PowerShell module for Databricks on Azure and AWS:Avaiilable via PowerShell Gallery: DatabricksPS Over the last year I worked a lot with Databricks on Azure and I have to say that I was (and still am) very impressed how well it works and how it integrates with other services of the Microsoft Azure Data Platform like Data Lake Store, Data Factory, etc. Some of the projects I worked on also included CI/CD like pipelines using Azure DevOps where Databricks did not really shine so bright in the beginning. There are no native tasks for it or anything. But this is OK as for those scenarios, where you need Continue reading PowerShell module for Databricks on Azure and AWS→
Storing Images in a PowerBI/Analysis Services Data Models:As some of you probably remember, when PowerPivot was still only available in Excel and Power Query did not yet exist, it was possible to load images from a database (binary column) directly into the data model and display them in PowerView. Unfortunately, this feature did not work anymore in PowerBI Desktop and the only way to display images in a visual was to provide the URL of the image which is public accessible. The visual would then grab the image on-the-fly from the URL and render it. This of course has various drawbacks: The image needs to be available Continue reading Storing Images in a PowerBI/Analysis Services Data Models→
Deploying an Azure Data Factory project as ARM Template:In my last post I wrote about how to Debug Custom .Net Activities in Azure Data Factory locally. This fixes one of the biggest issues in Azure Data Factory at the moment for developers. The next bigger problem that you will run into is when it comes to deploying your Azure Data Factory project. At the moment, you can only do it manually from Visual Studio which, for bigger projects, can take quite some time. So I extended and advanced the code from my CustomActivityDebugger. Well, actually I rewrote some major parts of it and moved it into a new Continue reading Deploying an Azure Data Factory project as ARM Template→
Error-handling in Power Query:Data is the daily bread-and-butter for any analyst. In order to provide good results you also need good data. Sometimes this data is very well prepared beforehand and you can use it as it is but it is also very common that you need to prepare and transform the data on your own. To do this Microsoft has introduced Power Query (on tool of the Power BI suite). Power Query can be used to extract, transform and load data into Excel and/or Power Pivot directly. When using any data you usually know what the data looks like and what to Continue reading Error-handling in Power Query→
A very common requirement for a Power BI report that I stumble across at almost all of my customers is to automatically show data for the current day/month/year when a report is opened. At first sight this seems like a very trivial problem but once you dig into the problem, you will realize that all of the common solutions out there have some disadvantages and only solve the problem partially.
So here is what we want to achieve:
Show the Current Month (or Day, or Year) by default
Works [in combination] with all other columns in the date table.
A single, easy to use slicer/filter to control the time selection and change from Current Month/Day/Year to any other value
Works with built-in time intelligence functions
Works with existing DAX measures
Works with any datamodel/report
Solutions like Relative Time Filter/Slicer, DAX or relative flags in the date table address only some points of the above list but definitely not all of them which is why I thought we need a better solution to this:
(please use full-screen mode)
We actually created a new table in our data model that is linked to the original date table. The reason why we cannot use the same table here is that the new table does not have unique date values as all dates/rows referring to our current calculations are duplicated. It has to be a many-to-one relationship with cross-filter direction set to both (even though we will only use the new table ‘Calendar_with_current’ to filter the existing table ‘Calendar’):
And that’s it basically. You can now exchange the original Calendar table with the new one to get the new “Current” values in your report. If you have time intelligence functions in place, you further need to extend them and add ALL('Calendar_with_current ') as a filter to make them work also with the new table. The old table can also be hidden now if you do not want to confuse the end users. To make a seamless switch you can further rename the tables.
I added an additional column to the table called Type that allows you to select which values you want to show – the original values (e.g. “September”), the values with “Current X” (e.g. “Current Month”), or both.Please see the second page/tab of the embedded report above.
