On June 20-21, I will be enjoying bratwurst and beer in Lingen, Germany – while speaking at DataGrillen 2019!
I’m so happy and thankful to be a part of this event again. Last year, when it was called SQLGrillen, there were a total of 35 sessions. This year, the event has grown to 50 sessions over 2 days! More time to learn and network, yay :)
DataGrillen 2019 Schedule
I will be presenting my session Uhms and Bunny Hands: Tips for Improving Your Presentation Skills on Friday, June 21st. This is a fun session to present, because I will show you all the presentation mistakes I’ve made over the years so you can learn what NOT to do :) I will also share tips on how to craft your session to help your attendees remember your key messages, how to prepare for demo failures and worst-case scenarios, and how to build confidence as a new speaker.
If you are looking for more technical sessions, check out the full schedule and speaker list. All I can say is: wow. That is one impressive line-up!
In 2019, the Azure Data Factory team announced two exciting features. The first was Mapping Data Flows (currently in Public Preview), and the second was Wrangling Data Flows (currently in Limited Private Preview). Since then, I have heard many questions. One of the more common questions is “which should I use?” In this blog post, we will be comparing Mapping and Wrangling Data Flows to hopefully make it a little easier for you to answer that question.
Should you use Mapping or Wrangling Data Flows?
Now, we all know that the consultant answer to “which should I use?” is It Depends ™ :) But what does it depend on?
To me, it boils down to a few key questions you need to ask:
What is the task or problem you are trying to solve?
Where and how will you use the output?
Which tool are you most comfortable using?
Before we dig further into these questions, let’s start with comparing Mapping and Wrangling Data Flows.
In a previous blog post, we looked at how to generate SQL using Biml. (If you haven’t read that post, you may want to start there and then come back here.) In this post, we will go through how to generate SELECT statements using the Biml column method GetColumnList.
Using Biml column methods
Biml column methods return code fragments. These code fragments can be used as building blocks to generate custom T-SQL statements. For example, the GetColumnList method returns a list of columns, separated by commas, that you can use in a SELECT statement. You can filter the columns and customize the output by passing parameters.
Examples of GetColumnList code fragments
If you have a table with three columns, the default output will look something like this:
[PersonID], [FirstName], [LastName]
But what if you don’t want to select all three columns? Or what if you want to use an alias for your table? No problem! The customized output can look something like this instead:
We will go through the different ways of customizing the output a little later in this post.
PASS turns 20 this April! To celebrate 20 years of educating data professionals, they are organizing a 24 Hours of PASS with the theme Past Learnings and Future Visions. From April 3-4, you can watch 24 free webinars back-to-back. If you can’t attend all 24 webinars live (who can!?), they will all be recorded. Woohoo!