Press "Enter" to skip to content

Author: Kevin Feasel

Tearing Down and Rebuilding Replication

Published 2019-08-01 by Kevin Feasel

Pamela Mooney takes us through tearing down replication, restoring a database, and rebuilding transactional replication with scripts:

If you use replication, you have had the situation occur where you had to restore a replicated database.  You’ve have doubtless been paged to restore a replicated database. You have experienced the ineffable joy of being tearing down replication-dependent indexed views (if you have them), blowing away replication, doing the restore, putting replication and indexing back together again, and finally redeploying your indexed views.  I know I have.

In fact, I’ve done it enough times that I didn’t want to do it anymore. So, you may ask, did I go to a different modality of replicating my data?  Did I go to Availability Groups or mirroring instead?  No.  I actually like replication.  It’s invaluable when you need to write code around real-time data (especially from a third party database), but you aren’t able to index the original copy.  It’s been around for a long time and is well vetted, and pretty forgiving, once you understand how it works.  So, no need to reinvent the wheel. I decided to automate replication instead.

This is specific to transactional replication. There’s a whole ‘nother kettle of fish for merge replication.

Comments closed

Getting Powershell 7 Preview on Ubuntu

Published 2019-08-01 by Kevin Feasel

Max Trinidad shows us how to get the Powershell 7 preview without using apt:

First, look under the release documentation and search for the deb package. In my case I’m install the amd64 version.

Then, right-click on the “powershell-preview_7.0.0-preview.2-1.ubuntu.18.04_amd64.deb”, and select “Copy link address“.

This is also something you could script out. It’s quite useful in scenarios where your apt repositories don’t have the latest version of something but you still need or want it.

Comments closed

Exporting Large Data Sets from Power BI

Published 2019-08-01 by Kevin Feasel

Imke Feldmann shows how you can export data in an amount which exceeds Power BI’s native limitations:

Although the method is fairly simple, there are simpler methods if you just need the raw data from your data model (and not the specific aggregations or measures that the visual contains):

– Use DAX Studio to download all tables from your data model at once
– Use DAX Studio to download specific tables from your data model (one by one)
– Or use R or Python to download specific tables if you’re comfortable with these languages. This method also allows scheduled refreshes in the service.

In short, this is probably a fifth-best solution, but it does work.

Comments closed

Updated Power BI Violin Chart

Published 2019-08-01 by Kevin Feasel

Meagan Longoria reviews an update to Daniel Marsh-Patrick’s violin chart custom visual in Power BI:

First, the violin plot is now a certified custom visual. This means that it has been tested by the Power BI team to ensure it meets certain requirements, one of which is that the visual does not access external services or resources. You can be confident your data isn’t being sent externally when you use the violin plot.

As for the functional enhancements, a new legend has been added. This is a great addition to make the chart clearer and more easily read, especially for audiences that may not be familiar with how the violin plot works. The customizable legend calls out what markers are used for mean, median, and quartiles.

Meagan is quite pleased with these updates.

Comments closed

Explaining Tree-Based Algorithms

Published 2019-07-31 by Kevin Feasel

Stephanie Glen takes us through quick explanations of decision trees, random forests, and gradient boosting:

The three methods are similar, with a significant amount of overlap. In a nutshell:

– A decision tree is a simple, decision making-diagram.
– Random forests are a large number of trees, combined (using averages or “majority rules”) at the end of the process.
– Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.

Read on for more details. All three are useful algorithms serving similar but slightly different purposes.

Comments closed

Query Store Required Permissions

Published 2019-07-31 by Kevin Feasel

Andreas Wolter takes us through exactly which permissions are required for Query Store to work:

Typically, there are 3 aspects to the work with the Query Store, which can be reflected in roles:
 
1) Configuration – turning Query Store on and off, clearing the contents, flushing its contents to disk and changing its settings.
2) Viewing the reports or using the DMVs to analyze the Query Store contents (queries, plans and wait statistics) to gain insights but not necessarily having the authority to change anything
3) Actively change Plans by forcing or un-forcing, based on the information obtained from (2)

This is a nice overview of the problem and a fair amount of the solution.

Comments closed

Kubernetes on Windows

Published 2019-07-31 by Kevin Feasel

Elton Stoneman helps us get started with Kubernetes on Windows boxes:

Now you can take older .NET Framework apps and run them in Kubernetes, which is going to help you move them to the cloud and modernize the architecture. You start by running your old monolithic app in a Windows container, then you gradually break features out and run them in .NET Core on Linux containers.

Organizations have been taking that approach with Docker Swarm for a few years now. I cover it in my book Docker on Windows and in my Docker Windows Workshop. It’s a very successful way to do migrations – breaking up monoliths to get the benefits of cloud-native architecture, without a full-on rewrite project.

Now you can do those migrations with Kubernetes. That opens up some interesting new patterns, and the option of running containerized Windows workloads in a managed Kubernetes service in the cloud.

Elton’s not kidding about this support being new. I’m not sure I’d entrust it for my production work just yet, but I’m glad to see people working on the problem.

Comments closed

Explaining Column Statistics

Published 2019-07-31 by Kevin Feasel

Bert Wagner takes us through column statistics in SQL Server:

Statistics are the primary meta data used by the query optimizer to help estimate the costs of retrieving data for a specific query plan.

The reason SQL Server uses statistics is to avoid having to calculate information about the data during query plan generation. For example, you don’t want to have the optimizer scan a billion row table to learn information about it, only to then scan it again when executing the actual query.

Instead, it’s preferable to have those summary statistics pre-calculated ahead of time. This allows the query optimizer to quickly generate and compare multiple candidate plans before choosing one to actually execute.

These statistics aren’t perfect, but life is almost always better when you have accurate, up-to-date statistics on relevant columns.

Comments closed

Microsoft ML Server 9.4

Published 2019-07-31 by Kevin Feasel

Jeroen Ter Heerdt announces Microsoft Machine Learning Server 9.4:

Today we’re excited to announce our latest Microsoft Machine Learning Server 9.4 release, which addresses popular customer requests as well as developments in the R and Python community.

Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features and algorithmic innovation that brings the best of open source and proprietary worlds together.

This is the best way to bind new versions of R and Python to your SQL Server ML Services installation.

Comments closed

Deploying a Big Data Cluster

Published 2019-07-31 by Kevin Feasel

Mohammad Darab takes us through the Big Data Cluster deployment process using Azure Data Studio:

I’ve been “playing around” with Big Data Clusters for some time now and CTP 3.2 is way ahead when it comes to streamlining the BDC deployment process. You can check out my 4-part series on deploying BDC on AKS to see how cumbersome the process used to be. New in CTP 3.2, you can deploy a BDC on AKS (an existing cluster OR a new cluster) using an Azure Data Studio notebook. Let’s see how.

Click through for instructions. It was rather smart of Microsoft to release the instructions as a notebook.

Comments closed