So this raises the question how this new table can be created? To simplify this I have created a Power Query function that takes 3 parameters:
The current date table
A list of definitions of your current-values
The name of the unique date-column in your current date table (parameter 1)
The first and the third parameter should be clear, but what are the “CurrentDefinitions”?
It is basically a table which defines the relative time calculations that you want to extend your existing date table with. Here is an example:
The column Column refers to the column in which you want to create the relative date definition. The column NewValue specifies the value that you want to set for rows that match the third column Filter. The column Filter either takes a static filter expression like [RelativeMonth] = 1 (as in lines 5-8) but can also use existing M-functions and reference the existing Date-column using the placeholder <<DateColumn>> as you can see in lines 1-4.
The table can be maintained using “Enter Data” and can contain any number of rows/definitions!
For most of my scenarios this works pretty well and addresses all major problems highlighted above.
When working with Power Query, you have probably already realized that every expression you write returns a value of a specific type. Usually this will be a primitive type like text, number, or date. (A full list of types available in Power Query can be found here: https://docs.microsoft.com/en-us/powerquery-m/m-spec-types). If for some reason the type of an expression cannot be defined, the special type *any* will be used. For sure you already encountered this when using Table.AddColumn which, by default, results in the new column being of type *any*.
To avoid this, you can use the optional fourth parameter and specify the resulting type of the expression. This can be very handy and saves you the Change Type step that usually comes afterwards.
This fourth parameter not only works for primitive types but also for complex types. If you do not specify it, the column type is again *any* even though the actual values are records:
Once you click the Expand-Button of the new MyRecord column in the table header, you will realize a short delay until the available fields are displayed. This indicates that PQ first has to evaluate the expression before it can provide you the list of fields within the record. For complex scenarios, this can take a long time and can also be avoided by explicitly specifying the type in the fourth parameter as shown below:
As you can see, PQ can now immediately display the available columns without having to evaluate the function!
This also works the very same way if you call a custom function as expression of your Table.AddColumn. But there is the caveat: If you have a function that returns a complex type, let’s say a record, you will usually want to specify the type as part of the function or within the definition of the function and not re-type it again each time you call the function.
Fortunately, there is a solution to this problem: the function Type.FunctionReturn. In combination with Value.Type you can derive the return type from the function dynamically!
You will realize that now again it takes some time until the available fields are displayed indicating the function must be evaluated first. Another indicator for this to happen is the warning at the bottom and the link to “Load more”. If you think of it, this makes sense – Power Query knows that the function now returns a record, but does not know which fields the record contains and thus has to evaluate it. So how can we combine custom function that return complex types and the ability to specify the resulting type as part of the function?
The first thing that would come to your mind is to simply strong-type the return type of the function specifying each field individually, but this will result in an error:
Currently it is not supported to specify a complex type as the return type of a function – it only works with primitive types. But as you can guess, I did not start this blog post for no reason. There is a way to achieve this, even though it may not be as nice as it could and should be.
The solution here is the Type.ForFunction function which allows you to create a more specific definition of your function including the return type. This definition/type can then be applied to your original function using Value.ReplaceType:
You basically first define the final return type of the function and the function itself (lines 2 and 3). The other lines (5 to 10) take care of applying the return type to the function which can then be used in combination with the approach above to dynamically derive the return type when calling the function (using ype.FunctionReturn and Value.Type). This now allows you to specify everything that is related to the function in one place!
This is especially handy if you have a function that returns a record or a table which is re-used multiple times and the fields/columns may change over time. Using this approach allows you to only change the function and everything else is derived automatically.
Working with analytical data platforms and big data on a daily basis, I was quite happy when Microsoft finally announced a connector for Parquet files back in November 2020. The Parquet file format is developed by the Apache foundation as an open-source project and has become a fundamental part of most data lake systems nowadays.
“Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.”
However, Parquet is just a file format and does not really support you when it comes to data management. Common data manipulation operations (DML) like updates and deletes still need to be handled manually by the data pipeline. This was one of the reasons why Delta Lake (delta.io) was developed besides a lot of other features like ACID transactions, proper meta data handling and a lot more. If you are interested in the details, please follow the link above.
