Honored to Present Moving Data with ADF at SQL Saturday Raleigh!

I am honored to present Moving Data with Azure Data Factory at SQL Saturday 863 – Raleigh 27 Apr!

Abstract

Azure Data Factory – ADF – is a cloud data engineering solution. ADF version 2 sports a snappy web GUI (graphical user interface) and supports the SSIS Integration Runtime (IR) – or “SSIS in the Cloud.”

Attend this session to learn:

  • How to build an ADF pipeline;
  • How to lift and shift SSIS to the Azure Data Factory integration Runtime; and
  • ADF Design Patterns to execute and monitor pipelines and packages.

Register today!
I hope to see you there.

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Honored to Present Moving Data with Azure Data Factory at SQL Saturday Atlanta 18 May!

I am honored to present Moving Data with Azure Data Factory at SQL Saturday 845 – Atlanta 18 May 2019!

Abstract

Azure Data Factory – ADF – is a cloud data engineering solution. ADF version 2 sports a snappy web GUI (graphical user interface) and supports the SSIS Integration Runtime (IR) – or “SSIS in the Cloud.”

Attend this session to learn:

  • How to build a “native ADF” pipeline;
  • How to lift and shift SSIS to the Azure Data Factory integration Runtime; and
  • ADF Design Patterns to execute and monitor pipelines and packages.

Register today. SQL Saturday – Atlanta is free but there are a limited number of seats available. The event historically sells out sooner rather than later and begins placing folks on the Wait List.

I hope to see you there!

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Lift and Shift SSIS to Azure

Enterprise Data & Analytics‘ data engineers are experts at lifting and shifting SSIS to Azure Data Factory SSIS Integration Runtime. 

Our state-of-the-art DILM Suite tools in the capable hands of our experienced data engineers combine to drastically reduce the amount of time to manually migrate and apply SSIS Catalog configuration artifacts – Literals, Catalog Environments and Catalog Environment Variables, References, and Reference Mappings – while simultaneously improving the quality of the migration effort.

Check out our Lift and Shift page to learn more!

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Updates to the ADF Execute SSIS Package Activity

Last night I presented Moving Data with Azure Data Factory to a packed house at the Richmond Azure User Group. The crowd was engaged and asked awesome questions. I had a blast!

Surprise!

I rehearsed many of my demos before the presentation and even updated my slides. One thing I did not do was rehearse configuring an Execute SSIS Package activity. Why? I’ve built these demos live a bajillion times. I know what I’m doing…

When I clicked on the Settings tab I found new stuff – options I’d not previously encountered. Thankfully, I immediately recognized the purpose of these new configuration options – and I also immediately liked them. The demo worked and no one was the wiser (except the handful of folks with whom I shared this story after the meeting).

New Dropdowns for Folder, Project and Package

The new interface sports new dropdowns for selecting the Catalog Folder, Project, and Package to executed. I like this – it’s slick. I had deployed a project to my Azure-SSIS instance between the time I started this part of the demo and the time I wanted to configure the Execute SSIS Package activity. During deployment I created a new Catalog Folder which was not initially listed in the Folder dropdown. Clicking the Refresh button remedied this, though, and I was able to complete configuration rapidly. 

Configuration Tabs

I cannot recall if the Connection Managers and Property Overrides tabs were part of the previous Execute SSIS Package activity interface. I don’t think so, but I could be wrong about that. Update: I verified these are new tabs by looking at screenshots from my June 2018 post titled ADF Execute SSIS Package Activity. The previous version had an Advanced tab. 

The SSIS package configuration tabs are SSIS Parameters, Connection Managers, and Property Overrides.

When your Azure-SSIS instance is running, you may use these tabs to update  Parameter, Connection Manager Property, and SSIS Package Property values:

Warnings

It’s possible to configure the Execute SSIS Package activity when your Azure-SSIS Integration Runtime is not running, but you don’t get the nice dropdown pre-population and have to revert to the previous method of configuring the full SSIS Catalog path to the package you desire to execute.

SSIS Catalog Browser To The Rescue!

If you find yourself in this predicament and would rather configure the Execute SSIS Package activity without waiting 20-30 minutes for the Azure-SSIS instance to spin up, you can use SSIS Catalog Browser – a free utility from DILM Suite – to connect to your Azure-SSIS instance:

SSIS Catalog Browser displays the Catalog path for an SSIS package (or Catalog Environment) when you select the artifact in the unified Catalog surface. Copy the package’s Catalog path displayed in the Status area and and paste the value into the Package Path textbox in ADF:

Make sure the Manual Entries checkbox is checked.

I like the warnings. Feedback is a good thing.

Once Azure-SSIS Is Running

When your Azure-SSIS instance is up and running, you may configure the path to your SSIS package using the dropdowns:

You can even configure the path to a Catalog Environment:

Conclusion

I believe there are at least two lessons to take away from my experience:

  1. When presenting on Microsoft Azure topics, always check your demos to make certain nothing has changed; and 
  2. Microsoft Azure is evolving at a rapid rate – especially Azure Data Factory!

Lift and Shift is Not a Strategy…

Lift and shift is a bridge.

