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Farewell Tour


Na bijna 10 jaar waag ik de grote stap. Ik verhuis naar Amerika en ga voor Microsoft in Seattle aan de slag. Daarmee komt er een einde aan mijn werk als Dutch Data Dude in Nederland (en daarbuiten). Werk dat ik met veel passie heb gedaan en heb kunnen doen dankzij en met jullie. Om dit deel gepast af te sluiten doe ik een FAREWELL TOUR: ik klim nog een laatste keer op het podium. Bij jullie, voor jullie. Want jullie hebben mij in staat gesteld te worden wie ik wilde zijn: het geluid van Microsoft rondom data en Artificial Intelligence. Dutch Data Dude signs off.


Tijdens de FAREWELL TOUR geef ik in korte tijd een flink aantal presentaties.
Dit organiseer ik samen met partners in October / November 2018. Het onderwerp van de presentaties is Business Intelligence, Big Data, Machine Learning of Artificial Intelligence. Soms staat mijn presentatie op zich, soms is het onderdeel van een groter geheel. Alles in samenspraak met de organiserende partner. Ik hoop je te mogen begroeten tijdens de FAREWELL TOUR.

Hieronder staan de locaties die ik aandoe tijdens mijn FAREWELL TOUR. Voor publieke evenementen kun je je direct inschrijven via de aangegeven link.

DatumTijdPlaatsOrganisatieSoort evenementInschrijven
17 October10:00BussumCraniumConnectVoor ledenWebsite
17 October17:00Den HaagTopBIInternn.v.t.
18 October17:00AmsterdamQNHInternn.v.t.
23 October16:00AmsterdamDatafiedPubliekInschrijven
25 October18:00RotterdamMotion10PubliekInschrijven
29 October16:30EindhovenValidPubliekInschrijven
30 October13:30VeenendaalHSOOp uitnodigingWebsite
30 October16:00HuizenSigma Data ConsultingInternn.v.t.
31 October17:00VianenRubiconPubliekInschrijven
2 November09:00AmsterdamBreinwaveOp uitnodigingn.v.t.
6 November13:30VeenendaalHillstarPubliekInschrijven
7 November19:00VeenendaalInfosupportPubliek (beperkt aantal plaatsen)Inschrijven
8 November14:30HoofddorpMacawPubliekInschrijven
12 November13:30UtrechtAxiansInternn.v.t.
13 November17:30UtrechtCaesar ExpertsPubliekInschrijven
14 November10:00BussumCraniumConnectVoor ledenWebsite
14 November18:00VianenSogetiInternn.v.t.
15 November15:00AmsterdamIreckonuOp uitnodigingn.v.t.
29 November17:00Den BoschPower BI GebruikersgroepPubliekInschrijven

SQL Server 2017: It’s here!

Anxiously you have waited for this day. The day you could finally get your hands on the latest version of the best database platform available. Today is that day. SQL Server 2017 is available and it runs on Windows, Linux and Docker. Find more info here: What changed? So many things it is hard to mention them all. Have a look at the Ignite sessions to get a feel for what happened.


Microsoft’s Language Understanding Service now speaks Dutch

Over the weekend Microsoft’s Language Understanding Service (LUIS) which is part of the Cognitive Services and the Cortana Intelligence Suite was updated to support the Dutch (Nederlands) language! Great news for anyone who needs to support Dutch in their applications and be able to understand Dutch. We are the 12th language on the list 🙂

Implementing incremental data load using Azure Data Factory

Azure Data Factory is a fully managed data processing solution offered in Azure. It connects to many sources, both in the cloud as well as on-premises. One of the basic tasks it can do is copying data over from one source to another – for example from a table in Azure Table Storage to an Azure SQL Database table. To get the best performance and avoid unwanted duplicates in the target table, we need to include incremental data load or delta’s. Also, we can build mechanisms to further avoid unwanted duplicates when a data pipeline is restarted.

In this post I will explain how to cover both scenario’s using a pipeline that takes data from Azure Table Storage, copies it over into Azure SQL and finally brings a subset of the columns over to another Azure SQL table. The result looks like this:

The components are as follows:

  • MyAzureTable: the source table in Azure Table Storage
  • CopyFromAzureTableToSQL: the pipeline copying data over into the first SQL table
  • Orders: the first SQL Azure database table
  • CopyFromAzureSQLOrdersToAzureSQLOrders2: the pipeline copying data from the first SQL table to the second – leaving behind certain columns
  • Orders2: the second and last SQL Azure database table

Setting up the basics is relatively easy. The devil is in the details, however.

  1. The linked services

Every data pipeline in Azure Data Factory begins with setting up linked services. In this case, we need two; one to the Azure Table storage and one to SQL Azure. The definition of the linked service to Azure Table Storage is as follows:

The SQL Azure linked service definition looks like this:

Note the name property – we will need to refer to it later.