So what is a Delta Lake table and how is it related to Parquet? Basically a Delta Lake table is a folder in your Data Lake (or wherever you store your data) and consists of two parts:
Delta log files (in the sub-folder _delta_log)
Data files (Parquet files in the root folder or sub-folders if partitioning is used)
The Delta log persists all transactions that modified the data or meta data in the table. For example, if you execute an INSERT statement, a new transaction is created in the Delta log and a new file is added to the data files which is referenced by the Delta log. If a DELETE statement is executed, a particular set of data files is (logically) removed from the Delta log but the data file still resides in the folder for a certain time. So we cannot just simply read all Parquet files in the root folder but need to process the Delta log first so we know which Parquet files are valid for the latest state of the table.
These logs are usually stored as JSON files (actually JSONL files to be more precise). After 10 transactions, a so-called checkpoint-file is created which is in Parquet format and stores all transactions up to that point in time. The relevant logs for the final table are then the combination of the last checkpoint-file and the JSON files that were created afterwards. If you are interested in all the details on how the Delta Log works, here is the full Delta Log protocol specification.
From those logs we get the information which Parquet files in the main folder must be processed to obtain the final table. The content of those Parquet files can then simply be combined and loaded into PowerBI.
I encapsulated all this logic into a custom Power Query function which takes the folder listing of the Delta table folder as input and returns the content of the Delta table. The folder listing can either come from an Azure Data Lake Store, a local folder, or an Azure Blob Storage. The mandatory fields/columns are [Content], [Name] and [Folder Path]. There is also an optional parameter which allows you the specify further options for reading the Delta table like the Version if you want to use time-travel. However, this is still experimental and if you want to get the latest state of the table, you can simply omit it.
The most current M-code for the function can be found in my Github repository for PowerBI: fn_ReadDeltaTable.pq and will also be constantly updated there if I find any improvement. The repository also contains an PowerBI desktop file (.pbix) where you can see the single steps that make up for the final function.
Once you have added the function to your PowerBI / Power Query environment you can call it like this:
I would further recommend to nest your queries and separate the access to the storage (e.g. Azure Data Lake Store) and the reading of the table (execution of the function). If you are reading for an ADLS, it is mandatory to also specify [HierarchicalNavigation = false] ! If you are reading from a blob storage, the standard folder listing is slightly different and needs to be changed.
Right now the connector/function is still experimental and performance is not yet optimal. But I hope to get this fixed in the near future to have a native way to read and finally visualize Delta lake tables in PowerBI.
After some thorough testing the connector/function finally reached a state where it can be used without any major blocking issues, however there are still some known limitations:
currently columns used for partitioning will always have the value NULLFIXED!
values for partitioning columns are not stored as part of the parquet file but need to be derived from the folder pathFIXED!
is currently not great but this is mainly related to the Parquet connector as it seems
very much depends on your data – please test on your own!
currently only supports “VERSION AS OF”
need to add “TIMESTAMP AS OF”
Predicate Pushdown / Partition Elimination
currently not supported – it always reads the whole tableFIXED!
Calculating and visualizing semi- and non-additive measures like distinct count in Power BI is usually not a big deal. However, things can become challenging if your data volume grows and exceeds the limits of Power BI!
In one of my recent projects we wanted to visualize data from the customers analytical platform based on Azure Databricks in Power BI. The connection between those two tools works pretty flawless which I also described in my previous post but the challenge was the use-case and the calculations. We wanted to display the distinct customers across various aggregations levels over a billion rows fact table. We came up with different potential solutions all having their pros and cons:
load all data into Power BI (import mode) and do the aggregations there
use Power BI with direct query and let the back-end do the heavy lifting
load only necessary pre-aggregated data into Power BI (import mode)
Please keep in mind that we are dealing with a distinct count measure here. Semi- and Non-additive measure like this cannot easily be aggregated from lower levels to higher levels without having all the detail data available!
