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Category: Cloud

Deploying a Container Instance in Azure

Published 2019-10-24 by Kevin Feasel

Anibal Kolker takes us through container deployment in Azure:

As derived from the title, the objective of this post is to help you deploy a container instance inside Azure.

However, we’ll extend the typical scenario and make a slightly more extensive use of networking capabilities, by placing the container group inside a private subnet.

Note: For this example, and for simplicity only, we’ll use NGINX as our container of choice. Of course, you’re welcome to try with any other image.

There are a few pieces in play, but Anibal does a good job putting it all together.

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Azure DevOps and Data Factory

Published 2019-10-23 by Kevin Feasel

Helge Rege Gardsvoll has a three-part series for us on using Azure DevOps to deploy Data Factories. Part 1 is all about environment setup:

Shared Data Factory
The shared Data Factory is there for one use; self-hosted integration runtimes. This is the component you will use to connect to on-premise or other sources that have restrictions on access such as IP restriction or other firewall rules. Migrating a Self Hosted Integration Runtime is not supported, but you can share the same Integration Runtime across different Data Factories. You can find a description for how to do this in this article.

Part 2 covers Git branching, linked services, and development:

Create datasets and pipeline
For this demo I create two datasets; one for source and one for target, and a simple pipeline that copies the data. Datasets have name that point to the data lake, like ADLS_datahelgeadls2_Brreg_MainUnits, but does not include environment information.

Part 3 covers the release process:

The release process will have these steps;
1. Stop any active triggers. We do not want any pipelines to start as we are changing things (and you should wait until running pipelines finish before publishing)
2. Release from development to target environment
3. Clean up target environment by removing objects that are not present in dev. Also start triggers

This is a great series of posts and also includes a bonus tidbit if you’re using Databricks.

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PowerApps Security

Published 2019-10-22 by Kevin Feasel

Jason Bonello gives us some tips on PowerApps security:

Depending on how the backend is set up, the tables having these sensitive data might be in the same database. For example, ERP solutions can have Company Accounts data, Customer related data and Inventory related data all in the same database, maybe under different schemas – but still part of the same database.

Now let’s say we are about to create a PowerApps solution to maintain Customer information. However, as part of the organization policy, this information should not be shared across other departments apart from the intended users.

Read on for some ideas of how to limit the risk of data exposure.

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Azure Data Factory Switch Activity

Published 2019-10-22 by Kevin Feasel

Rayis Imayev explains what the Switch activity does in Azure Data Factory:

Developing conditional logic of your Azure Data Factory control flow has been simplified with introducing of the Switch activity – https://docs.microsoft.com/en-us/azure/data-factory/control-flow-switch-activity. Official documentation resource states, this new data factory activity “provides the same functionality that a switch statement provides in programming languages“. I would also add a more simplified definition of the Switch activity in Azure Data Factory: it is a container (or wrapper) for multiple IF conditions.

Click through for an example.

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Running Big Data Clusters on VS Subscriptions

Published 2019-10-21 by Kevin Feasel

Kevin Chant has a few tips for people wanting to try out Big Data Clusters with their Visual Studio subscriptions to Azure:

In order to present the right results for various outcomes I attempted to deploy Big Data Clusters multiple times.

When I say multiple times, I mean the number of deployments easily went into double figures. Because I was testing deploying various virtual machine sizes in multiple regions.

Hence, I spent many hours testing and verifying the results in order to present them properly.

Read on to see Kevin’s notes and recommendations.

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Backing Up SQL Server on Azure VMs

Published 2019-10-21 by Kevin Feasel

Arun Sirpal looks at three techniques for backing up SQL Server running on Azure virtual machines:

In the previous blog post I did a quick overview building a SQL VM (imaged) in Azure. It is now time to clarify some backup techniques because it can get confusing.

At a high level there are 3 techniques.
– Automated backup.
– Azure backup for SQL VM (that’s what MS call it).
– Manual backup, for example backup to URL.

Read on to learn more about each.

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Running Oracle on Azure

Published 2019-10-16 by Kevin Feasel

Kellyn Pot’vin-Gorman takes us through various options on running Oracle in Azure:

Running Oracle on Azure VM environments aren’t that different from running Oracle on VMs in your on-premises for a DBA.  The DBAs and developers that I work with still have their jobs, still work with their favorite tools and also get the chance to learn new skills such as cloud administration.

Click through for more, including a setup script.

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Comparing Azure SQL DB and RDS on Price and Performance

Published 2019-10-16 by Kevin Feasel

Joey D’Antoni summarizes a recent publication:

Yesterday, GigaOm published a benchmark of Azure SQL Database as compared to Amazon’s RDS service. It’s an interesting test case that tries to compare the performance of these platform as a service database offerings. One of the many challenges of this kind of a study is that the product offerings are not exactly analogous. However, GigaOm was able to build a test case where they used similar sized offerings as shown in the image below.

Read on for the summary and Joey’s thoughts.

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Limiting Index Sizes in Cosmos DB

Published 2019-10-11 by Kevin Feasel

Hasan Savran explains why you might want to exclude columns from Cosmos DB indexes:

If everything is indexed already; Why do we want to exclude some of indexes? Indexes are saved on disk, you pay for the storage in Azure. If you keep indexing everything, your index file gets larger and you pay more for storage.

     Also; write operations to index file takes longer if index file is larger. By keeping only what you need in index file will improve the latency of write operations. If you will need to change your indexing policies, Rebuilding indexes will take less time.

This behavior is quite different from the way SQL Server behaves, where indexing is more of an opt-in philosophy.

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Using Azure Kubernetes Services for Big Data Clusters

Published 2019-10-09 by Kevin Feasel

Mohammad Darab explains why it’s a good idea to use Azure Kubernetes Service when building out a Big Data Cluster:

According to the Microsoft documentation, there are three ways to deploy a Big Data Cluster:

1. Minikube
2. Kubeadm
3. AKS

I’ll go into each and list the pros and cons.

Of course, if you have a great Kubernetes admin, on-prem is certainly a viable option, but AKS is definitely easier to get started with.

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