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Category: Cloud

Don’t Miss These Settings in Azure SQL DB

Published 2020-01-14 by Kevin Feasel

Arun Sirpal takes us through a few things administrators tend to miss in Azure SQL Database:

2. Allow Azure Services and resources to access this server setting set to on/off?

I always set this to off. I do not like it ON.

Why? Because I like to control things via vnets (maybe IPs if really needed – it depends on your solution). Nowadays you can use private endpoint connections which allow connections from within a vnet to a private IP.  Sure, you may want to use IP addresses, if you do then I suggest database level firewall rules over server level, especially if you use failover groups.

There are several good ones here.

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Labeling Queries in Azure Synapse Analytics

Published 2020-01-13 by Kevin Feasel

Niko Neugebauer touches on something I want for on-premises SQL Server:

In Azure Synapse Analytics (Azure SQL DW) we have a tool that can help us – the query labels. Firing up the same analytical query, but this time with the OPTION (LABEL = ‘QueryLabelIdentification’) can help us with the identification of the processing. So for the test example I have simply included the format QL – [Query Pupose] where QL stands for Query Labelling:

I think this would have a lot of value on-prem, especially if you are using Query Store.

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Accessing S3 Data from Apache Spark

Published 2020-01-08 by Kevin Feasel

Divyansh Jain shows how we can connect to AWS’s S3 using Apache Spark:

Now, coming to the actual topic that how to read data from S3 bucket to Spark. Well, it is not very easy to read S3 bucket by just adding Spark-core dependencies to your Spark project and use spark.read to read you data from S3 Bucket.

So, to read data from an S3, below are the steps to be followed:

This isn’t a built-in source, so there is a little bit of work to do, but it’s not that bad.

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Registering a Raspberry Pi 4 as an IoT Edge Device

Published 2020-01-02 by Kevin Feasel

Hasan Savran takes us through turning a Raspberry Pi 4 into an Azure IoT Edge device:

You can buy all type of sensors and connect them to Raspberry Pi. Then you can use Python or .NET Core to write small applications to check your connected sensors and read data from the sensors. If you like to push this data to store or analyze in Azure, then you need to make Raspberry Pi ready by installing couple of applications.
      Installing an application in Windows, is not a big deal for me. I had to install and configure all the applications in Linux in this project. First thing we need to do is copying some files to register Microsoft GPG key and software repository feed. To do that, we will use the curl command. Curl is used for transferring data using various protocols including HTTP/S. We are going to use it to copy some files from Internet to local storage. It’s a fancy copy tool.

There are a few steps involved, but nothing too onerous. I think I know where Hasan is going with this, too.

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Azure Synapse Analytics Result Set Caching

Published 2020-01-02 by Kevin Feasel

Niko Neugebauer takes us through result set caching in Azure SQL Data Warehouse Azure Synapse Analytics:

I just put some result on the output, because as you can imagine there are some certain limits on the amount of the output that will be cached and that will be not. Besides the basic logical stuff, such as having deterministic functions only (functions which output will not be varying depending on the execution), not using System Objects or UDFs (and it seems that scalar UDF inlining is not a part of Azure SQL DW yet), no row-level security or column-level security enabled, the main thing and which seems to be pretty good decision as far as I am concerned – the row size larger than 64KB won’t be cached period.

Read on to see what Niko has learned, including cache performing and limitations. Between this and the data pools in SQL Server Big Data Clusters, Microsoft’s spent some time thinking about data caching in cloud-based versions of SQL Server.

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Columnstore Indexes in Azure SQL Database

Published 2019-12-31 by Kevin Feasel

Niko Neugebauer takes us through the columnstore offerings available in Azure SQL Database:

Almost 2 years ago (22nd of March 2018) in Columnstore Indexes – part 121 (“Columnstore Indexes on Standard Tier of Azure SQL DB”) I have already mentioned that Columnstore Indexes were available in Azure SQL Database in Standard 3 (S3) edition and higher, while people I meet keep on mentioning and believing that in order to get Columnstore Indexes one needs to use Premium editions.

Since that blog post a lot of time has passed and in the mean time we have got new tiers with new generations of provisioned General Purpose tiers (Generation 4, Generation 5, FSv2 Series & M Series) appearing, plus the Serverless Tier and not to forget the very promising Hyperscale tier … besides the Azure SQL Database Managed Instance of course, which has already been generally available for some time and the good old Elastic Pools which were never mentioned in original article.

It sounds like, on the whole, columnstore is a normal part of Azure SQL Database across the board—it’s not a special add-on feature.

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Azure Data Factory Notifications

Published 2019-12-31 by Kevin Feasel

Rayis Imayev walks us through three different techniques for sending notifications in Azure Data Factory:

While working on data integration projects and using Azure Data Factory as your main orchestration tool will help you to develop strategic forward thinking about your development tasks: to ponder on what your data sources are, point of destinations to land this information into a new data model and transformation steps to shape data from the source to its destination. Just like when you play chess and have to plan ahead several of your next moves.

Along with this structural thinking to develop and execute your data flows, timely notifications of when something goes left or right would give you additional peace of mind.

Something I appreciate in this post is that Rayis contrasts the Azure Data Factory techniques with SSIS methods, giving you a solid base for comparison.

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Query Concurrency + Azure Synapse Analytics

Published 2019-12-30 by Kevin Feasel

James Serra takes us through query concurrency with Azure Synapse Analytics:

A common question I here from customers is because of the performance of Azure Synapse Analytics (formally called Azure SQL Data Warehouse or SQL DW), can they run Power BI dashboards against it using DirectQuery (and not have to use Azure Analysis Services (AAS), Import the data into Power BI, or use Power BI aggregation tables), avoiding having another copy of the data (saving money), and having data “real time” (as of the last refresh of the data warehouse)?

There are two things to think of in considering an answer this question. The first is if you will get the performance you need (discussed in my last blog), the second is if a certain amount of concurrent queries or connections will cause a problem (the subject of this blog).

Read the whole thing.

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Monitoring Oracle on Azure

Published 2019-12-27 by Kevin Feasel

Kellyn Pot’vin-Gorman covers several tools which are available for working with Oracle databases in Azure:

The Oracle SQL Developer product has come a long way since it’s inception and much of that credit needs to go to the incredible team at Oracle, including those that are prevelant in the Oracle community, Like Jeff Smith, Kris Rice and Ashley Chen.  Their willingness to listen to the Oracle community and turn their needs into features has been one of the critical reasons for the product success.

Although this product is more focused towards the developer, unlike the previous three, I want to point out a few areas that hopefully will convince you there are more similarities than differences.

The shortest version of this is “the same tools as exist on-prem” but if you don’t know that answer, Kellyn’s got you covered.

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Parsing ADF ARM Templates with T-SQL

Published 2019-12-26 by Kevin Feasel

Paul Andrew shows how you can use T-SQL to read an Azure Data Factory ARM template:

While documenting a customers data platform solution I decided it would be far easier if we could summarise the contents of a fairly complex Data Factory using its ARM Template. So, this is what I’ve done using T-SQL to parse the ARM Template JSON and output of series of tables containing details about the factory components.

That is quite the clever solution.

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