Understanding Microsoft Purview Pricing

Ever since its official launch around October 2021, Microsoft Purview has been one of the more popular services in Azure, with a steady stream of new features expanding the product greatly beyond its base GA.

For those new to Purview, its a multi-faceted service which includes features such as meta-data catalogue/repository/search, deep scanning and classifications, security/policy features, data sharing, etc – you can see the full details here on MS Docs.

Like all services in Azure, there’s associated costs when using the service, and naturally Microsoft Purview is no different. If interested in reading the standard pricing model for Microsoft Purview it has been outlined here – and follows a similar layout to all Azure price models.

However – as a result of such a broad range of capabilities, its pricing model is one of the more difficult to understand!

Microsoft Purview Pricing Example Deck

To help take price understanding a few steps further, I’ve created a presentation deck which breaks down the pricing into more detailed sections, including the direct or indirect costs, the main key features, which are mandatory, and others which are optional.

The pricing model deck below outlines the how Microsoft Purview is priced in the following areas;

• Indirect Costs
  • Storage, Event Hubs, Private Endpoints, SHIR VM’s, etc

• Direct Costs
  • Elastic Data Map (CU)
  • Scanning and Classification (compute hours)
  • Advanced Resource Sets (compute hours)
  • Data Estate Insights – Report Generation and Consumption (compute hours)

You can download the Presentation Deck here.

Conclusion

OK, so there you have it, a quick summary and approach to the pricing model for Microsoft Purview.

By the way, if I’m missed something super obvious, or you think something needs to be changed, then ping me in the comments and I’ll check it out!

Adios for now… however as usual, and as I always say, review this yourself, as your own mileage may vary!

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Disclaimer: all content on Mr. Fox SQL blog is subject to the disclaimer found here

Timeseries Analytics Capabilities, and Azure Data Explorer (ADX)

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I’ve had a few recent conversations where customers/partners were encountering scale concerns in existing timeseries database applications hosted outside of Azure, and wished to explore the native services in Azure to support their list of critical analytical requirements. After reviewing the list my mind immediately went to the awesome Azure Data Explorer (ADX).

Without going into detail about these scenarios – this blog post explores the types of core functionality that typical timeseries data processing applications seek, and how “out of the box” functionality built into ADX aligns extremely well to meet these challenges head-on.

So given the above, hopefully this breakdown of critical ADX capabilities helps you align your own timeseries database / application needs to Azure Data Explorer!

Where Can I Manage Spatial Data in Azure?

[read this post on Mr. Fox SQL blog]

Every now and then you come across a use-case where you need to do something with spatial data, and you need to do it in the cloud (Azure, of course)! Up until that very point you maybe didn’t know, or perhaps even care, much about the intricacies of spatial data assets, let alone how the heck you were going to store it, process it, and query it, without making a mess of your current data stack.

Well, if you’re that person, then I say welcome to this blog post!

So given the above, hopefully this breakdown of Azure Services that can manage spatial data assets will help you position which service is right fit for your scenario!

The Journey to SQL Server 2019 – where does your own journey start?

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A couple of weeks back I presented the keynote at SQL Server Saturday Sydney, which was an absolute blast. It was my first keynote, so honestly I wasn’t quite sure what to talk about. Now I’ve been to many seminars, conferences, etc and seen many, many key notes – and so in the end I decided to have a bit of “light-hearted” fun and simply tell a story!

So I decided to talk about SQL Server – yes, I know its exciting – but moreso about how it is that we all got to be there in that very room that morning, listening to some guy present a keynote on SQL. And most importantly how everyone else in that room had their own related personal journey with SQL Server.

And so – this journey starts all the way back in 1989; 3 great friends got together one summer and built something AMAZING; SQL Server 1.0. And it ends some 30 years and 13 versions later with SQL Server 2019 – more amazing than anyone could have imagined when they cut that code all those years before.

So sit back, reminisce, and have fun!

Azure Service Logging in the Modern Data Warehouse

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The “modern data platform” architecture is becoming more and more popular as organisations shift towards identifying, collecting and centralising their data assets and driving towards embracing a “data driven culture“.

Microsoft Azure has a suite of best-of-breed PaaS based services which can be plugged together by organisations wishing to create large scale Data Lake / Data Warehouse type platforms to host their critical corporate data.

When working with customers going down the Modern Data Platform path I often hear very similar questions;

• What is the most suitable and scaleable architecture for my use case?
• How should I logically structure my Data Lake or Data Warehouse?
• What is the most efficient ETL/ELT tool to use?
• How do I manage batch and streaming data simultaneously?
• …etc

While these are all very valid questions, sorry, but that’s not what this blog is about! (one for another blog perhaps?)

In my view – what often doesn’t get enough attention up front are the critical aspects of monitoring, auditing and availability. Thankfully, these are generally not too difficult to plug-in at any point in the delivery cycle, but as like with most things in cloud there are just so many different options to consider!

So the purpose of this blog is to focus on the key areas of Azure Services Monitoring and Auditing for the Azure Modern Data Platform architecture.

Machine Learning + DevOps = ML DevOps (Together at Last)

[read this post on Mr. Fox SQL blog]

For the longest time data science was often performed in silos, using large machines with copies of production data. This process was not easily repeatable, explainable or scalable and often introduced business and security risk. With modern enterprises now adopting a DevOps engineering culture across their applications stack, no longer can machine learning development practises operate in isolation from the rest of the development teams.

