependencies then applies query optimization to make the query as efficient as possible.
AI infused experiences – column by example, table by example, fuzzy merge, data profiling, column pair suggestions. Table by example web is pretty awesome. You can extract any data from any HTML page and have it figure out how to turn that HTML into the table you want to see. Basically allows you to screenscrape as part of your ETL process and it will refresh if any changes are done on the website. Table by example for a text/csv is also amazing, it can try to figure out how to take a human readable file and split it into multiple columns. Game changers!
Website Analytics in my pocket using Microsoft Fabric by Catherine Wilhelmsen
Catherine built her own website and blog hosted on Cloudflare. Cloudflare has an API that pulls all the stats. To build this, she worked with Cloudflare GraphQL API. They have one single endpoint for all API calls and queries are passed using a JSON object.
Data is returned as a json object. She built the orchestration inside of Fabric Data Factory.
First portion is to set the date, she has this check if she wants to use today’s date or the custom date.
After that, she accesses the data using a copy data activity. Last step is to copy that data into a lakehouse. The file name is dynamic so she can capture the date that the data was pulled from.
The destination is a Lakehouse where she can build a report off of it. The best part of the copy data is the ability to map that nested JSON response into destination columns within the lakehouse. As a bonus for herself, she made a mobile layout so she can open it on her phone and keep track of it on the go.
Next steps for her project – see country statistics, rank content by popularity, compare statistics across time periods.
Calculation Groups & C# the perfect couple by Paulina Jedrzejewska
What makes it a perfect couple? We can add C# to our calc group dev to make it much more flexible.
Calc groups don’t allow you to create a table with customizable sorting of the measures and have measures not using the calc group within the same visual. For example, if I have previous year and current year in my calc group, it would display all of my current year measures together and all the prior year measures together, but I may want to see current year sales right next to prior year sales instead of having current year sales, revenue, cost, etc. then have the prior year much further away. To solve this, we will create separate measures for each time intelligence option. But why do that by hand when we can automate?
What can we automate? Creating & deleting measures, renaming objects, moving objects to folders, formatting objects.
Why automate? Works through a bunch of objects in seconds, reusable, homogeneous structure and naming, and no more tedious tasks for you!
For this demo, we will use Tabular Editor 2, PBI Desktop, Code Editor (visual studio code). She uses a .cs file to script out the code. All the columns in the fact table that will be turned into a measure have a prefix of “KPI”.
column.DaxObjectFullName will give you the full table, column reference which allows you to reference that dynamically within C# code.
Calculation groups are essentially a template for your measures. It’s a feature in data modeling that allows you to take a calculation item and use it across multiple measures. These are very useful for time intelligence, currency conversion, dynamic measure formatting, and dynamic aggregations.
When creating a calc group, always be sure to include one for the actual measure value by using SELECTEDMEASURE(). You can also refer to other calculation items within others as a filter.
Now that we have a calc group, the C# can loop through every base measure and through all the calculation group items and generate our new measures off of that. Remember all the code is in her github 🙂
Another great use case for C# is to add descriptions to all the measures.
DataTune 2024 was a huge success! Thank you so much to the organizers who spent countless months planning and making this an incredible conference! It was so great to meet so many people and hang out with so many fellow data nerds!
Microsoft Fabric for Power BI Users by Alex Powers
Put your data in OneLake and do everything you want. The “OneDrive for data”. Every org has ONE OneLake. You can share across workspaces via datalakes.
There’s a tons of new Fabric items, what is important for PBI users?
Goal: Get data (OneDrive) > ingest (Dataflow Gen 2) > store (Lakehouse/semantic model) > deliver insights (PBI) with data pipelines to orchestrate.
Start by creating a lakehouse. Tables are optimized delta/parquet format. Files is unstructured. You can create sub folders within those as well. There’s a OneLake file explorer that lets you drag and drop files into the OneLake.
Next is a dataflow. If you want to copy and paste M queries, you can hold down control on the queries and control C then control V directly into a cloud dataflow! When merging tables, there’s a light bulb in top right that will suggest the join option for you so you don’t have to scroll anymore. The visual dataflow allows you how to see the lineage of various tables. Be sure to stage the changes then publish.
Next is to publish to the lakehouse we made before. You get a chance to double check the sources and make sure all the data types work with parquet format. You can also choose to append or replace on refresh. You can also allow for dynamic schema so it’ll pick up any name changes to columns! Lightning bolt on table means that it has been optimized. Right now this is all low code no code, but there may be some announcements at the Vegas conference at the end of the month to allow for writing code instead of using the point and clicks.
Next build a data pipeline. 95% parody to ADF pipelines. Create a dataflow step then configure settings to grab the new dataflow. Add a teams activity so when it fails it will send a teams notification. Just need to sign into teams and allow access. You can send to a channel or group chat. The UI is a lot like Power Automate.
To set up refresh, go to pipeline and schedule there! It’s way way easier on the schedule options. Soon there will be an option to refresh a semantic model.
To create shortcuts, go to the lakehouse and create a new shortcut. You can even reference items from other workspaces/lakehouses. Right now, you can also do a shortcut to AWS and ADLS gen 2.
In lakehouse, you can create a new semantic model. XMLA editing is supported! You can create your DAX measures there as well. You can even then create a new report from this semantic model! No need to import any data. Now you can do this all on Mac, Raspberry Pi, Linex, any OS with a browser. You can also use the SQL analytics endpoint for a SQL experience for navigating the lakehouse in a SQL manner that lets you run and save as views or create sps (upper right corner in lakehouse view). It is case sensitive.
Microsoft Fabric + Power Platform + Azure Open AI = Magic Mix by Alex Rostan & Gaston Cruz
Goal how to combine technologies together. Focus will be starting with a Power App and end with data in Fabric.