Option 1. has the obvious drawback that data model would be huge in size as we were dealing with billions of transactions. This would have exceeded our current size limits for Power BI data models.
Option 2. would usually work fine, but again, for the amount of data we were dealing with the back-end was just no able to provide sub-second latency that was required.
So we went for Option 3. and did the various aggregations on the different levels in Azure Databricks and loaded only the final results to Power BI. First we wanted to use Power BI Aggregations and Composite Models. Unfortunately, this did not work out for us as we were not in control which aggregation table (we had multiple for the different aggregation levels) was used by the engine which potentially resulted in wrong results when additional aggregation was done in Power BI. Also, when slicing for random aggregation levels, Power BI was querying the details in direct query mode causing very poor query performance.
After some further thinking we came up with a new solution which was also based on pre-calculated aggregations but not realized using built-in aggregation tables but having a combined table for all aggregations and some very straight-forward DAX to select the row we wanted! In the end the whole solution consisted of one SQL view using COUNT(DISTINCT xxx) aggregation and GROUP BY GROUPING SETS (T-SQL, Databricks, … supported in all major SQL engines) and a very simple DAX measure!
Here is a little example that illustrates the approach. Assume you want to calculate the distinct customers that bought certain products in a subcategory/category by year. The first step is to create a view that provides this information:
Please note that when we have a natural relationship between hierarchy levels (= only 1:n relationships) we need to specify the current level and also all upper levels to allow a proper drill-down later on! E.g. ProductCategory (1 -> n) ProductSubcategory
This calculates all the different aggregation levels we need. Columns with NULL mean they were not filtered/grouped by when calculating the aggregation. Rows 80-84 contain the aggregations grouped by Year only whereas rows 77-79 contain only aggregates by ProductCategoryKey. The rows 75-76 were aggregated by Year AND ProductCategoryKey. Depending on your final report layout, you may not need all of them and you should consider removing those that are not needed!
This table is then loaded into Power BI. You can either use a custom SQL query like above in Power BI directly or create a view in the back-end system which would be my preferred solution. Alternatively you can also create all these grouping sets using Power Query/M. The incredible Imke Feldmann (t, b) came up with a solution that allows you to specify the grouping sets in a similar way as in SQL and do all this magic within Power BI directly! I hope she will blog about it pretty soon! (The sample workbook at the end of this post also contains a little preview of this M-magic.)
Now that we have all the data we need in Power BI, we need to display the right values for the selections in the report which of course can be dynamic. That’s a bit tricky but once you understand the concept, it is pretty straight forward. First of all, the table containing the aggregations must not be related to any other table as we build them on the fly within our DAX measure. The table itself can also be hidden.
The first part is to get all the selected values of the lookup/dimension tables the user selects on the report. These are all the _sel_XXX variables. SELECTEDVALUE() returns the selected value if only one item is in the current filter context and BLANK()/NULL otherwise. We then use TREATAS() to apply those filters (either a single item or NULL) to our aggregations table. This should usually only return a table with a single row so we can use MAXX() to get our actual value from that one row. I also added a check in case multiple rows are returned which can potentially happen if you use multi-selects in your filters and instead of showing wrong values I’d rather indicate that there is something wrong with the calculation.
The measure can then be sliced and diced by our pre-defined aggregation levels as if it would be a regular measure but instead of having to process those expensive calculations on the fly we use the pre-calculated aggregates!
One thing to be aware of is that it will produce wrong results if multiple items for any of the aggregation levels are selected so it is highly recommended to set all slicers/filters to single select only or ensure that the filtered aggregation levels are also used in the chart. In this case only the grand total will show wrong values or NULL then. This could also be fixed in the DAX measure by checking how many rows are actually selected for each level and throw an error in case it is used in a filter and the count of values is > 1.