  1. The datasets

Datasets define tables or queries that return data that we will process in the pipeline. The first dataset we need to define is the source dataset (called MyAzureTable). The definition is as follows:

Note that, again, this item has a name. We will use it in the pipeline later. Also, you will need to specify the name of your Azure Table in the “tablename” property. Note that the “LinkedServiceName” property is set to the name of the linked service we definied earlier. This way, Azure Data Factory knows where to find the table. Also, the “availability” property specifies the slices Azure Data Factory uses to process the data. This defines how long ADF waits before processing the data as it waits for the specified time to pass before processing. The settings above specify hourly slices, which means that data will be processed every hour. We will later set up the pipeline in such a way that ADF will just process the data that was added or changed in that hour, not all data available (as is the default behavior). Minimum slice size currently is 15 minutes. Also note that the dataset is specified as being external (“external”:true). This means that ADF will not try to coördinate tasks for this table as assumes the data will be written from somewhere outside ADF (your application for example) and will be ready for pickup when the slice size is passed.

The target dataset in SQL Azure follows the same definition:

Important to note is that we defined the structure explicitly – it is not required for the working of the first pipeline, but it is for the second, which will use this same table as source. Also note that presence of the column ‘ColumnForADuseOnly’ in the table. This column is later used by ADF to make sure data that is already processed is not again appended to the target table. Of course, the SQL table itself will need to have (at least) the same columns and matching data types:

  1. The first pipeline (from Azure Table to SQL)

The first pipeline takes the order data in the Azure table and copies it into the Orders table in SQL Azure. It does that incrementally and with repeatability – which means that a) each slice will only process a specific subset of the data and b) if a slice is restarted the same data will not be copied over twice. This results in a fast processing engine without duplication in the target table – data is copied over once, regardless of the number of restarts. Note that by default ADF copies all data over to the target so you would get so many rows in the table as there are orders in the Azure Table times the number of slices that ran (each slice bringing over the full Azure table). The definition is as follows:

Note that the pipeline consists of a single activity, which is a Copy activity. I could have specified another activity in the same pipeline – I have not done so for simplicity. The Copy activity takes as input the Azure Table (MyAzureTable) and outputs into the SQL Azure Table “Orders”. The source Query is very important – as this is used to select just the data we want! We use the column ‘OrderTimestamp’ which and select only the orders from MyAzureTable where the OrderTimestamp is greater than or equal to the starting time of the slice and less than the end time of the slice. A sample query against the Azure Table executed in this way looks like this:

OrderTimestamp ge datetime’2017-03-20T13:00:00Z’ and OrderTimestamp lt datetime’2017-03-20T15:00:00Z’

Also, look at the specification of the “sliceIdentifierColumnName” property on the target (sink) – this column is in the target SQL Azure table and is used by ADF to keep track of what data is already copied over so if the slice is restarted the same data is not copied over twice.

This pipeline will run each hour (“scheduler” properties), starting at 09:00:00 local clock (“specified by the “start” property) and can run 10 slices in parallel (specified by the “concurrency” property).

  1. The second pipeline (from SQL to SQL)

The second pipeline is there to prove the mapping of specific columns to others as well as showing how to do an incremental load from SQL Azure to another target. Note that I use the same linked service so this exercise is not really useful – the same effect could be retrieved by creating a view. The definition is as follows:

Note that we specify a “sqlReaderQuery” this time which selects the right subset of data for the slice. We use WindowStart and WindowEnd this time instead of SliceStart and SliceEnd earlier. At this point is does not matter as ADF requires both to be the same. WindowStart and WindowEnd refer to the pipeline start and end times, while SliceStart and SliceEnd refer to the slice start and end times. Using the “translator” properties we specify which columns to map – note that we copy over SalesAmount and OrderTimestamp exclusively.

There you have it – a fully incremental, repeatable data pipeline in Azure Data Factory, thanks to setting up a smart source query and using the “sliceIdentifierColumnName” property. The full source code is available on Github. More info on how this works is available in the official documentation.

Questions? Remarks? Let me know!

Dutch elections analyzed in Power BI

Just in time for the elections in the Netherlands: I made an analysis using Power BI of the results of the elections since 1918. It is published (in Dutch) on the official Microsoft the Netherlands blog:


SQL Server news from connect start to resonate

Hi all,

A month ago at the Connect event in New York Microsoft did some anouncements on SQL Server vNext and Azure Data Services.
It’s good to see the traction it’s getting!

I work with a lot of partners on implementing Microsoft stuff and the partners are very keen in getting the latest info on new capabilities.
Also they enlist in new online and in classroom courses on the new stuff. So let’s take a quick look:

  • Azure Data Lake store and analytics went live in Novmeber, This is a no limits data lake with on-demand analytics that instantly scales to your needs. With petabyte size files and trillions of objects and parallel processing!
  • R Server for Azure HD Insight for Largest R compatible parallel analytics ML library with terabyte-scale machine learning!
  • SQL Server on Linux public preview is released. Check in out on a linux box!
  • Moving selected Enterprise Edition capabilities to SQL Server Standard like in memory and Polybase.

