It’s December 31st, 2019. WHAAAAAT? 🤯 I have no idea how we’re almost in 2020, but here we are! Just a few hours left of the year. (Hi to my friends around the world who are already in 2020! 👋🏻) Like many others, I enjoy reflecting on the year that’s almost over. This year, I’ve decided to collect some of my highlights from 2019.
(Warning! There will be lots of tweets and pictures.)
This is a total brag fest that I’m writing solely for myself. It’s my 2019 highlight reel that I can look back on when days get rough and I need a reminder that life is actually pretty awesome and I’m insanely lucky and privileged to be here. And when we get to 2025, future Cathrine can re-read everything and go “oh yeah, I remember that, we were so young and inexperienced back then, awww!” …like I do now with my old posts. It’s fun. You should try it! 😁
Lessons Learned in 2019
I also started writing about some of the more difficult parts of my year and what I learned from it… And in the middle of it, I realized that I’m not quite ready to share those thoughts yet. I still have lots of processing to do before I can turn my struggles into any kind of useful advice for others. I’m hoping to be able to do that in 2020.
After reading that book in 2018 and reflecting on it for all of 2019, I’ve started learning to take responsibility for my own feelings, to set healthy boundaries for myself, and to choose my f*cks wisely.
For the past 25 days, I have written one blog post per day about Azure Data Factory. My goal was to start completely from scratch and cover the fundamentals in casual, bite-sized blog posts. This became the Beginner’s Guide to Azure Data Factory. Today, I will share a bunch of resources to help you continue your own learning journey.
I’ve already seen from your questions and comments that you are ready to jump way ahead and dive into way more advanced topics than I ever intended this series to cover 😉 And as much as I love Azure Data Factory, I can’t cover everything. So a little further down, I will share where and how and from who you can continue learning about Azure Data Factory.
Congratulations! You’ve made it through my entire Beginner’s Guide to Azure Data Factory 🤓 We’ve gone through the fundamentals in the first 23 posts, and now we just have one more thing to talk about: Pricing.
And today, I’m actually going to talk! You see, in November 2019, I presented a 20-minute session at Microsoft Ignite about understanding Azure Data Factory pricing. And since it was recorded and the recording is available for free for everyone… Well, let’s just say that after 23 posts, I think we could both appreciate a short break from reading and writing 😅
(And as a side note, I’m originally publishing this post on December 24th. Here in Norway, we celebrate Christmas all day today! This is the biggest family day of the year for me, full of food and traditions. So instead of spending a lot of time writing today, I’m going to link to my video and spend the rest of the day with my family. Yay! 🎅🏻🎄🎁)
In the previous post, we looked at foreach loops and how to control them using arrays. But you can also control them using more complex objects! In this post, we will look at lookups. How do they work? What can you use them for? And how do you use the output in later activities, like controlling foreach loops?
Lookups are similar to copy data activities, except that you only get data from lookups. They have a source dataset, but they do not have a sink dataset. (So, like… half a copy data activity? :D) Instead of copying data into a destination, you use lookups to get configuration values that you use in later activities.
And how you use the configuration values in later activities depends on whether you choose to get the first row only or all rows.
But before we dig into that, let’s create the configuration datasets!
In the previous post, we looked at how to use variables in pipelines. We took a sneak peek at working with an array, but we didn’t actually do anything with it. But now, we will! In this post, we will look at how to use arrays to control foreach loops.
You can use foreach loops to execute the same set of activities or pipelines multiple times, with different values each time. A foreach loop iterates over a collection. That collection can be either an array or a more complex object. Inside the loop, you can reference the current value using @item().
Let’s take a look at how this works in Azure Data Factory!