Start off by making a Power App using copilot. Prompt = let’s create a new application to audit power bi licenses. Power bi pro and premium per user licenses tied to our organization. It creates a dataverse table within the environment. You can edit this by using another prompt = add a new column to check the latest access to each user for our Power BI Tenant. It created a new column for us! Then just say create app. And boom we have a functional application!
In the tables tab, you can click Analyze and there’s an option to Link to Microsoft Fabric. Dataverse is a service, so depending on the use case it’ll make the correct configuration to optimally grab your data. That would allow you to create Power Apps that people input data into then instantly see it in Fabric in real-time.
Now we go to Fabric. Data verse is a native connection in data pipeline. Use dataflow for ETL and pipeline for orchestration. Then determine where to drop data. You can choose Lakehouse (structured & unstructured data), KQL database (near real-time), or Warehouse (structured data).
Data activator is the way to go for alerting.
Once you create semantic model, you can create a report on top of it. This will use Direct Lake mode (perfect combo of import and direct query). No refresh needed. Lakehouse holds the data for the model to leverage. Report will automatically grab data from there.
The Problem with Pipelines: Building Extensible Data Code by Matthew Kosovec
Goal is to take software engineering principals
Pipelines can turn ugly fast when the requirements change or when introducing new source systems. You can easily end up with code that isn’t used.
Software Engineering concepts:
DRY (don’t repeat yourself)
SRP (single responsibility principle)
Pure Functions Only
DRY – Every piece of knowledge must have a single, unambiguous, authoritative representation within a system. Ways to dry off: CTEs, views/materialized tables, macros/UDFs (user definied functions), ETL packages. Challenges: Encapsulation and composability (how generic is your code snippet that can be reused), dependency management, refactoring and regression testing. Smallest unit of work is a single SELECT statement (table, view, etc.) which can be the challenge of encapsulation.
DRY example – you have a feature engineering table need that is the summation of a BI table. To keep this DRY, you can build the FE table on top of the BI table to avoid the code duplication. The con here is that FE is now dependent on BI code. The additional layer can also create slower refresh cycles. This may also not be possible always because the relational logic may be different enough.
SRP – separation of concerns. Gather together the things that change for the same reasons. Separate those things that change for different reasons. Medallion architecture is a common approach. Pros is helps separate concerns between layers and provides a baseline for architectural gov and reviews for pipeline design. Cons are lacks structures for the most important part of pipelines (transformational code!) and often devolves into multiple sub-layers of storage to help with DRY or performance.
Pure Functions Only – data pipelines are imperative. Focuses on step by step program. Uses statements that change a program’s state. Goal is to have zero side effects, ultimate composability, DRY by default. Because you can reuse everything in small bits, you don’t need to rewrite your code over and over.
Example – Pure functions: modifies a single row in a single column with unlimited input columns/rows and can create a new column. Example of that is Scalar calcs, UDFs, and rollups to current grain. Semi pure functions: modifies multiple rows in a single column, unlimited input columns/rows, can create a new column. Example is window functions like ranking. Impure/non-functional: modifies multiple rows in multiple columns, creates multiple columns. Examples are CTEs/procedures, unpivot, CDC/refresh processes.
Making code DRY and Functional:
Use SRP to break down SQL into small, functional, composable parts (SELECT, FROM, etc.)
Build a system to check for DRY-ness and ensure referential integrity between objects
Develop code generator to create runnable SQL from objects
Use SRP to define processing steps to separate functional from non-functional (bonus step but has some serious benefits by repeating the process)
DataForge example:
Step 1 – identify the composable parts. Examples: aliasing (columnA as A), scalar calculation (ie CAST (A as int)), aggregation (existing grain aka rollup), aggregation (new grain), Filter (WHERE), all joins (including cardinality), etc. Sorting those into object groups. Rules = aliasing, scalar calcs, aggregation rollup. Channel mapping = filter, aggregations (new grain). Relation = joins. Not comprehensive, there’s tons.
Step 2 – design and build a meta-repo database to store and relate objects. Build monitoring services to check for duplicates on save and compilation. This gets stored.
Step 3 – Take the broken down model and use a SQL Code Generator which can write DDL (change tables) then Pure SQL (do all the SQL at once, no need for intermediate storage) then Impure SQL (everything else). The development is no longer the entire table, you write a function that can create the pieces you need. The system can do a bunch of checks to see if there’s any downstream effects or if it already exists. Then it gets orchestrated.
There are vendors who build data mappings from source data to target system and build adaptors that can work with DataForge. Matthew says this is fairly easy to do on your own as well. The goal of this is that when you make changes, you aren’t too worried since there are checks for duplication and reuse.
Step 4 – blow up the medallion architecture. Non-functional/imperative (ingest – parse – capture data changes) > pure (enrich) > semi-pure (refresh recalc – output) > imperative (custom post output). Everything goes through every step. You end up not needing orchestration. Grouping pure functions together removes that need and helps prevent the eventual chaos/spaghetti code.
This isn’t about building the first thing. For that, pipelines are pretty straightforward. This is more about adding to it. You end up building your lineage through the metadata which allows for increased observability.
DataForge has an out-of-the-box solution to this – www.dataforgelabs.com. This solution works with existing pipelines. Under the hood, this is databricks so it can integrate with a lot of different code bases. They have two ways to do this – an IDE/UI (tons of checks with real-time feedback) and a YAML config language which then is compiled in their system through an import process where the checks are then performed. On the roadmap is a CLI to compile locally. There’s a private cloud version and a SAAS version.
Data on Wheels - Kristyna Ferris & Steve Hughes 