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Author: Kevin Feasel

Introducing PowerApps

Jason Thomas has a great introduction to PowerApps:

you will be able to pass context aware data to a PowerApps app which updates in real time as you make changes to your report. Now, your users can derive business insights and take actions from right within their Power BI reports and dashboards. No need to switch tabs to open the separate apps, copy paste data from one window to another or worry about fat fingering the wrong customer id or invoice amount.

If you think about it, this is a game changer – you finally have a BI tool that allows you to collaborate and take actions right within the report. How many times have you looked at a report, found out an insight and wished that you could send an email to the account manager, only to forget later? Well, now you don’t have to worry about that, as I am going to show you an example of how we can collaborate by adding comments within the report (not just comments, but context aware comments, based on what you are selecting) as well as show how to send emails (to the appropriate people based on your selection).

This is an interesting concept and Jason has a detailed overview of it.

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Customizing SQL Operations Studio

Samir Behara shows how to differentiate environments with custom tab colors:

The initial January release insiders build focuses on bug fixes and minor feature improvements. One thing which caught my attention was the ‘SQL Editor Tab Color‘ to differentiate between query tabs inside the IDE.

Using Custom Color to differentiate between environments is one of my favorite feature inside SQL Server Management Studio. The color is displayed in the SSMS status bar, at the bottom. Hence when you connect with a particular environment, it uses the same assigned color. This presents a visual indication of the environment in which you are running your scripts. Lot of 3rd Party tools from RedGate and ApexSQL also has their own versions of setting different colors while connecting to different environments.

Using environment-specific color schemes can be a life-saver.

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The Premise Of Cloud Data Warehousing

Derik Hammer explains how cloud data warehouses differ from their on-prem cousins:

Given the data processing needs of a data warehouse, they tend to be implemented on massively parallel processing (MPP) systems. The MPP architecture replies upon a shared nothing concept for distributing data across various slices. Compute nodes are layered on top of the storage and processes queries for data residing in its local slice. The control node is responsible for taking a query and dividing it up into smaller queries to be run in parallel on the compute nodes.

Read the whole thing.

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Monitoring Using SQL Operations Studio

Marlon Ribunal demonstrates how to build a widget in SQL Operations Studio:

This is where SQL Operations Studio (SOS) comes in the picture. SOS is a lightweight, cross-platform client. It is also open-source. Aside from its IDE functionalities, SOS has a lot of more offerings for DBAs, Devs, and DevOps. One of these is that you can use it as a dashboard to monitor your databases. SSMS comes with standard reports as well as custom reports using Report Definition Language or RDL (which is essentially an SSRS-like report residing in SSMS). SQL Operations Studio raised the bar in this regard. You can create multiple widgets to display on your dashboard.

Click through for a demonstration and screen shots.

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Migrating A Memory-Optimized Database

Michael Bourgon notes that there’s an extra step when migrating a database with a memory-optimized filegroup from one server to another:

So, I was trying to get an in-memory database moved from one server to another (long story, involving IMOLTP melting down on me and resulting in a 2 terabyte log file).

I thought it’d be simple: copy the files over, along with the Filestream folders, and attach.  Nope!  Various errors, including “5(Access is denied.)”.

Click through for the steps involved.

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Analyzing Web Server Logs With Spark

Fisseha Berhane uses web server log analysis to contrast three methods of using Spark:

This is the third tutorial on the Spark RDDs Vs DataFrames vs SparkSQL blog post series. The first one is available here. In the first part, we saw how to retrieve, sort and filter data using Spark RDDs, DataFrames and SparkSQL. In the second part (here), we saw how to work with multiple tables in Spark the RDD way, the DataFrame way and with SparkSQL. In this third part of the blog post series, we will perform web server log analysis using real-world text-based production logs. Log data can be used monitoring servers, improving business and customer intelligence, building recommendation systems, fraud detection, and much more. Server log analysis is a good use case for Spark. It’s a very large, common data source and contains a rich set of information.

This tutorial shows you three different ways to solve several problems, including file sizes, counts by response code, top endpoints, etc.

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File Growth Trace Flags

Jason Brimhall investigates trace flags 1117 and 1118 and how they work in SQL Server 2016 versus older editions:

With the release of SQL Server 2016, these trace flags were rumored to be a thing of the past and hence completely unnecessary. That is partially true. The trace flag is unneeded and SQL 2016 does have some different behaviors, but does that mean you have to do nothing to get the benefits of these Trace Flags as implemented in 2016?

As it turns out, these trace flags no longer do what they did in previous editions. SQL Server now pretty much has it baked into the product. Buuuuut, do you have to do anything slightly different to make it work? This was something I came across while reading this post and wanted to double check everything. After all, I was also under the belief that it was automatically enabled. So let’s create a script that checks these things for me.

Click through for the script and a summary of his findings.

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Choose Your Own Regression Adventure

Jim Frost explains when you might use different types of regression analysis:

Regression analysis mathematically describes the relationship between a set of independent variables and a dependent variable. There are numerous types of regression models that you can use. This choice often depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. In this post, I cover the more common types of regression analyses and how to decide which one is right for your data.

I’ll provide an overview along with information to help you choose. I organize the types of regression by the different kinds of dependent variable. If you’re not sure which procedure to use, determine which type of dependent variable you have, and then focus on that section in this post. This process should help narrow the choices! I’ll cover regression models that are appropriate for dependent variables that measure continuous, categorical, and count data.

It’s a good overview of several techniques.

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Failure To Connect With A SQL Login

Bert Wagner hits on the most common reason why you might fail to connect with a SQL authentication login:

I thought it would be best to start with a clean slate so I created a new SQL login and database user so that I could definitively figure out which permissions are needed.

Normally I use Windows Authentication for my logins, but this time I thought “since I’m getting crazy learning new things, let me try creating a SQL Login instead.”

After I created my login, I decided to test connecting to my server before digging into the permissions. Result?

After the fifth or sixth time it happens to you, you start making that the first thing you check.

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Fun With Undocumented Trace Flags

Joe Obbish has a list of 45 undocumented trace flags:

Below is a list of trace flags which, as far as I can tell, have never been publicly documented. I did not fully investigate many of them and many of the descriptions are just guesses. I make no guarantees and none of these should be used in production. All tests were performed on SQL Server 2017 CU2 with trace flags enabled at the global level.

This is combining a bit of database archaeology and database anthropology.

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