A Fine Slice Of SQL Server
Arun Sirpal shows how to set up geo-replication in Azure Redis Cache:
The concept of a geo-replicated partnership between a primary and secondary node is very similar to that of something you may have seen with Azure SQL DB, where the primary handles all R/W and then the changes are pushed to secondary ( async). This is no different with Redis.
Read on to see what limitations exist and how you can set up geo-replication.
Comments closedGulshan Singh describes three tricky terms for newcomers to functional programming:
Monoid is based on an associative function. Formally, a functor is a type F[A] with an operation
map with type (A => B) => F[B]. In functional programming one typically only deals with one category, the category of types. A functor is an interface with one method i.e a mapping of the category to category. Monads basically is a mechanism for sequencing computations. A monad is a way to wrap stuff, then operate on the wrapped stuff without unwrapping it.
If that wasn’t too clear, check out the post for more detail. And if you want a whole lot more detail, Bartosz Milewski’s YouTube series (and book) on category theory are great resources for dozens of hours of learning.
Comments closedDavid Wilson takes us through debugging Powershell code in Visual Studio Code:
Microsoft has a created a great Powershell extension for VS Code that makes it easy to work with Powershell files. Once the plugin is installed you get intellisense, syntax highlighting, and even visual debugging for files that have a Powershell extension. This debugger can be easily started by pressing F5 when you’re in a Powershell script. Stopping execution on a particular line is as easy as setting a breakpoint by clicking next to a line number.
If you’re coming at debugging from Visual Studio, the VS Code is a little more complicated than what’s in Visual Studio, at least in terms of things to set up first. Once you’ve tried it out a couple of times, though, it’s a pretty convenient experience.
Comments closedARM templates are widely used when we need to repeatedly deploy solutions/infrastructures in the cloud. Leveraging the concept of infrastructure as code ARM templates are a powerful resource to ease our daily job, however we might face some challenges when using them.
When we are creating several resources within the same template – using Json or Bicep – it’s crucial to make sure that all resources are created in the right order, ensuring that all depending on resources are fully provisioned before you move to the next operation.
Error (internal server errors) and conflicts can occur during our ARM template deployment and it could be difficult to troubleshoot or understand the root cause of them.
Read on for one annoying error and its fix.
Comments closedJames Bartlett solves a tricky problem:
Most of you have probably run into a situation where someone in your organization has authored a report in the Power BI web service, and now they want to make changes that can only be done with Power BI Desktop. So, you try to download the PBIX file from the Power BI web service, only to discover that you can’t, because if it wasn’t created as a PBIX, it can’t be downloaded as a PBIX. Infuriating!
Read on for a solution as well as a Powershell function to make it easier.
Comments closedPaul Andrew takes us through a thought process:
In the context of an idealistic data mesh architecture, establishing a working definition of a data product seems to be very real problem for most. What constitutes a data product seems to be very subjective, circumstantial in terms requirements and interlaced with platform technical maturity. AKA, a ‘minefield’ to navigate in definitional terms.
To help get my thoughts in order (as always) here is my currently thinking and definition for a data mesh – data product.
Read on for Paul’s thoughts.
Comments closedMilecia McGregor looks at a version control system for ML projects (and data):
What happens when the machine learning model you’ve worked so hard to get to production becomes stale? Machine learning engineers and data scientists face this problem all the time. You usually have to figure out where the data drift started so you can determine what input data has changed. Then you need to retrain the model with this new dataset.
Retraining could involve a number of experiments across multiple datasets, and it would be helpful to be able to keep track of all of them. In this tutorial, we’ll walk through how using DVC, an open source version control system for machine learning projects, can help you keep track of those experiments and how this will speed up the time it takes to get new models out to production, preventing stale ones from lingering too long.
My team is working on integrating DVC. It’s a really good project for analytics teams, as it extends the notion of version control to datasets and helps you tie in code (source control), models (tools like MLflow), and data.
Comments closedJoe Moser and Yun Gao announce Apache Flink 1.15:
Thanks to our well-organized and open community, Apache Flink continues to grow as a technology and remain one of the most active projects in the Apache community. With the release of Flink 1.15, we are proud to announce a number of exciting changes.
One of the main concepts that makes Apache Flink stand out is the unification of batch (aka bounded) and stream (aka unbounded) data processing, which helps reduce the complexity of development. A lot of effort went into this unification in the previous releases, and you can expect more efforts in this direction.
Apache Flink is not only growing when it comes to contributions and users, but also out of the original use cases. We are seeing a trend towards more business/analytics use cases implemented in low-/no-code. Flink SQL is the feature in the Flink ecosystem that enables such uses cases and this is why its popularity continues to grow.
Flink SQL is Feasel’s Law in action.
Comments closedBrent Ozar explains that index seeks aren’t always as selective as they sound:
When you see “index seek” on an execution plan, that doesn’t mean SQL Server is jumping to exactly the row you’re looking for. It only means that SQL Server is seeking on the first column of the index.
This is especially misleading on indexes where the first column isn’t very selective.
Definitely worth the read.
Comments closedLukas Eder shows off natural joins:
At first I though of the
UNION CORRESPONDINGsyntax, which doesn’t really exist in most SQL dialects, even if it’s a standard feature. But then, I remembered that this is again a perfect use case forNATURAL FULL JOIN, this time slightly differently from the above example where two tables are compared for contents. This time, we want to make sure the two joined tables never have matching rows, in order to get theUNIONlike behaviour.
I wasn’t aware of the notion of natural joins because they’re not available in SQL Server. They are available in Oracle, Postgres, and MySQL. Fun as Lukas’s blog post is, I could see natural joins going wrong in so many ways.
Comments closed