We should have known better. Five days ago I wrote that the dishwasher we originally ordered wouldn’t fit in our kitchen and thus went back to the shop we bought it from. Then we quickly decided to order another one, to be delivered a week from now. The gamble we took was to order a ‘second chance’ model instead of a new one. That gamble turned sour, but not for the reason I thought it was a gamble (that it was previously repaired). The Man noticed that the credit card was still not credited for the new order and called customer service. It turned out that the entire order, including the appointment for installation, was cancelled without noticing us. We could have been waiting all week, expecting a new dishwasher next Saturday, for nothing. When on the phone the employee offered to place a new order “and that can be delivered tomorrow”. With that promise he forgot that the delay is in the installation. Two weeks, maybe even longer, to make an appointment for that.
Of course we were livid. The Man send a very lengthy e-mail to customer service this afternoon and as I’m writing this we’re waiting for their response.
Considering the shortage in dishwashers, I didn’t wait for the company to come up with a solution. Their own stock is even lower than when we first ordered, three weeks ago. I checked their site but they have absolutely no model left that fits our kitchen. Acting quickly is key, so again I scoured the web for dishwashers. It really feels like a scavenger hunt by now. I ended up with a company in Dokkum, way up north, that has one Siemens model on stock that suits us. We even called the shop, just before closing hours, to make sure our kitchen panel will indeed fit. They don’t do installation, but I’d rather have a machine and find a local professional to pay for the installation (or even try installing it myself!) than end up without a new dishwasher for many more weeks. We placed our order and now we have to wait and see if indeed this dishwasher can and will be delivered soon. Fingers crossed!
Though talking from the viewpoint of the US, I really enjoyed listening to Allie Ward’s episode Quarantinology. Covid-19 regulations are being lifted, now what? Ward talked to several researchers covering the history of quarantine, what to expect in the near future regarding infection rates and how to deal with settling in to our new lives. Indeed, we’re not getting back to normal. Normal is dead, according to Cole Imperi. Imperi also talks about ‘shadow losses’, things we lost during the pandemic, and our need to grief for them. That part really made sense to me. I highly recommend listening to this episode of Alli Ward’s excellent show.
I am slowly morphing into a true data nerd. This week I started learning the data engineering part of data. Learning about lambda and delta lake architecture, ETL, OLTP, OLAP, Apache Spark, notebooks, parquet files, SCD Types 1 2 3 and 6, pipelines, serverless and dedicated SQL pools, PolyBase and there’s more to come.
Since Fall 2020 I haven’t had that many occasions where I had to pay attention to what I’m wearing. As I spend most of my days sitting at my desk while watching my computer screen, especially since starting my course at Techionista Academy, I prefer to wear loose-fit trousers. Not the sporty kind or the pyjama kind, but nicely designed trousers with an elastic band and soft fabric. For months, the only reason to go outside was to go for a walk and for the larger part I did that at the end of the afternoon when it was already dark. So wearing my loose-fit pants outside was perfectly fine.
The past few weeks I slowly came out of hibernation mode (as Spring just keeps disappointing me). I went shopping on two or three occasions, a good excuse to wear more regular ‘can be seen in this by strangers’-trousers. And yesterday I had to ‘dress up’ for a visit to the exam centre in my home town.
In contrast to many people I didn’t gain corona kilo’s, I lost a few. The consequence is that my preferred pair of trousers for such occasions now require a belt. And after not wearing trousers requiring belts for many months, for the first time in my life I noticed how annoying belts actually are when in desperate need to pull down your trousers for a toilet visit.
The past two weeks I spent most of my time studying for the MS DA-100 exam, also known as ‘Analyzing Data with Microsoft Power BI’. This morning I took the exam and passed with a very decent score of 893/1000 (although I have to admit I was a bit annoyed not breaking the 900 barrier). After the training and passing the exam I am now skilled enough to start my own data analyzing projects. I’m looking for ideas where to apply my new skills.
The past two weeks I was introduced to the ins and outs of Power BI. Four full training days I’ve been practising doing transformations on columns, making calculated measures and dragging columns and measures into visualisations. For those who are not into data analyses, Power BI is a piece of software developed by Microsoft to handle data sets. When spreadsheets are no longer sufficient to handle your data, you can step up the game by using Power BI.
Before this training I practised with SQL and Python to create scatter plots and calculate summations, and I have to admit that after using Power BI I finally understand what kind of actions I was doing to data sets when using Python. Power BI is a visual tool, so you click on the transformations you need to do to prepare your data and the results are immediately visible. And you can easily undo a step with one click.
I wouldn’t say Power BI is data analysis for dummies, because you still need to know conceptually understand what you’re doing to the data, but I totally see why many people prefer using Power BI over messing about with Python. It is visual, quicker and can create interactive reports and dashboards. The reporting part is (for now) least interesting to me, as I don’t work in a big company with lots of (sales) data that needs to flow through the organisation. However, I do feel more confident after the past weeks that I’m capable to get meaningful information from data sets. And that was the whole point of investing in this course.