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Author: Kevin Feasel

Named Volumes in Docker with SQL Server 2019

Andrew Pruski takes us through one of the biggest changes with SQL Server 2019 in containers:

I’ve seen a few people online asking how to use docker named volumes with the new SQL Server 2019 RTM images. Microsoft changed the way SQL runs within a container for the RTM versions, SQL no longer runs as the root user.

This is a good thing but does throw up some issues when mounting volumes to create databases on.

Let’s run through what the issue is and how to overcome it.

Click through to see what you need to add to your DOCKERFILE to get things working.

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Installing Power BI Gateway

Paulina Nowinska shows how to install the Power BI Gateway in its two separate modes:

This On-premises was created for a multi-developer environment. Here multiple people can work on the same Data Gateway if the administrator authorized them before. With this Data Gateway, you will have much more fun than Personal Mode. Why? Because of on-premises support not only Power BI just like his brother Personal Mode. Here you can provide quick and secure data transfer between data which is not in the cloud and Microsoft cloud services: PowerApps, Microsoft Flow, Azure Analysis Services, and Logic Apps and of course Power BI. Depending on your needs, you can choose one of them.

Click through for step-by-step instructions on both techniques.

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Cross-Validation Versus Regularization

Nina Zumel takes us through a pair of techniques for avoiding overfitting:

Cross-validation is relatively computationally expensive; regularization is relatively cheap. Can you mitigate nested model bias by using regularization techniques instead of cross-validation?

The short answer: no, you shouldn’t. But as, we’ve written before, demonstrating this is more memorable than simply saying “Don’t do that.”

Definitely worth the read.

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Fun with CHAR(0)

Kenneth Fisher learns a bit about the 0 byte:

Ok, now things are getting interesting. An ASCII value of 0? I’ve never heard of that. I honestly didn’t know it was possible. As it happens, yes, it’s a real value and in SSMS it does a few strange things.

In the comments Denis Gobo is right: the 0 byte is the null terminator, which should appear at the end of a variable-length string to indicate that there’s nothing more to read there.

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View Filters and Short-Circuiting

Reitse Eskens takes us through a fun oddity with short-circuiting and views:

The question from my coworker was simple. Why is this happening? Because he’s selecting from the view, his instinct is that the returned result set should be filtered within the view first and that the resultset can be narrowed down further with the regular query.

It’s interesting that there’s deterministic behavior both ways. My recollection is that ANSI SQL does not honor short-circuiting, as all filters are considered to happen at the same time, and thus any ordering is valid. But in practice, there are places where different code bases end up with stable short-circuiting as an implementation detail.

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Limitations with Memory-Optimized TempDB Metadata

Milos Radivojevic takes us through a few limitations in memory-optimized TempDB metadata tables in SQL Server 2019:

When we are about to enable a new feature, one of the first things we have to check is whether enabling this feature will break the existing code. Enabling this feature could bring two breaking changes: one is related to columnstore indexes, the other to transactions with memory-optimized tables.

I don’t think these limitations are that game-breaking, but if you’re regularly loading large tables in tempdb and using columnstore indexes on them, you might be in for a nasty surprise.

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Your Power BI Administrator’s Privileges

Melissa Coates goes into exactly what it is that a Power BI admin can see and do:

I wrote about (and updated) this topic previously, but this is so important that it warrants revisiting. So let’s have a quick chat about what privileges a Power BI administrator has with respect to accessing data throughout the Power BI tenant.

All metadata throughout the tenant is available to the Power BI administrator (ex: if they want to enumerate a list of workspaces, reports, dashboards, etc using the APIs). So, metadata is easily discoverable but — technically speaking — a Power BI administrator cannot access datasets in Power BI unless they have permission to that workspace. However…

Read the whole thing.

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Evolution of the Data Lake

Jim Wankowski takes us through the history of data lakes:

It is important to understand the difference between data lakes and data warehouses. A data warehouse is highly structured. Much effort is done upfront in developing schemas and hierarchies prior to the data being loaded into a warehouse. There is no hierarchy or structure to the way data is stored in a data lake. The structure is applied afterward. There can be multiple schemas applied to the same data in a data lake.

Read on to learn how the data lake concept has evolved over the past few years.

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Use SQL for XML and JSON Creation

Lukas Eder argues that if you’re storing the data in SQL and you need to get data from a database into JSON or XML format, just use SQL for that:

In English: We need a list of actors, and the film categories they played in, and grouped in each category, the individual films they played in.

Let me show you how easy this is with SQL Server SQL (all other database dialects can do it these days, I just happen to have a SQL Server example ready:

Lukas makes a great point and has a FAQ to follow up on it. If there’s a reason for mapping at a higher layer—if you’re actually adding value rather than building out a set of converters—that’s one thing, but if you’re just accepting a data set and returning a JSON blob…well, your database product can do that too.

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