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Category: Versions

Azure Data Studio November 2020 Release

Alan Yu announces the November 2020 release of Azure Data Studio:

Another feature request was to provide support for parameters in a notebook. Parameterization is the ability to execute the same notebook with different parameters.

With this release of Azure Data Studio, users will now be able to utilize Papermill’s ability to parameterize, execute, and store notebooks. By stating the parameters cell as the first code cell in your notebook, it ensures that the injected parameters in the outputted parameterized notebook will be placed directly after the original parameters cell. That way the parameterized notebook will utilize the newly injected parameters instead of the original parameters cell.

Users can utilize Papermill CLI as well as the Python API  to pass in a new set of parameters quickly and efficiently as shown below.

That does look interesting.

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Downgrading SQL Server on Linux

Sreekanth Bandarla wants to roll back cumulative updates on Linux:

Of course you can get this info from SQL or several other ways in Linux. Okay, now we know we got SQL Server 2019 CU5 running on this server to work with. Let’s just assume CU5 broke something in my database and I want to go back to CU4. How do I do that?

Click through to see how to do this for Red Hat (or any system using yum). Debian-based don’t have a downgrade option, but you can use apt-get install mssql-server=[version number] instead.

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Finding and Downloading SQL Server Updates

Andy Levy combines dbatools and KBUpdate:

Another of Chrissy LeMaire’s (blog | twitter) projects is KBUpdate. Compared to dbatools it’s a pretty compact module, but it’s incredibly useful – it’ll seek out information about KB updates and even download them for you! She’s also rolled these functions into dbatools for convenience, so we don’t need to install or import that module separately.

Read on to see how Andy ties it all together.

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The Tuple Mover in SQL Server 2019

Taryn Pratt gives us closure on an issue from a few months back:

I suggest reading my other post first, it’ll only take a few minutes. I’ll wait…

However, if you really don’t want to read it, here’s a quick recap on the initial issue.

In early February 2020, a lot of data was deleted from some clustered columnstore indexes in our PRIZM database. Some of the tables were rebuilt, but 11 tables weren’t since we don’t have maintenance windows, and that would involve downtime. The rebuilds would happen once we upgraded to SQL Server 2019, to take advantage of the ability to rebuild those columnstore indexes online.

Taryn now has the full story and I recommend giving it a read.

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Apache Flink 1.1.0 Released

Marta Paes announces Apache Flink version 1.11:

Change Data Capture (CDC) has become a popular pattern to capture committed changes from a database and propagate those changes to downstream consumers, for example to keep multiple datastores in sync and avoid common pitfalls such as dual writes. Being able to easily ingest and interpret these changelogs into the Table API/SQL has been a highly demanded feature in the Flink community — and it’s now possible with Flink 1.11.

Click through for the full list of updates.

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Using Specific R Package Versions in Docker Images

Roman Lustrik shares how to fix package versions in Docker images:

Using package in R is easy. You install from CRAN using install.packages("packagename"), it resolves dependencies and you’re good to go. What R natively doesn’t handle so well is installing a particular package version without jumping through hoops. Technically you need the source file of the package version you want to install AND all source files of the dependencies (in the correct version, of course). This has been made almost seamless with packages packrat and recently, renv.

This comes handy when you are constructing a Docker file to run in production. Usually you want to run this defensively and do not want things to change from one image build to another. To get there, you can save all your package names and version into a file (renv.lock) and use that to reconstruct the now defined package structure with predictable versions (see renv vignette here).

This is quite useful as R package developers tend not to covet backwards compatibility, and one of the key benefits of containers is to have the option to keep the same code base and configuration in all environments.

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SQL Server 2019 CU5

Microsoft has released SQL Server 2019 CU5:

This article describes Cumulative Update package 5 (CU5) for SQL Server 2019. This update contains fixes that were released after the initial release of SQL Server 2019 and updates the SQL Server and Analysis services components to the following builds.

I see a half-dozen or so PolyBase-related fixes, though I was hoping to read that they’ve fixed the issue around creating and using external objects when you are connected to SQL Server via Windows authentication. I’ll have to test that out to see if it was fixed—that was broken in CU3 and remained so in CU4.

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Azure Data Studio June 2020 Release

Alan Yu announces a new release of Azure Data Studio:

The Data Virtualization extension for Azure Data Studio is now updated with more functionality and a new logo. This update allows you to use the data virtualization wizard to virtualize MongoDB and Teradata data sources into your SQL Server. This new functionality is available for SQL Server 2019 instances running CU5 or later.

To install the extension, search for Data Virtualization in the extension viewlet in Azure Data Studio and click install.

Of course I’m going to clip the bit about PolyBase.

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