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Category: Cloud

Azure SQL Data Warehouse GA

James Serra notes that Azure SQL Data Warehouse is now generally available:

In brief, SQL DW is a fully managed data-warehouse-as-a-service that you can provision in minutes and scale up in seconds.  With SQL DW, storage and compute scale independently.  You can dynamically deploy, grow, shrink, and even pause compute, allowing for cost savings.  Also, SQL DW uses the power and familiarity of T-SQL so you can integrate query results across relational data in your data warehouse and non-relational data in Azure blob storage or Hadoop using PolyBase.  SQL DW offers an availability SLA of 99.9%, the only public cloud data warehouse service that offers an availability SLA to customers.

The pricing calculator now reflects GA prices.

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SSMS And Azure SQL Data Warehouse

Rob Farley reports that we can now use Management Studio to connect to Azure SQL Data Warehouse:

One of the biggest frustrations that people find with SQL DW is that you need (or rather, needed) to use SSDT to connect to it. You couldn’t use SSMS. And let’s face it – while the ‘recommended’ approach may be to use SSDT for all database development, most people I come across tend to use SSMS.

But now with the July 2016 update of SSMS, you can finally connect to SQL DW using SQL Server Management Studio. Hurrah!

…except that it’s perhaps not quite that easy. There’s a few gotchas to be conscious of, plus a couple of things that caused me frustrations perhaps more than I’d’ve liked.

Yes, it’s never quite that easy…  Read the whole thing.

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Always Encrypted In Azure SQL Database

Jakub Szymaszek notes that Azure SQL Database can now support Always Encrypted:

I’m happy to announce Always Encrypted in Azure SQL Database is now generally available!

Always Encrypted is a feature designed to ensure sensitive data and its corresponding encryption keys are never revealed in plaintext to the database system. With Always Encrypted enabled, a SQL client driver encrypts and decrypts sensitive data inside client applications or application servers, by using keys stored in a trusted key store, such as Azure Key Vault or Windows Certificate Store on a client machine. As a result, even database administrators, other high privilege users, or attackers gaining illegal access to Azure SQL Database, cannot access the data.

To be honest, I’d much rather try Always Encrypted against an Azure SQL Database instance than an on-premise instance, mostly because if I hose Azure SQL Database that badly or the company decides that Always Encrypted isn’t a good fit, I can grab the data and dump the instance.  It’s a little harder to do that with physical hardware or even an on-prem VM.

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Azure Automation

Steph Locke looks at Azure Automation:

Azure Automation is essentially a hosted PowerShell script execution service. It seems to be aimed primarily at managing Azure resources, particularly via Desired State Configurations.

It is, however, a general PowerShell powerhouse, with scheduling capabilities and a bunch of useful features for the safe storage of credentials etc. This makes it an excellent tool if you’re looking to do something with PowerShell on a regular basis and need to interact with Azure.

Read the whole thing.

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Tabular Image For Azure VMs

Mark Vaillancourt has a new Connect item:

Currently, when utilizing the SQL Server images in the VM Gallery in Azure, any installations of SQL Server Analysis Services default to Multidimensional. Thus, if you want SSAS Tabular, you have additional work to perform.

I was just chatting with a Senior Program Manager on the SQL Server Analysis Services product team. They currently don’t have anything in their plans for providing SQL Server Gallery Images with SSAS Tabular instead of Multidimensional. We agreed that it is a good idea for that to happen. We also agreed that a Connect suggestion would be a great way to gauge broader community support/appetite for providing Gallery images with Tabular installed.

Here’s the Connect item.

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Diagnosing Virtual Machine Cloning Issues

Jack Li walks through a few common problems when creating Azure VMs based off of captured images:

When you create VM from a captured image, the drive letters for data disks may not preserved.  For example if you have system database files on E: drive, it may get swapped to H: drive.  If this is the case, SQL Server can’t find system database files and will not start.  If the driver letter mismatch occurs on user database files, then the user databases will not recover.   After VM is created, you just need to go to disk management to change the drive letters to match your original configuration.

Read the whole thing if you’re thinking about copying your on-premise server to an Azure VM.

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Stretch Databases

Tim Radney looks at Stretch Database functionality in RTM:

Prior to learning about this new billing method for DSU, I could make the argument that using Stretch Database would be a very cost effective method for storing cold data (unused data) into the cloud. By stretching this data into Azure, you could migrate a large portion of older data, which would decrease the size (and thus cost) of your local backups. In the event you had to restore a database, you would simply have to establish the connection to Azure for the stretched data, thus eliminating the need to restore it. However, with the minimal cost being nearly $1,000 per month for the low end DSU scale, many organizations will find that it is much cheaper to retain the data on a less expensive tier of storage within their data center and find other methods for HA such as mirroring, log shipping, or Availability Groups.

Read the whole thing.  Maybe V2 of stretch databases will fix some of the biggest problems (the cost, needing to pull all of your data back down before you make any schema changes, etc.) and become a viable feature, but I can’t see it being one today.

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HDInsight Tool For Eclipse

Xiaoyong Zhu reports that the HDInight tool for Eclipse is now generally available:

The HDInsight Tool for Eclipse extends Eclipse to allow you to create and develop HDInsight Spark applications and easily submit Spark jobs to Microsoft Azure HDInsight Spark clusters using the Eclipse development environment.  It integrates seamlessly with Azure, enabling you to easily navigate HDInsight Spark clusters and to view associated Azure storage accounts. To further boost productivity, the HDInsight tool for Eclipse also offers the capability to view Spark job history and display detailed job logs.

Check out the link for videos and additional resources.

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Running Compiled Code In Azure ML

Max Kaznady shows how to use R or Python scripts to call compiled code within Azure ML:

In this post, we focus on sourcing R and Python’s external dependencies, such as R libraries and Python modules, which are not already installed on Azure ML and require code compilation. Commonly the compiled code comes from a variety of other languages such as C, C++ and Fortran. One could also use this approach to wrap their compiled code with R or Python wrappers and run it on Azure ML.

To illustrate the process, we will build two MurmurHash modules from C++ for R and Python using the following two implementations on GitHub, and link them to Azure ML from a zipped folder

Link via David Smith.  I knew it was possible to call compiled C code from Python and R, but didn’t expect to be able to do it within Azure ML, so that’s good to know.

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Using Azure Data Catalog

Melissa Coates has some good advice if you start using Azure Data Catalog:

Register only data sources that users interact with. Usually the first priority is to register data sources that the users see-for instance, the reporting database or DW that you want users to go to rather than the original source data. Depending on how you want to use the data catalog, you might also want to register the original source. In that case you probably want to hide it from business users so it’s not confusing. Which leads me to the next tip…

Use security capabilities to hide unnecessary sources. The Standard (paid) version will allow you to have some sources registered but only discoverable by certain users & hidden from other users (i.e., asset level authorization). This is great for sensitive data like HR. It’s also useful for situations when, say, IT wants to document certain data sources that business users don’t access directly.

This is a good set of advice.

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