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Category: Cloud

Azure SQL Database Business Continuity Options

Published 2020-07-21 by Kevin Feasel

James Serra covers business continuity scenarios with Azure SQL Database:

I have wrote a number of blogs on the topic of business continuity in SQL Database before (HA/DR for Azure SQL Database, Azure SQL Database high availability, Azure SQL Database disaster recovery) but with a number of new features I felt it was time for a new blog on the subject, focusing on disaster recovery and not high availability.

Business continuity in Azure SQL Database and SQL Managed Instance refers to the mechanisms, policies, and procedures that enable your business to continue operating in the face of disruption, particularly to its computing infrastructure. In the most of the cases, SQL Database and SQL Managed Instance will handle the disruptive events that might happen in the cloud environment and keep your applications and business processes running.

James takes us through options available for Azure SQL Database as well as managed instances.

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Azure Data Factory and Key Vault References

Published 2020-07-20 by Kevin Feasel

Gerhard Brueckl shows how we can get around a limitation in the Azure Data Factory user interface:

As You can see, the setting “AccessToken” can use a Key Vault reference whereas settings like “Databricks Workspace URL” and “Cluster” do not support them. This is usually fine because the guys at Microsoft also thought about this and support Key Vault references for the settings that are actually security relevant or sensitive. Also, providing the option to use Key Vault references everywhere would flood the GUI. So this is just fine.

But there can be good reasons where you want to get values from the Key Vault also for non-sensitive settings, especially when it comes to CI/CD and multiple environments. From my experience, when you implement a bigger ADF project, you will probably have a Key Vault for your sensitive settings and all other values are provided during the deployment via ARM parameters.

So you will end up with a mix of Key Vault references and ARM template parameters which very likely will be derived from the Key Vault at some point anyway. To solve this, you can modify the JSON of an ADF linked service directly and inject KeyVault references into almost every property!

Click through to see how that works, as well as the ramifications.

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Comparing Integration Services and Azure Data Factory

Published 2020-07-17 by Kevin Feasel

Tim Mitchell compares SQL Server Integration Services to Azure Data Factory:

For the better part of 15 years, SQL Server Integration Services (SSIS) has been the go-to enterprise extract-transform-load (ETL) tool for shops running on Microsoft SQL Server. More recently, Microsoft added Azure Data Factory (ADF) to its stable of enterprise ETL tools. In this post, I’ll be comparing SSIS and Azure Data Factory to share how they are alike and how they differ. I’ll also review the strengths and shortcomings of each, including the architectures in which each of these is likely to do well.

Read on for Tim’s thoughts on the subject. Tim lays out his biases up-front but also gives you a good feel for where both products are in their lifecycles.

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Case-Insensitive Searches in Cosmos DB

Published 2020-07-17 by Kevin Feasel

Hasan Savran gives us a few techniques for case-insensitive searching in Azure Cosmos DB:

Data gets saved as JSON in Azure Cosmos DB. JSON documents supports string, numeric and Boolean data types. Because of limited data types, you might end up picking string data type for most of your data. You may need to use Cosmos DB’s string functions to help you in many situations. For example; you might need to find names start with bunch of characters or you might need to find data ends like a word or bunch of characters. You may need to find all data that has address contains a street name. What do you do in these cases? Azure Cosmos DB has couple of system functions to help you, I will focus on the following functions in this post. These are the functions just got updated in June 2020.

Click through to learn a bit about those money-saving updates.

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Getting Started with Redshift

Published 2020-07-17 by Kevin Feasel

Rahul Mehta has a primer on AWS Redshift:

AWS Redshift is a columnar data warehouse service on AWS cloud that can scale to petabytes of storage, and the infrastructure for hosting this warehouse is fully managed by AWS cloud. Redshift operates in a clustered model with a leader node, and multiple worked nodes, like any other clustered or distributed database models in general. It is based on Postgres, so it shares a lot of similarities with Postgres, including the query language, which is near identical to Structured Query Language (SQL). This Redshift supports creating almost all the major database objects like Databases, Tables, Views, and even Stored Procedures. In this article, we will explore how to create your first Redshift cluster on AWS and start operating it.

I’m not really the biggest fan of Redshift around, but Rahul does a good job walking us through the basics of the product.

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Incremental Pipline Development with Azure Data Factory

Published 2020-07-17 by Kevin Feasel

Andy Leonard shows how you can incrementally develop Azure Data Factory pipelines:

A friend pinged me recently to ask about rolling back Azure Data Factory (ADF) pipeline versions. My response was a question: Are you using source control with ADF? That did not help the current situation.

I thought of the way I often build ADF pipelines and shared my methodology, which is relatively simple (it has to be simple for me to understand it!):

Click through for Andy’s approach.

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Methods to Run Scheduled Tasks in Azure

Published 2020-07-15 by Kevin Feasel

Joey D’Antoni has a roundup of several techniques you can use to run scheduled tasks against an Azure SQL Database:

If you’ve worked with Microsoft SQL Server for any period of time, you are familiar with the SQL Server Agent. The Agent, which remains mostly unchanged since I started working with in 1999, is a fairly robust job scheduler that can also alert you in the event of job failures or system errors. I feel as though it’s a testament to the quality of the original architecture that the code hasn’t changed very much–it still meets the needs of about 90-95% of SQL Server workloads, based on an informal twitter discussion I had a few months ago. There are some cases where an enterprise scheduling tool is needed, but for maintaining most SQL Servers and executing basic ETL, the agent works fine. There’s one problem–the agent is only available in SQL Server and Azure SQL Managed Instance.

Read on to learn about those options.

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Azure Elastic Jobs Now GA

Published 2020-07-14 by Kevin Feasel

Niko Neugebauer gives into Azure Elastic Jobs, now generally available:

They key feature that needs to be well understood and which points to the potential of the Elastic Job Agent is that you are in no way limited by your own Azure SQL Database, nor by the logical Azure SQL Server where this database is located (contrary to the MSDB Database on the SQL Server), nor will you be limted by the Azure Region, Azure Resource Group or even Azure Subscription – you can configure the Elastic Job that will be reaching out to potentially any Azure SQL Database (given the necessary settings & permissions are correctly configured).

Read the whole thing. Niko shares some interesting thoughts on how it works, how you can tie your one server to a whole host of SQL Databases, and a wish list on what should come next.

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Refreshing Power BI Datasets in Azure Data Factory

Published 2020-07-13 by Kevin Feasel

Meagan Longoria shows us how to refresh a Power BI dataset using Azure Data Factory:

I recently needed to ensure that a Power BI imported dataset would be refreshed after populating data in my data mart. I was already using Azure Data Factory to populate the data mart, so the most efficient thing to do was to call a pipeline at the end of my data load process to refresh the Power BI dataset.

Power BI offers REST APIs to programmatically refresh your data. For Data Factory to use them, you need to register an app (service principal) in AAD and give it the appropriate permissions in Power BI and to an Azure key vault.

Click through for the solution.

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