I did some further thinking and this approach could probably also be used to mimic custom roll-ups and unary operators we know from Analysis Services Multidimensional cubes. If I find some proper examples and this turns out to be feasibly I will write another blog post about it!
I work a lot with Azure Databricks and a topic that always comes up is reporting on top of the data that is processed with Databricks. Even though notebooks offer some great ways to visualize data for analysts and power users, it is usually not the kind of report the top-management would expect. For those scenarios, you still need to use a proper reporting tool, which usually is Power BI when you are already using Azure and other Microsoft tools.
So, I am very happy that there is finally an official connector in PowerBI to access data from Azure Databricks! Previously you had to use the generic Spark connector (docs) which was rather difficult to configure and did only support authentication using a Databricks Personal Access Token.
With the new connector you can simply click on “Get Data” and then either search for “Azure Databricks” or go the “Azure” and scroll down until you see the new connector:
The next dialog that pops up will ask you for the hostname and HTTP path – this is very similar to the Spark connector. You find all the necessary information via the Databricks Web UI. As this connection is always bound to an existing cluster you need to go the clusters details page and check the Advanced Tab “JDBC/ODBC” as described here: (NOTE: you can simply copy the Server Hostname and the HTTP Path from the cluster page)
The last part is then the authentication. As mentioned earlier the new connector now also supports Azure Active Directory authentication which allows you to use the same user that you use to connect to the Databricks Web UI! Personal Access Tokens are also still supported and there is also Basic authentication using username/password.
Once you are connected, you can choose the tables that you want to import/connect and start building your report!
Here is also a quick overview which features are supported by the Spark and the Azure Databricks connector as there are some minor but important differences:
Power BI Desktop
Power BI Service
Direct Query (Desktop)
Direct Query (Service)
Manual Refresh (Service)
Scheduled Refresh (Service)
Azure Active Directory (AAD) Authentication
Personal Access Token Authentication
Performacne Improvements with Spark 3.x
Supports On-Premises data gateway
Features supported by Spark and Databricks Connector for PowerBI
*) Updated 2020-10-06: the new Databricks Connector for PowerBI now supports all features also in the PowerBI service!
Update 2020-10-06: So from the current point of view the new Databricks Connector is a superset of old Spark Connector with additional options for authentication and better performance with the latest Spark versions. So it is highly recommended to use the new Databricks Connector unless you have very specific reasons to use the Spark connector! Actually the only reason why I would still use the Spark connector is the support for the On-Premises data gateway in case your Spark or Databricks cluster is hosted in a private VNet.
So currently the generic Spark connector still looks superior simply for the support in the Power BI Service. However, I am quite sure that it will be fully supported also by the Power BI Service in the near future. I will update this post accordingly! On the other hand, Azure Active Directory authentication is a huge plus for the native Azure Databricks connector as you do not have to mess around with Databricks Personal Access Tokens (PAT) anymore!
Another thing that I have not yet tested but would be very interesting is whether Pass-Through security works with this new connector. So you log in with your AAD credentials in Power BI, they get passed on to Databricks and from there to the Data Lake Store. For Databricks Table Access Control I assume this will just work as it does for PAT as it is not related to AAD authentication.
Paul Andrews (b, t) recently blogged about HOW TO USE ‘SPECIFY DYNAMIC CONTENTS IN JSON FORMAT’ IN AZURE DATA FACTORY LINKED SERVICES. He shows how you can modify the JSON of a given Azure Data Factory linked service and inject parameters into settings which do not support dynamic content in the GUI. What he shows with Linked Services and parameters also applies to Key Vault references – sometimes the GUI allows you to reference a value from the Key Vault instead of hard-coding it but for other settings the GUI only offers a simple text box:
As You can see, the setting “AccessToken” can use a Key Vault reference whereas settings like “Databricks Workspace URL” and “Cluster” do not support them. This is usually fine because the guys at Microsoft also thought about this and support Key Vault references for the settings that are actually security relevant or sensitive. Also, providing the option to use Key Vault references everywhere would flood the GUI. So this is just fine.