Thankfully – earlier this year Microsoft GA’d a new service called Azure Machine Learning Services which provides data scientists and DevOps engineers a central place in Azure to create order out of what can be a complicated process.

This blog post outlines the DevOps process when applied to ML. I have also presented on this topic several times, see My Presentation section here – Azure ML DevOps Workflow (wordpress.com)

SQL Saturday 769 Melbourne & 771 Sydney

For those not aware there’s some excellent local SQL events coming up here in Melbourne and Sydney…

• SQL Saturday 769 (Sat 30 Jun 2018) Melbourne.  SQL Saturday 771 (Sat 07 Jul 2017) Sydney.  For those looking for some great free local SQL / Azure / BI / etc learning, you simply cannot go past a SQL Saturday anywhere in the world!  And this one right here in Melbourne will again be no exception.  There is a lineup of fantastic local speakers including Microsoft and MVP’s as well as international speakers too.
• SQL Saturday Pre-Con Training (Fri 29 Jun 2018).  Melbourne.   Leading up to the main event are 3 pre-con training events which cover some very interesting topics around SQL Performance Analysis, Azure SQL Cloud Migrations and Data Science using Azure.

 

SQL Saturday 769 – Melbourne

SQL Saturday is an excellent free learning resource for all things SQL Server – all costs are covered by donations and sponsorships.  Some of the excellent sponsors this year are Microsoft, Wardy IT, SQLBI, and PASS.

Some of the session focus areas include SQL 2017/19 (many deep dives across almost all facets!), SQL DB/DW in Azure, CosmosDB, Azure Machine Learning, R, Data Lakes, BI, DAX, …and so much more!

The event is being held at Northcote Town Hall (189 High Street, Northcote, VIC 3070)

For those wanting to come along here are the links you need to know.  Please go to the website and register to attend.

• Event Home – http://www.sqlsaturday.com/769/EventHome.aspx
• Event Registration – https://www.sqlsaturday.com/769/registernow.aspx
• Session Schedule – (under development, posted soon).  Sneak peek of submitted sessions here – http://www.sqlsaturday.com/769/Sessions/SubmittedSessions.aspx

 

SQL Saturday 769 Pre-Con Training Options

There’s also 3 pre-con training sessions held the day before on Fri 29 Jun 2018.  Definitely work a look in…

• Data Science with Microsoft Cloud – Leila Etaati
• Move Your Database to The Cloud – Jose Manuel
• Practical SQL Server Performance Analysis – Andreas Wolter

Session: Building Streaming Data Pipelines in Azure…

For those attending – I am presenting a pretty fun session on Building Streaming ETL Pipelines Using Azure Cloud Services.  

Session Details here – http://www.sqlsaturday.com/769/Sessions/Details.aspx?sid=78630

We’ll talk though the various different shape, speed and size of data sources available to modern business today, and discuss various PaaS streaming methods available to ingest data at scale all using the Azure Cloud Platform.  I also have a few pretty fun demos which will aim to show how all the Azure services can tie together to perform ETL/ELT!

Feel free to pop in to have a chat!

 

I hope to see you all in Melbourne at SQL Saturday!

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Disclaimer: all content on Mr. Fox SQL blog is subject to the disclaimer found here

Using Elastic Query to Support SQL Spatial in Azure SQL DW

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Recently we had a requirement to perform SQL Spatial functions on data that was stored in Azure SQL DW.  Seems simple enough as spatial has been in SQL for many years, but unfortunately, SQL Spatial functions are not natively supported in Azure SQL DW (yet)!

If interested – this is the link to the Azure Feedback feature request to make this available in Azure SQL DW – https://feedback.azure.com/forums/307516-sql-data-warehouse/suggestions/10508991-support-for-spatial-data-type

AND SO — to use spatial data in Azure SQL DW we need to look at alternative methods.  Luckily a recent new feature in Azure SQL DB  in the form of Elastic Query to Azure SQL DW now gives us the ability to perform these SQL Spatial functions on data within Azure SQL DW via a very simple method!

So the purpose of this blog is to show how to perform native SQL Spatial functions on data within Azure SQL DW.

Azure Cognitive Services API’s with SQL Integration Services Packages (SSIS)

[read this post on Mr. Fox SQL blog]

I had a recent requirement to integrate multi-language support into a SQL DW via a SQL SSIS ETL solution.  Specifically the SQL DW platform currently only supported English translation data for all Dimension tables, but the business was expanding internationally so there was a need to include other language translations of the Dimensional attributes.

We wanted to do this without having to manually translate English text attributes that exist already, or new ones that are added or modified over time.  We wanted an automated method that simply “worked“.

Enter Azure Cognitive Services Translator Text API service!

So the purpose of this blog is to outline the code/pattern we used to integrate the Azure Cognitive Services API into SQL SSIS ETL packages.

Database Backup Options for SQL on Azure IaaS

[read this post on Mr. Fox SQL blog]

Recently I had a requirement to collate and briefly compare some of the various methods to perform SQL Server backup for databases deployed onto Azure IaaS machines.  The purpose was to provide a few options to cater for the different types (OLTP, DW, etc) and sizes (small to big) of databases that could be deployed there.

Up front, I am NOT saying that these are the ONLY options to perform standard SQL backups!  I am sure there are others – however – the below are both supported and well documented – which when it comes to something as critical as backups is pretty important.

So the purpose of this blog is to provide a quick and brief list of various SQL backup methods!