A Fine Slice Of SQL Server
Mark Kromer shows off a few functions in Azure Data Factory to modify data in arrays:
The first transformation function is map() and allows you to apply data flow scalar functions as the 2nd parameter to the map() function. In my case, I use upper() to uppercase every element in my string array:
map(columnNames(),upper(#item))
Read on for more iteration and aggregation functions akin to map, reduce, and filter.
Comments closedThe Hadoop in Real World team show us a few ways to add jar files to a Spark application:
There are so many properties in Spark that affect the way you can add jars to a Spark application. We understand it could be confusing and this post is aimed at giving you clarity on different options and when to use which option.
Read on for the options.
Comments closedVikas Hazrati takes us through the pros and cons of using event sourcing for a project:
You would always get a ton of literature on Event Sourcing and CQRS. The key question is WHEN do you use it? Under what circumstances? Is your problem really in need of ES?
I would not go into the details of what Event Sourcing and CQRS is. The industry stalwarts have covered that in adequate detail. This post delves into battle-tested scenarios on where we should have used and otherwise ignored ES.
Click through for an analysis of pros and cons, as well as some advice on what it all means.
Comments closedSergey Tihon notices a problem:
After reading all these 3 samples I realised that I do not fully understand what is Label column is used for. Later I came to a conclusion that all three samples most likely are incorrect and here is why.
Click through for a description of the problem as well as the answer.
Comments closedEmanuele Meazzo needs to pull data from Oracle into SQL Server:
The most atrocious part of my search for glory was without doubt navigating all the packages to download and install for each component, between broken links and differences between the instructions and the actual content, it’s a mess.
It took a while, based on Emanuele’s tone. With SQL Server 2019, you can avoid some of this pain by using PolyBase. But for prior versions of SQL Server, your options are more limited.
Comments closedMarco Russo and Alberto Ferrari cover a pain point in Power BI:
The VisibleProducts variable contains a list of products for the selection currently displayed in the visual. In the example, we have the top 3 products for each Product Category included in our report. The ranking that is returned is only up to the value selected in the TopN parameter – for this reason, we can use the result of Ranking by Sales to filter the visual, including only the products ranked in the 1-to-TopN Value range. We use a filter in the Power BI filter pane to accomplish this task.
This is a common enough pattern that I do wish Power BI made it easy.
Comments closedWolfgang Strasser has a project for us:
In todays blog post I would like to build an end-to-end solution to combine data coming from different sources and stored in different form factors into a single Power BI data model using Azure Synapse Analytics.
Click through for the full demo.
Comments closedWhen I was learning statistics, I was surprised by how few learning materials I personally found to be clear and accessible. This might be just me, but I suspect I am not the only one who feels this way. Also, everyone’s brain works differently, and different people would prefer different explanations. So I hope that this will be useful for people like myself – social scientists and economists – who may need a simpler and more hands-on approach.
These series are based on my notes and summaries of what I personally consider some the best textbooks and articles on basic stats, combined with the R code to illustrate the concepts and to give practical examples. Likely there are people out there whose cognitive processes are similar to mine, and who will hopefully find this series useful.
This is clear and well-written, so check it out even if you feel like you have a solid understanding of the topic.
Comments closedHui Li developed a flow for determining appropriate machine learning algorithms:
Since the cheat sheet is designed for beginner data scientists and analysts, we will make some simplified assumptions when talking about the algorithms.
The algorithms recommended here result from compiled feedback and tips from several data scientists and machine learning experts and developers. There are several issues on which we have not reached an agreement and for these issues we try to highlight the commonality and reconcile the difference.
Additional algorithms will be added in later as our library grows to encompass a more complete set of available methods.
Read the whole thing.
Comments closedWhen I wrote we’d look at it in a future post I thought to myself; “how hard can it be?”. I had read the steps of how to build a Python language extension for Windows here, and it didn’t seem that hard: some Boost, CMake, compile, and Bob’s your uncle! Well, it turned out it was somewhat more complicated than what I anticipated. So, if you are interested – read on!
I was going to say that the steps seem a bit complicated but not overly terrible, though Niels’s conclusion leaves me wondering.
Comments closed