But there can be good reasons where you want to get values from the Key Vault also for non-sensitive settings, especially when it comes to CI/CD and multiple environments. From my experience, when you implement a bigger ADF project, you will probably have a Key Vault for your sensitive settings and all other values are provided during the deployment via ARM parameters.
So you will end up with a mix of Key Vault references and ARM template parameters which very likely will be derived from the Key Vault at some point anyway. To solve this, you can modify the JSON of an ADF linked service directly and inject KeyVault references into almost every property! Lets have a look at the JSON of the Databricks linked service from above:
As you can see in lines 8-15, the property “accessToken” references the secret “Databricks-Accesstoken” from the Key Vault linked service “KV_001” and the actual value is populated at runtime.
After reading all this, you can probably guess what we are going to do next – We also replace the other properties by Key Vault references:
You now have a linked service that is configured solely by the Key Vault. If you think one step further, you can replace all values which are usually sourced by ARM parameters with Key Vault references instead and you will end up with an ARM template that only has two parameters – the name of the Data Factory and the URI of the Key Vault linked service! (you may even be able to derive the Key Vaults URI from the Data Factory name if the names are aligned!)
The only drawback I could find so far was that you cannot use the GUI anymore but need to work with the JSON from now on – or at least until you remove the Key Vault references again so that the GUI can display the JSON properly again. But this is just a minor thing as linked services usually do not change very often.
I also tried using the same approach to inject Key Vault references into Pipelines and Dataset but unfortunately this did not work 🙁 This is probably because Pipelines and Datasets are evaluated at a different stage and hence cannot dynamically reference the Key Vault.
Databricks recently announced that it is now also supporting Azure Active Directory Authentication for the REST API which is now in public preview. This may not sound super exciting but is actually a very important feature when it comes to Continuous Integration/Continuous Delivery pipelines in Azure DevOps or any other CI/CD tool. Previously, whenever you wanted to deploy content to a new Databricks workspace, you first needed to manually create a user-bound API access token. As you can imagine, manual steps are also bad for otherwise automated processes like a CI/CD pipeline. With Databricks REST API finally supporting Azure Active Directory Authentication of regular users and service principals, this last manual step is finally also gone!
As I had this issue at many of my customers where we had already fully automated the deployment of our data platform based on Azure and Databricks, I also wanted to use this new feature there. The deployment of regular Databricks objects (clusters, notebooks, jobs, …) was already implemented in the CI/CD pipeline using my PowerShell module DatabricksPS and of course I did not want to rewrite any of those steps. So, I simply extend the module’s authentication methods to also support Azure Active Directory Authentication. The only thing that actually changed was the call to Set-DatabricksEnvironment which now supports additional parameter sets and parameters:
The first thing you will realize is that it is now necessary to specify the Databricks Workspace explicitly either using SubscriptionID/ResourceGroupName/WorkspaceName to uniquely identify the Databricks workspace within Azure or using the OrganizationID that you see displayed in the URL of your Databricks Workspace. For the actual authentication the parameters -ClientID, -TenantID, -Credential and the switch -ServicePrincipal are used.
As you can see, once the environment is set up using the new authentication methods, the rest of the script stays the same and there is not much more you need to do fully automate your CI/CD pipeline with DatabricksPS!
I have not yet fully tested all cmdlets of the module so if you experience any issues, please contact me or open a ticket in the GIT repository.
When working with Databricks you will usually start developing your code in the notebook-style UI that comes natively with Databricks. This is perfectly fine for most of the use cases but sometimes it is just not enough. Especially nowadays, where a lot of data engineers and scientists have a strong background also in regular software development and expect the same features that they are used to from their original Integrated Development Environments (IDE) also in Databricks.
