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Month: September 2019

Reporting Services and SPNs

Greg Dodd shares a couple tips on creating SPNs for SQL Server Reporting Services:

Reporting Services often requires an SPN assigned to the account running the Reporting Services Service. You’ll know that you need to set this up when you try connecting to your Reporting Services instance from within the same domain and you are prompted for credentials. If SPN’s are setup correctly then your browser will work out the authentication for you and your users won’t need to login again.

Read on for an example, but also a pitfall and how to avoid it.

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Intellisense and the DAC

Slava Murygin doesn’t like severity 20 errors just popping up for no good reason:

Yesterday I’ve needed to use Dedicated Administrator Connection (DAC) once in a while, and because I have all kinds of notifications in my system, I immediately got an “Severity 20” alert.

As you probably know, Severity 20 Errors “Indicate system problems and are fatal errors” (See books online: https://docs.microsoft.com/en-us/sql/relational-databases/errors-events/database-engine-error-severities?view=sql-server-2017)

Even though “Severity 20” does not indicate any problems with data and belong only to a user process it is still worth to investigate the problem.

Read on to see the cause of Slava’s problem and how there’s no way to fix it in SSMS.

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Progressive Disclosure

Marc Lelijveld continues a series on storytelling with Power BI:

Progressive disclosure
It is all about giving that little bit more insights which can be done in many ways. For example, you want to show the sales by product category, which you’ve put in a bar chart. Looking at these bars, you might be interested in the number of manufactures involved in these sales amounts for product category. You can create a stacked barchart representing the different manufacturers in a legend. Or you can use another chart in your report to represent the top 5 products, which will interact with the sales over time chart. But both options will use additional space on your report canvas and look a bit messy, which can distract the users of where it is all about.

Marc is wrapping up the series and it’s worth the read.

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Tuning YARN

Dmitry Tolpeko helps us tune YARN settings:

Sometimes it may take a few iterations to find the proper container size, but usually it helps and the query succeeds.

But what if you set the container size 4096 MB or 8192 MB but the query could complete successfully even with 2048 MB?

Read on to learn more.

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On-Prem Data, Azure Apps

Jamie Wick helps us figure out how to keep our data local while using Azure services:

One of the challenges many organizations face when beginning to work with Azure applications (PowerBI, PowerApps, Flow, etc.) is that their data is on-premise and the applications are hosted in the cloud. Moving the data to the cloud is often cost-prohibitive and there can be operational requirements that prevent the data, or the systems hosting it, from being relocated to the cloud.

So, how can on-prem data be used with Azure apps?

Read on for more.

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Processing JSON in Biml

Bill Fellows takes us through a library which (seemingly by law) must be in every .NET project:

#sqlhelp #biml I would have the metadata in a Json structure. How would you parse the json in the C# BIML Script? I was thinking use Newtonsoft.Json but I don’t know how to add the reference to it

Adding external assemblies is a snap but here I’ll show how to use the NewtonSoft Json library to parse a Json based metadata structure and then use that in our Biml.

Click through to learn how.

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Using Azure Storage Explorer

Arun Sirpal takes us through Azure Storage Explorer:

I only ever use the storage explorer when managing my blobs, files, queues within storage accounts. It is your single view access point for all your storage needs and I totally recommend downloading it and using it (https://azure.microsoft.com/en-gb/features/storage-explorer/).

Why do I like using it? I am sure there are more reasons, but these are personal to me.

Click through for Arun’s reasons as well as installation basics.

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Dealing with Thousands of Databases

Andy Levy has some Q&A about dealing with large numbers of databases on a single server. Part one:

What was the most difficult challenge faced initially with a large environment and how does that challenge relate to now?

For me personally, it was just getting a handle on how to deal with this many databases because I didn’t “grow up” with the system. I walked into an environment with a lot of established tools and procedures for performing tasks and had to learn how those all fit together while also not breaking anything. You don’t want to be the person who walks in the door, says “why are you doing things like this, you should be doing it this other way” and then falls victim to hubris. If something seems unusual, there’s probably a reason for that and you need to understand the “why” before trying to change anything.

Part 2 is also up:

How large is the team that manages the databases? Is the knowledge shared and everyone can work on everything or do these people fill niches?

There are two of us. We each have a few specialties but we aren’t “territorial” and we try to share as much as possible. If we aren’t both directly involved in a given project, we keep each other in the loop as it progresses.

Stay tuned for part 3.

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PolyBase and Dockerized Hadoop

I have a solution to a problem which vexed me for quite some time:

Quite some time ago, I posted about PolyBase and the Hortonworks Data Platform 2.5 (and later) sandbox.

The summary of the problem is that data nodes in HDP 2.5 and later are on a Docker private network. For most cases, this works fine, but PolyBase expects publicly accessible data nodes by default—one of its performance enhancements with Hadoop was to have PolyBase scale-out group members interact directly with the Hadoop data nodes rather than having everything go through the NameNode and PolyBase control node.

Click through for the solution.

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Null Checks in Spark DataFrames

Bipin Patwardhan gives us four techniques for validating whether data in Spark exists:

The task at hand was pretty simple — we wanted to create a flexible and reusable library of classes that would make the task of data validation (over Spark DataFrames) a breeze. In this article, I will cover a couple of techniques/idioms used for data validation. In particular, I am using the null check (are the contents of a column ‘null’). In order to keep things simple, I will be assuming that the data to be validated has been loaded into a Spark DataFrame named “df.”

Click through for those techniques.

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