If your enterprise is considering migrating enterprise data and data integration workloads from on-premises to the cloud, lifting and shifting is not the end of the story. Lifting and shifting is, rather, the mechanism – the conversion project – that positions your enterprise to leverage the economies of scale afforded by cloud technology.

Andy’s Lift and Shift FAQ

“Should We Lift and Shift?”

I hear this question often and my response is, “When it makes sense to do so, yes, you should lift and shift.” This begs the next question, which is…

“How Do We Know It’s Time to Lift and Shift?”

My engineer-y response to this question is, “You will know it’s time to lift and shift to the cloud when you want to leverage functionality available in the cloud that is not (or not yet) available in on-premises versions of the enterprise platform(s) in use in your enterprise.”

“What Are Some Advantages of Migrating to the Cloud?”

The biggest advantage of lifting and shifting enterprise data to the cloud is the ability to efficiently scale operations. By efficiently, I mean quickly and easily – especially when compared to the time and expense (don’t forget opportunity cost when calculating expense) to scale up systems on-premises.

The ability to scale up and scale down on demand is a huge advantage for some business models which experience “spike-y” demand for operations at different times of the year, quarter, or month. But even if that’s not the case, all data scales. It’s very handy to be able to connect to the Azure Portal and move a slider (as opposed to purchasing and provisioning more hardware…).

There’s a brand new (in my opinion) “knob” exposed by cloud-enabled efficient scaling. As I wrote in my post titled Time and Money in the Cloud:

Let’s say you pay $100 to incrementally load your data warehouse and the load takes 24 hours to execute at the scale you’ve selected in the cloud. Prior to thinking in DTUs, engineers and business people would think, “That’s just the way it is. If I want more or faster, I need to pay for more or faster.” But DTU math doesn’t quite work that way. Depending on your workload and DTU pricing at the time (FULL DISCLOSURE: DTU PRICING CHANGES REGULARLY!), you may be able to spend that same $100 on more compute capabilities and reduce the amount of time required to load the same data into the same data warehouse to minutes instead of hours…

The fact that the cost/performance curve can be altered in seconds instead of months meta-changes everything.

“Are There Disadvantages of Migrating to the Cloud?”

It depends. (You knew that was coming, didn’t you?)

Enterprise Data & Analytics helps enterprises migrate data, data integration, lifecycle management, and DevOps to the cloud. In some cases (~30%), the enterprises spend a little more money in the near-term. There are two reasons for this:

  1. When upgrading, it’s always a good idea to operate new systems in tandem with existing systems. In a lift and shift scenario, this means additional expenses for cloud operations while maintaining the expense of on-premises operations. As cloud operations are validated, on-premises operations are shut off; thereby reducing operating expenses. In truth, though, this dynamic (and expense) exists whether one is lifting and shifting to the cloud or simply upgrading system on-premises.
  2. “Standing on the bridge” (more in a bit) can cost more than remaining either on-premises or lifting and shifting the entire enterprise workload to the cloud.
  3. Regulatory requirements – including privacy and regulations about which data is allowed to leave nation-states – will constrain many industries, especially government agencies and NGOs (non-governmental organizations) who interact heavily with government agencies.

Standing On The Bridge

One option we at Enterprise Data & Analytics consider when assisting enterprises in lift and shift engagements is something we call “standing on the bridge.” 

Standing on the bridge is present in each lift and shift project. It’s one strategy for implementing hybrid data management, which almost every enterprise in the cloud today has implemented. Hybrid means part of the enterprise data remains on-premises and part of the enterprise data is lifted and shifted to the cloud. 

Hybrid is implemented for a variety of reasons which include:

  • Mitigating regulatory concerns; and
  • As part of the normal progression of lifting and shifting enterprise data and data workloads to the cloud.

Standing on the bridge for too long is a situation to avoid. 

“How Do We Avoid Standing on the Bridge For Too Long?”

Planning. Planning is how an enterprise avoids standing on the bridge too long. Your enterprise wants advice from experienced professionals to shepherd the lift and shift operation. 

Enterprise Data & Analytics can help.

Helpful Tools

Enterprise Data & Analytics has been delivering, and writing and speaking about Data Integration Lifecycle Management for years. 

We’ve built helpful tools and utilities that are available at the DILM Suite. Most of the DILM Suite tools are free and some are even open source. 

Want to Learn More About Azure Data Factory?

From me?

I am honored to announce Fundamentals of Azure Data Factory – a course from Enterprise Data & Analytics!

The next delivery is 04 Mar 2019, 9:00 AM – 4:30 PM ET.

Azure Data Factory, or ADF, is an Azure PaaS (Platform-as-a-Service) that provides hybrid data integration at global scale. Use ADF to build fully managed ETL in the cloud – including SSIS. Join Andy Leonard – authorblogger, and Chief Data Engineer at Enterprise Data & Analytics – as he demonstrates practical Azure Data Factory use cases.

In this course, you’ll learn:

  • The essentials of ADF
  • Developing, testing, scheduling, monitoring, and managing ADF pipelines
  • Lifting and shifting SSIS to ADF SSIS Integration Runtime (Azure-SSIS)
  • ADF design patterns
  • Data Integration Lifecycle Management (DILM) for the cloud and hybrid data integration scenarios

I hope to see you there!

PS – Join me For Expert SSIS Training!

Follow Andy Leonard’s SSIS Training page for more information.