And last but not least: connectivity to all top programming languages!


Passing filters to Power BI reports using the URL / query string

It was only recently that I discovered this – you can pass filters to Power BI reports by deep linking to it and adding a filter in the URL (also called query string). I am not even sure this is a feature, since it only seems to work for ‘equals’ and does not work on Dashboards in Power BI.

First of all, we need to understand deep linking in Power BI. Most artifacts in Power BI (for example reports and dashboards) have a their own URL. This means we can open them directly when we know the URL. The URL will have the following format:

https://[tenant][workspace]/[artifact type]/[artifact id]/[optional part to be opened]

Thus, the URL may look like this:

This would open page ReportSection3 in report with id dc121d4b-9aad-4494-b1de-8037f53d8355 in my personal workspace in the tenant app.

If you know anything about web development, you know that you can pass things through the URL to the application this is normally done by adding a question mark to the end of the URL and specifying whatever you want to pass. Multiple items can be added by separating them out by ampersands, like this:

(notice the %20, which is a URL encoded space).

Combining these two things, you can pass parameters to Power BI reports if you have the report URL (simply open the report to get that). Once you have it, add the following:

?filter=[tablename]/[columnname] eq ‘[filter value]’

So, if I wanted to filter the activity column in my workitem table so it only shows items where the activity is blogging, I would add the following:

?filter=workitem/activity eq ‘blogging’

(eq is short for equals here).

This needs, however, to be encoded for the URL. You can easily find tools online to do that for you, or if you know a little bit about what you are doing, you can do it by yourself. The addition above becomes:


(/ becomes %252F, space becomes %20 and ‘ becomes %27)

This would open page ReportSection3 in report with id dc121d4b-9aad-4494-b1de-8037f53d8355 in my personal workspace in the tenant app with a filter set on the workitem table’s activity column to be equal to blogging:

By the way, you would probably only want to use this is very specific scenario’s. It is way better to look at Power BI Embedded to integrate Power BI in your applications. Note that Power BI Embedded is targeted at organizations providing software to others (hosted or not). It is not for internal applications.

Power BI Custom Visual Development Tip: VisualPlugin.ts: Property ‘Visual’ does not exist error

So here you are, creating your very own Power BI custom visual. You have read the documentation and ran the tutorial ( and You feel proud because you are done creating your awesome looking 3d-piechart-barchart-mashup visual. Then it happens. You run: pbiviz start to view your visual and….BAM:

Ouch. Now, before you starting banging your head against the wall until it hurts, here is the solution:

Most probably you have (as good practice dictates) changed the class name of your visual from the default ‘Visual’ to something more interesting, such as MyAwesome3DpieChartBarChartMashupTheDutchDataDudeIsSoGoingToHateThisVisual.

Well, you forgot to change the visualClassName as specified in pbiviz.json so the code can actually find the entry point for your awesome visual. So, quick fix: open pbiviz.json and change the visualClassName property into your class name (which is hopefully not alike the one above). Save the file, re-run pbiviz start and done!

(I know this is a very newbie / getting started type of error, but it took me more than 5 minutes searching for it when I first encountered it. I figured it is worthwhile saving everyone’s time and log it for my own future reference ;))

First look: Esri ArcGIS Maps in Power BI

Esri is a leader in the GIS industry and ArcGIS is a very popular product to build great maps. Now, you can use ArcGIS maps in Power BI (in preview). See the official information here: This is really cool, I know a lot of you have been asking for this for a long time!

You will find the option to enable this preview in, not in the Power BI Desktop. Log in to PowerBI and open the settings. You can find the ArcGIS preview there and enable it by simply selecting the checkbox:

With that enabled, create a report with some geographical information (or edit an existing one). I used the Google Analytics data that keeps track of my blog. Google Analytics data can be loaded into Power BI simply by using the content pack. In edit mode in the report you will find the ArcGIS component in the Visualizations list:

Click it and create your map as you would with the normal map. I noticed it needs some time to build the map (probably due to the preview) but once it is done it is fully interactive with the other items on your report as you would expect:

You can change a lot of the ArcGIS options, such as switching out maps, changing symbol styles, adding reference layers, etc.

I love this – the awesome power of ArcGIS and Power BI combined! I cannot wait to see what you will create with this.

Adding sequence numbers using R in Azure ML

When going through data preparation sometimes sequence numbers need to be added. If you are like me, you probably spent some time looking for a component in Azure ML to do this. I never found it.

Turns out it is really easy to do this in R and as a result also very easy to do in Azure ML.

In your experiment, add an Execute R Script component and connect it to the data flow.

Edit the script and add a column to the dataset that equals:

See my code example:


On the third line the column is added and defined as a sequence number. The resulting dataset indeed has an extra column (called time) that like this:

The small histogram at the top and the details that right confirm it has only unique values and starts at 1; our sequence column has been added!

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