For those users Databricks has developed Databricks Connect (Azure docs) which allows you to work with your local IDE of choice (Jupyter, PyCharm, RStudio, IntelliJ, Eclipse or Visual Studio Code) but execute the code on a Databricks cluster. This is awesome and provides a lot of advantages compared to the standard notebook UI. The two most important ones are probably the proper integration into source control / git and the ability to extend your IDE with tools like automatic formatters, linters, custom syntax highlighting, …
While Databricks Connect solves the problem of local execution and debugging, there was still a gap when it came to pushing your local changes back to Databricks to be executed as part of a regular ETL or ML pipeline. So far you had to either “deploy” your changes by manually uploading them via the Databricks UI again or write a script that uploads it via the REST API (Azure docs).
NOTE: I also published a PowerShell module that eases the automation/scripting of these tasks also as part of CI/CD pipeline. It is available from the PowerShell gallery DatabricksPS and integrates very well with this VSCode extension too!
However, this is not really something you would call a “seamless experience” so I also started working on an extension for Visual Studio Code to work more efficiently with Databricks. It has been in the VS Code gallery (Databricks VSCode) for about a month now and I received mostly positive feedback so far. Now I am at a stage where I want to get more people to use it – hence this blog post to announce it officially. The extension is currently published under GPLv3 license and is free to use for everyone. The GIT repository is also linked in the VS Code gallery if you want to participate or have any issues with the extension.
It currently supports the following features:
Up-/download of notebooks and whole folders
Compare/Diff of local vs online notebook (currently only supported for raw files but not for notebooks)
Execution of local code and notebooks against a Databricks Cluster (via Databricks-Connect)
Script cluster definition as JSON
View job-run history + status
Script job definition as JSON
Script job-run output as JSON
(also works with mount points!)
Create/delete secret scopes
Support for multiple Databricks workspaces (e.g. DEV/TEST/PROD)
Easy configuration via standard VS Code settings
More features to come in the future but these will be mainly based on the requests that come from users or my personal needs. So your feedback is highly appreciated – either directly here or using the feedback section in the GIT repository.
I will also write some follow up post to show you how to work in the most efficient way using this new VSCode extension in combination with your Databricks workspace so stay tuned!
Foreword: The approach described in this blog post only uses the Databricks REST API and therefore should work with both, Azure Databricks and also Databricks on AWS!
It recently had to migrate an existing Databricks workspace to a new Azure subscription causing as little interruption as possible and not loosing any valuable content. So I thought a simple Move of the Azure resource would be the easiest thing to do in this case. Unfortunately it turns out that moving an Azure Databricks Service (=workspace) is not supported:
Resource move is not supported for resource types ‘Microsoft.Databricks/workspaces’. (Code: ResourceMoveNotSupported)
I do not know what is/was the problem here but I did not have time to investigate but instead needed to come up with a proper solution in time. So I had a look what needs to be done for a manual export. Basically there are 5 types of content within a Databricks workspace:
Workspace items (notebooks and folders)
Security (users and groups)
For all of them an appropriate REST API is provided by Databricks to manage and also exports and imports. This was fantastic news for me as I knew I could use my existing PowerShell module DatabricksPS to do all the stuff without having to re-invent the wheel again. So I basically extended the module and added new Import and Export functions which automatically process all the different content types:
They can be further parameterized to only import/export certain artifacts and how to deal with updates to already existing items. The actual output of the export looks like this and of course you can also modify it manually to your needs – all files are in JSON except for the notebooks which are exported as .DBC file by default:
A very simple sample code doing and export and an import into a different environment could look like this:
Having those scripts made the whole migration a very easy task. In addition, these new cmdlets can also be used in your Continuous Integration/Continuous Delivery (CI/CD) pipelines in Azure DevOps or any other CI/CD tool!
So just download the latest version from the PowerShell gallery and give it a try!
