A Fine Slice Of SQL Server
Arun Sirpal wants to restore a database:
Now that we have a Managed Instance built, the next question is how do we get data across? I will break this up into separate posts but the lesson for this blog post is ANALYSIS FIRST!
Click through for the analysis.
Comments closedChad Baldwin wants to check if rows exist before inserting:
I haven’t posted in a while, so I thought I would throw a quick one together to hopefully restart the habit of writing and posting on a regular basis.
One of my first blog posts covered how to only update rows that changed. In that post, I described a popular method that uses
EXISTSandEXCEPTto find rows that had changed while also implicitly handlingNULLvalues.
Click through for two types of technique, one for non-nullable data and one which can include NULL.
Comments closedAveek Das explains the notion of Azure Time Series Insights:
In this article, we are going to learn in detail about Azure Time Series Insights. Microsoft Azure is one of the leading cloud providers these days. With a lot of companies adopting or migrating to the cloud these days, it has become a usual trend to convert existing technologies into cloud-based services and consume them. This not only helps the companies to reduce their cost but also in turn allows them to focus on more business-related problems rather than concentrating on infrastructure costs.
Azure Time Series Insights is one of the cloud services that users can use to integrate with their data that is constantly changing with time such as data from various sensors or machines, data from satellites, airlines etc. Any data that can be generated on a high scale and needs to be analysed, can be used through Azure Time Series Insights. In this article, we will focus on a high-level introduction of this service along with some use cases in detail.
Read on for the article.
Comments closedEvan Seabrook takes us through a Python library:
If you’re really lucky, there will be a docstring for this function that outlines the structure of the parameter user, saving you from having to dig through the function and identify the possible keys that exist in parameter user.
The problem here is twofold:
1. Dictionaries in python are mutable and can have arbitrary schemas.
a. This in itself isn’t a problem and can be a good thing, depending on your needs. Its usage, however, is really only enabled by the quality of the second point, which is:
2. You must rely on the documentation to know the structure, and the documentation must stay updated as the structure evolves.
Read on to see how the dataclass library can create a wrapper around dictionary objects.
Tanishka Garg starts a series on TensorFlow:
TensorFlow is an open-source end-to-end machine learning library. It is for preprocessing data, modeling data, and serving models (getting them into the hands of others).
It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML. And developers easily build and deploy ML-powered applications.
Read on for basic setup instructions and a primer on tensors.
Comments closedKenneth Fisher takes us through a case of deleting rows:
Deleting rows from a table is a pretty simple task right? Not always. Foreign keys, while providing a ton of benefits, do make deletes a bit more complicated.
Click through for an example of this, as well as a quick discussion of cascading deletes, which sound really useful until you make a big mistake. The other problem with cascading deletes is, even if you do intend to delete everything noted, the process is a lot slower than what you can do in batches, and you’re liable to increase the size of your transaction log file to boot.
Comments closedMicrosoft’s recommended SQL Server upgrade workflow is to upgrade to the latest SQL Server but keep the source DB compatibility level, assess the workload impact via establishing a baseline and based on testing move the compatibility level to the latest.
Upon creation of a new user database – the create database sets the compatibility level at the default compatibility level of the SQL Server. Keep in mind – if the model database level is set lower than the create database will set the compatibility level based on the model db
You can also change the database compatibility level at any time
Read on for more information about compatibility mode and some inbuilt guard rails around upgrades. Those guard rails aren’t perfect by any means, but over the past couple of editions, we’ve seen a fair amount of movement toward this ideal of compatibility mode being a guarantee of behavior between versions.
Comments closedGilbert Quevauvilliers writes some Powershell:
In this blog post I am going to show you how to use PowerShell to run a DAX query from my dataset, and then store the results in a CSV file.
I will also include the PowerShell code!
I really liked the awesome blog post by Kay on the Power BI Team which you can find here: Announcing the public preview of Power BI REST API support for DAX Queries
Read on to see what prep work you need to do, as well as the scripts needed to pull this off.
Comments closedKendra Little reviews a tips and tricks guide:
Today I walked through the Use Azure Data Studio to connect and query Azure SQL database Quickstart. This Quickstart is solid and is great for someone new to Azure Data Studio.
At the end of the Quickstart it suggested I try the Tutorial: Use the Transact-SQL editor to create database objects – Azure Data Studio. The tutorial taught me a couple of things that I’ve not noticed about Azure Data Studio, even though I’ve used it for a couple of years.
Read on to see what Kendra learned.
Comments closedMatthew Rhodes, et al, take us through an interesting case study:
According to a recent study, defective products cost industries over $2 billion from 2012–2017. Defect detection within manufacturing is an important business use case, especially in high-value product industries like the automotive industry. This allows for early diagnosis of anomalies to improve production line efficacy and product quality, and saves capital costs. Although advanced anomaly detection systems employ sensors as well as Internet of Things (IoT) devices to collect multimodal data to improve performance, computer vision continues to be a common approach. Detecting anomalies in automotive parts and components using computer vision can be done using normal images, and even X-Ray based images for structural damages. Recent advances in deep learning and computer vision have allowed scientists and manufacturers to develop enhanced anomaly detection systems, including surface defect detection on automotive body panels and dent detection in vehicles.
Read on for case notes.
Comments closed