The Microsoft Fabric labs in Github are used by multiple courses, including DP-600 and DP-700. I've put my notes on the labs in this blog post rather than in the posts on those courses.
Links
https://github.com/MicrosoftLearning/mslearn-fabric
Note that the two courses use different names for the labs - the names below are from the Github source above.
Coding Style
The coding style in the labs is generally good (especially the "SELECT 1" in the SQL EXISTS subquery!) though I would like to see more use of the AS keyword.
However, using a "v" prefix in a SQL view (for example "vSalesByRegion") is poor practice. This naming convention is technically called Systems Hungarian and scornfully called "tibbling".
The lab almost always use backslashes to span Python code across multiple lines. The Python community generally recommends using parens.
https://stackoverflow.com/questions/53162/how-can-i-do-a-line-break-line-continuation-in-python-split-up-a-long-line-of
Create a Microsoft Fabric Lakehouse (01-lakehouse.md) - DP-600 lab 1, DP-700 lab 1
A routine lab. You might spend longer on the capcha than on the lab itself.
Analyze data with Apache Spark in Fabric (02-analyze-spark.md) - DP-600 lab 4, DP-700 lab 2
Delivery hint: IL.
Note that in Spark SQL you use backticks around identifiers containing nonstandard characters, not square brackets as in T-SQL.
"SELECT * FROM `lab 4 lh`.salesorders LIMIT 1000"
instead of
"SELECT * FROM [lab 4 lh].salesorders LIMIT 1000"
Use Delta Tables in Apache Spark (03-delta-lake.md) - DP-700 lab 3
Create a medallion architecture in a Microsoft Fabric lakehouse (03b-medallion-lakehouse.md) - DP-700 lab 6
Ingest data with a pipeline in Microsoft Fabric (04-ingest-pipeline.md) - DP-600 lab 3, DP-700 lab 5
Make sure you enter a new unique connection name. Don't just accept the default (the URL) otherwise you will get a "Conection name already exists" error.
Note that the name for the activity is randomised. Mine was called "Copy_ind"; yours will be different. You can, of course, rename these.
Why do I have to save pipelines but notebooks autosave?
Create and use Dataflows (Gen2) in Microsoft Fabric (05-dataflows-gen2.md) - DP-600 lab 2, DP-700 lab 4
Make sure you enter a new unique connection name. Don't just accept the default (the URL) otherwise you will get a "Conection name already exists" error.
Analyze data in a data warehouse (06-data-warehouse.md) - DP-600 lab 5, DP-700 lab 10
Update Microsoft Edge before starting the exercises.
Given the sunsetting of the Default Semantic Model, this lab needs to be updated. An incident has been raised with the authors to do this, so watch this space.
Load data into a warehouse using T-SQL (06a-data-warehouse-load.md) - DP-600 lab 6, DP-700 lab 11
Gotcha: The New SQL Query utton does not put the focus in the editor.
As an aside, I created a Lakehouse using schemas (a preview feature) and did this lab using Staging.Sales. As expected there were no issues.
Query a data warehouse in Microsoft Fabric (06b-data-warehouse-query.md) - DP-600 lab 7
A very straightforward lab with a couple of gotchas.
Gotcha: The Sample Warehouse (NYC taxi data) is case-sensitive.
Gotcha: The columns in the Date dimension table are all varchar! I tried an ORDER BY Month and did not get the results I was expecting. This has to be a mistake, surely? The whole point of Month and MonthName columns is to get sorting and displaying correct.
Monitor a data warehouse in Microsoft Fabric (06c-monitor-data-warehouse.md) - DP-600 lab 8, DP-700 lab 12
A very simple lab. Run a few queries, do nothing with them.
Gotcha: The Sample Warehouse (NYC taxi data) is case-sensitive.
Note the Reaults:1 dropdown in the results pane when the resaults consists of more than one recordset.
Secure a Microsoft Fabric data warehouse (06d-secure-data-warehouse.md) - DP-600 lab 9, DP-700 lab 13
Grant user-2 the rights to connect to your data using the Manage Access button (the lab does not include instructions for this).
Get started with Real-Time Intelligence in Microsoft Fabric (07-real-time-Intelligence.md) - DP-600 lab 14, DP-700 lab 7
Get started with data science in Microsoft Fabric (08-data-science-get-started.md)
Explore data for data science with notebooks in Microsoft Fabric (08a-data-science-explore-data.md)
Preprocess data with Data Wrangler in Microsoft Fabric (08b-data-science-preprocess-data-wrangler.md)
Train and track machine learning models with MLflow in Microsoft Fabric (08c-data-science-train.md)
Generate batch predictions using a deployed model in Microsoft Fabric (08d-data-science-batch.md)
Ingest real-time data with Eventstream in Microsoft Fabric (09-real-time-analytics-eventstream.md) - DP-700 lab 8
Ingest data with Spark and Microsoft Fabric notebooks (10-ingest-notebooks.md)
Use Data Activator in Fabric (11-data-activator.md)
Work with data in a Microsoft Fabric eventhouse (12-query-data-in-kql-database.md) - DP-700 lab 9
Get started with Real-Time Dashboards in Microsoft Fabric (13-real-time-dashboards.md)
Create and explore a semantic model (14-create-a-star-schema-model.md)
Create DAX calculations in Power BI Desktop (14-create-dax-calculations.md) - DP-600 lab 10
No Fabric required - the lab only uses Power BI Desktop.
Note the tab-completion.
Design scalable semantic models (15-design-scalable-semantic-models.md) - DP-600 lab 11
No Fabric required - the lab only uses Power BI Desktop.
Delivery hint: Some part might need to be IL.
Work with model relationships (15-work-with-model-relationships.md)
Create reusable Power BI assets (16-create-reusable-power-bi-assets.md) - DP-600 lab 12
Note that if you modify a template, nochanges are made to existing reports. There is no "linking" between a report and a template like there is in a word processor. Creating an item from a teplace is a one-time load (as is using Import Theme).
Use tools to optimize Power BI performance (16-use-tools-to-optimize-power-bi-performance.md)
Enforce semantic model security (17-enforce-model-security.md) - DP-600 lab 13
Note that the colours of the vosialisation persist (light blue for Australia, dark blue for Canada, and so on).
Monitor Fabric activity in the monitoring hub (18-monitor-hub.md) - DP-700 lab 15
Secure data access in Microsoft Fabric (19-secure-data-access.md) - DP-600 lab 15, DP-700 lab 16
Work with SQL Database in Microsoft Fabric (20-work-with-database.md)
Work with API for GraphQL in Microsoft Fabric (20a-work-with-graphql.md)
Implement deployment pipelines in Microsoft Fabric (21-implement-cicd.md) - DP-700 lab 14