In my previous post I showed how you can use Microsoft Power BI to create a Data Virtualization layer on top of multiple relational data sources querying them all at the same time through one common model. As I already mentioned in the post and what was also pointed out by Adam Saxton (b, t) in the comments is the fact, that this approach can cause serious performance problems at the data source and also on the Power BI side. So in this post we will have a closer look on what actually happens in the background and which queries are executed when you join different data sources on-the-fly.
We will use the same model as in the previous post (you can download it from there or at the end of this post) and run some basic queries against it so we can get a better understanding of the internals. Here is our relationship diagram again as a reference. Please remember that each table comes from a different SQL server:
In our test we will simply count the number of products for each Product Subcategory:
Even though this query only touches two different data sources, it is a good way to analyze the queries sent to the data sources. To track these queries I used the built-in Performance Analyzer of Power BI desktop which can be enabled on the “View”-tab. It gives you detailed information about the performance of the report including the actual SQL queries (under “Direct query”) which were executed on the data sources. The plain text queries can also be copied using the “Copy queries” link at the bottom. In our case 3 SQL queries were executed against 2 different SQL databases:
The query basically selects two columns from the DimProductSubcategory table:
ProductSubcategoryKey – which is used in the join with DimProduct
ProductSubcategoryName – which is the final name to be displayed in the visual
The inner sub-select (line 7-14) represents the original Power Query query. It selects all columns from the DimProductSubcategory table and renames [EnglishProductSubcagetoryName] to [ProductSubcategoryName] (line 10). Any other Power Query steps that are supported in direct query like aggregations, groupings, filters, etc. would also show up here.
(SELECTN'Mountain Bikes'AS[c67],1AS[c29])UNION ALL
(SELECTN'Road Bikes'AS[c67],2AS[c29])UNION ALL
(SELECTN'Touring Bikes'AS[c67],3AS[c29])UNION ALL
(SELECTN'Bottom Brackets'AS[c67],5AS[c29])UNION ALL
(The query was shortened at line 16 and line 29 as the removed columns/rows are not relevant for the purpose of this example.)
Similar to Query 1 above, the innermost sub-select (line 13-17) in the FROM clause returns the results of the Power Query query for DimProduct whereas the outer sub-select (line 7-20) groups the result by the common join-key [ProductSubcategoryKey]. This result is then joined with a static table which is made up from hard-coded SELECTs and UNION ALLs (line 24-30). If you take a closer look, you will realize that this table actually represents the original result of Query 1! Additionally it also includes a special NULL-item (line 30) that is used to handle non-matching entries. The last step is to group the joined tables to obtain the final results.
(The query was shortened at line 9 as the removed columns/rows are not relevant for the purpose of this example.)
The last query is necessary to display the correct grand total across all products and product sub-categories.
As you can see, most of the “magic” happens in Query 2. The virtual join or virtualization is done by hard-coding the results of the remote table/data source directly into the SQL query of the current table/data source. This works fine as long as the results of the remote query are small enough – both, in terms of numbers of rows and columns – but the more limiting factor is the number of rows. Roughly speaking, if you have more than thousand items that are joined this way, the queries tend to get slow. In reality this will very much depend on your data so I would highly recommend to test this with your own data!
I ran a simple test and created a join on the SalesOrderNumber which has about 27,000 distinct items. The query never returned any results and after having a look at the Performance Analyzer I realized, that the query similar to Query 2 above was never executed. I do not know yet whether this is because of the large number of items and the very long SQL query that is generated (27,000 times SELECT + UNION ALL !!!) or a bug.
At this point you may ask yourself if it makes sense to use Power BI for data virtualization or use another tool that was explicitly designed for this scenario. (Just google for “data virtualization”). These other tools may perform better even on higher volume data but they will also reach their limits if the joins get too big and, what is even more important, the are usually quite expensive.
So I think that Power BI is still a viable solution for data virtualization if you keep the following things in mind: – keep the items in the join columns at a minimum – use Power Query to pre-aggregate the data if possible – don’t expect too much in terms of performance – only use it when you know what you are doing 🙂