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Category: Cloud

Integrating Azure Data Factory With GitHub

Published 2019-02-07 by Kevin Feasel

Rayis Imayev shows us how to tie Azure Data Factory pipelines with GitHub, allowing automatic check-in based on ADF pipeline changes:

Working with Azure Data Factory (ADF) enables me to build and monitor my Extract Transform Load (ETL) workflows in Azure. My ADF pipelines is a cloud version of previously used ETL projects in SQL Server SSIS.

And prior to this point, all my sample ADF pipelines were developed in so-called “Live Data Factory Mode” using my personal workspace, i.e. all changes had to be published in order to be saved. This hasn’t been the best practice from my side, and I needed to start using a source control tool to preserve and version my development code.

Click through for a detailed demo.

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Automated Query Capture With Logic Apps

Published 2019-02-06 by Kevin Feasel

Arun Sirpal shows how we can use Azure Logic Apps to automate periodic capture of running queries in Azure SQL Database:

Have you ever wanted to capture the T-SQL, waits, sessions IDs (etc) at a specific time for Azure SQL Database? Sure there are a few ways to do this. Extended Events comes to mind but I wanted to do something different.

For this blog post I decided to use Brent Ozar’s famous sp_BlitzWho command (in expert mode) coupled with Azure Logic Apps. At a high level it is simple. At a specific time trigger the execution of sp_BlitzWho stored procedure and query it for later use.

Click through to see how to set this up.

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Query Store On Azure SQL DW

Published 2019-02-04 by Kevin Feasel

Matt Usher announces Query Store is now available to all in Azure SQL Data Warehouse:

Since our preview announcement, hundreds of customers have been enabling Query Store to provide insight on query performance. We’re excited to share the general availability of Query Store worldwide for Azure SQL Data Warehouse.

Query Store automatically captures a history of queries, plans, and runtime statistics and retains them for your review when monitoring your data warehouse. Query Store separates data by time windows so you can see database usage patterns and understand when plan changes happen.

Given its power in the on-prem product, I’m glad that Azure SQL Data Warehouse is getting Query Store as well.

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Where To Store SQL Server DB Files In Azure

Published 2019-02-04 by Kevin Feasel

James Serra gives us a few options for storing database files (that is, your MDF, NDF, and LDF files) when running SQL Server in an Azure VM:

If using managed disks, the recommendation is to attach several premium SSDs to a VM.  From Sizes for Windows virtual machines in Azure you can see for the size DS5_v2 you can add 64 of 4TB data disks (P50) yielding the potential total volume size of up to 256TB per VM, and if you use the preview sizes (P80) your application can have up to around 2PB of storage per VM (64 of 32TB disks).  With premium storage disks, your applications can achieve 80,000 I/O operations per second (IOPS) per VM, and a disk throughput of up to 2,000 megabytes per second (MB/s) per VM.  Note the more disks you add the more storage you get and the more IOPS you get, but only up to a certain point due to VM limits, meaning at some point more disks get you more storage but not more IOPS.

But there are a few other options too, so check them out.

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Apps To Manage SQL Server On Azure VMs

Published 2019-02-04 by Kevin Feasel

Kevin Chant has a list of tools you can use to manage SQL Server on Azure VMs:

From experience I know it’s important to know what applications you can use locally with Azure to manage SQL Server solutions. So you have the right tools for the job.

For instance, I was talking with some people at a client’s site the other day about deciding what application to use to future proof themselves.

In this post I will cover applications for use with Windows, MacOS and Linux distributions. 

I don’t think I’m spoiling too much in saying that about 80% of these are the same tools you would use for on-prem work.

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Using AWS Lambda To Get Into Nice Restaurants

Published 2019-02-01 by Kevin Feasel

Stephane Maarek gives us the best use of AWS Lambda I’ve seen yet:

One attentive eye would have noticed that the booking platform is not hosted on the restaurant website at http://www.septime-charonne.fr/en/ but instead on https://module.lafourchette.com.

Upon using the Chrome Web Developer Tools to analyze the network calls being made between my browser and the booking service, I stumbled upon an easy to use and completely unprotected REST API:

I love the bonus hack at the end.

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Orphaned Workspaces In Power BI

Published 2019-02-01 by Kevin Feasel

David Eldersveld explains what orphaned workspaces are in Power BI:

One of the newer features in the Power BI Admin Portal is the ability to view all of a tenant’s Workspaces. As I was browsing through the collection of workspaces, I noticed several marked as Orphaned. What is an orphaned workspace, and how does it occur?

I was expecting orphaned workspaces to be a new thing where you pay for an Azure service using a distributed blockchain technology called Gruel (or maybe Grool).

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Azure Data Factory Data Flows

Published 2019-01-31 by Kevin Feasel

Joost van Rossum takes a look at data flows in Azure Data Factory:

2) Create Databricks Service
Yes you are reading this correctly. Under the hood Data Factory is using Databricks to execute the Data flows, but don’t worry you don’t have to write code.
Create a Databricks Service and choose the right region. This should be the same as your storage region to prevent high data movement costs. As Pricing Tier you can use Standard for this introduction. Creating the service it self doesn’t cost anything.

Joost shows the work you have to do to build out a data flow. This has been a big hole in ADF—yeah, ADF seems more like an ELT tool than an ETL tool but even within that space, there are times when you need to do a bit more than pump-and-dump.

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Changing Red Hat’s SSH Port On An Azure VM

Published 2019-01-31 by Kevin Feasel

Paul Randal has a post showing you how to change the default SSH port on a Red Hat Enterprise Linux VM hosted in Azure:

The steps that need to be performed are:
– Allow the new port in the RHEL firewall
– Change the SSH daemon to listen on the new port
– Add an incoming rule in the VM network security group for the new port
– Remove the rule that allows port 22

The Ubuntu process will be pretty close to this as well.

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Stream Analytics And Power BI

Published 2019-01-31 by Kevin Feasel

Brad Llewellyn gives us a demo on connecting a Stream Analytics stream to Power BI for data analysis:

We understand that streaming data isn’t typically considered “Data Science” by itself.  However, it’s are often associated and setting up this background now opens up some cool applications in later posts.  For this post, we’ll cover how to sink streaming data to Power BI using Stream Analytics.

The previous posts in this series used Power BI Desktop for all of the showcases.  This post will be slightly different in that we will leverage the Power BI Service instead.  The Power BI Service is a collaborative web interface that has most of the same reporting capabilities as Power BI Desktop, but lacks the ability to model data at the time of writing.  However, we have heard whispers that data modeling capabilities may be coming to the service at some point.  The Power BI Service is also the standard method for sharing datasets, reports and dashboards across organizations.  For more information on the Power BI Service, read this.

Brad has a nice demo, so check it out.

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