So how do you restore from Azure storage? You do so from an URL. Let’s take a look!
When you backup a database to Azure, there are two types of blobs that can be utilized, namely page and block blobs. Due to price and flexibly, it is recommended to use block blobs. However, depending on which type you used to perform the backup will dictate how the restores are performed. Both methods require the use a credential, so that information will need to be known before being able to restore from Azure.
Click through for examples using both page blobs and block blobs.
Azure Data Factory (ADF) offers a convenient cloud-based platform for orchestrating data from and to on-premise, on-cloud, and hybrid sources and destinations. But it is not a full Extract, Transform, and Load (ETL) tool. For those who are well-versed with SQL Server Integration Services (SSIS), ADF would be the Control Flow portion.
You can scale out your SSIS implementation in Azure. In fact, there are two (2) options to do this: SSIS On-Premise using the SSIS runtime hosted by SQL Server or On Azure using the Azure-SSIS Integration Runtime.
Azure Data Factory is not quite an ETL tool as SSIS is. There is that transformation gap that needs to be filled for ADF to become a true On-Cloud ETL Tool. The second iteration of ADF in V2 is closing the transformation gap with the introduction of Data Flow.
Despite it not being nearly as complete as SSIS, there are useful data transformations available in Azure Data Factory, as Marlon shows.
One of the best things about Azure, and the cloud in general, is we can automate most anything, and we are going to look at how to automate Azure VM Storage. This allows us to come up with some outside-of-the-box solutions. I had a customer with a road block that we were able to work around by automating some things with their Azure Virtual Machines.
Their challenge was that they wanted to move their test and development environments to Azure, but the storage cost was prohibitive. They needed premium storage to mimic their production environment, but it was not financially viable for test and development so they were going to keep it all on premises. During our conversations I learned that they only test between 8am and 5pm, Monday through Friday. My suggestion was that we put their databases on cheaper storage during off times and only premium when they are actively using it.
This doesn’t look like a one-hour task but if you’re in need of some cost savings on storage in non-production environments, check out Ryan’s scripts.
The Automatic Scaling feature in Amazon EMR lets customers dynamically scale clusters in and out, based on cluster usage or other job-related metrics. These features help you use resources efficiently, but they can also cause EC2 instances to shut down in the middle of a running job. This could result in the loss of computation and data, which can affect the stability of the job or result in duplicate work through recomputing.
To gracefully shut down nodes without affecting running jobs, Amazon EMR uses Apache Hadoop‘s decommissioning mechanism, which the Amazon EMR team developed and contributed back to the community. This works well for most Hadoop workloads, but not so much for Apache Spark. Spark currently faces various shortcomings while dealing with node loss. This can cause jobs to get stuck trying to recover and recompute lost tasks and data, and in some cases eventually crashing the job.
Auto-scaling doesn’t always mean scaling up.
A couple of people have asked me recently about how to ‘bone up’ on the new data lake service in Azure. The way I see it, there are two aspects: A, the technology itself and B, data lake principles and architectural best practices. Below are some links to resources that you should find helpful.
There’s a lot of good stuff there.
You have many options to access to CosmosDB. Rest API is one of these options and it is the low level access way to Cosmos DB. You can customize all options of CosmosDB by using REST API. To customize the calls, and pass the required authorization information, you need to use http headers. There are many headers you can set depending on the operation you want to run in CosmosDB. I am going to cover only the required headers here.
In the following example, I am going to try to create a database in CosmosDB emulator by using the REST API. First let’s look at the required header fields for this request. These requirement applies to all other REST API calls too.
It’s a little more complicated than just posting to a URL and Hasan has you covered.
When to use Blob vs ADLS Gen2
New analytics projects should use ADLS Gen2, and current Blob storage should be converted to ADLS Gen2, unless these are non-analytical use cases that only need object storage rather than hierarchical storage (i.e. video, images, backup files), in which case you can use Blob Storage and save a bit of money on transaction costs (storage costs will be the same between Blob and ADLS Gen2 but transaction costs will be a bit higher for ADLS Gen2 due to the overhead of namespaces).
Looks like there are still some things missing from Gen2, so don’t automatically jump on an upgrade. Read the documentation first to make sure you aren’t relying on something which isn’t there yet.
Today, we’re going to talk about combining Stream Analytics with Azure Machine Learning Studio within Power BI. If you haven’t read the earlier posts in this series, Introduction, Getting Started with R Scripts, Clustering, Time Series Decomposition, Forecasting, Correlations, Custom R Visuals, R Scripts in Query Editor, Python, Azure Machine Learning Studio and Stream Analytics, they may provide some useful context. You can find the files from this post in our GitHub Repository. Let’s move on to the core of this post, Stream Analytics.
This post is going to build directly on what we created in the two previous posts, Azure Machine Learning Studio and Stream Analytics. As such, we recommend that you read them before proceeding.
Read on for the demo.
Azure Data Factory (ADF) provides you with a framework for creating data transformation solutions in the Microsoft cloud environment. Recently introduced Template Gallery for ADF pipelines can speed up this development process and provide you with helpful information to create additional activity tasks in your pipelines.
We naturally long to seek if something standard can be further adjusted. That custom design is like ordering a regular pizza and then hitting the “customize” button in order to add a few toppings of our choice. It would be very impressive then to save this customized “creation” for future ordering. And Azure Data Factory has a similar option to save your custom data transformation solutions (pipelines) as templates and reuse them later.
Click through to see how you can do just that.
What does this mean? To have a supported WSFC-based configuration (doesn’t matter what you are running on it – could be something non-SQL Server), you need to pass validation. xFailOverCluster does not allow this to be run. You can create the WSFC, you just can’t validate it. The point from a support view is that the WSFC has to be vetted before you create it. Could you run it after? Sure, but you still have no proof you had a valid configuration to start with which is what matters. This is a crucial step for all AGs and FCIs, especially since AGs do not check this whereas the installation process for FCIs does.
If you look at MSFT_xCluster, you’ll see what I am saying is true. It builds the WSFC without a whiff of Test-Cluster. To be fair, this can be done in non-Azure environments, too, but Microsoft givs you warnings not to do that for good reason. I understand why Microsoft did it this way. There is currently no tool, parser, or cmdlet to examine the output of Test-Cluster results. This goes back to why building WSFCs is *very* hard to automate.
I’m not sure how easy some of these fixes would be, but they’d definitely be nice.