The ability to connect data, processing, and services is the foundation of the modern financial services enterprise. You want to do those things in the most efficient, transparent, scalable, and cost-effective way. This is the key to business stability, growth, and competitiveness regardless of market or sector.
Without true workflow automation, the old aphorism “time is money” becomes literal since lack of agility, visibility, flexibility, and scalability carries heavy costs. If your enterprise has historically used AutoSys, you’re seeing its limitations as a legacy tool.
Those limitations and what they mean to the competitiveness of your financial services enterprise may have you looking at a migration to Airflow and Cloud Composer. This blog goes through some of the common pain points of AutoSys and how a Cloud Composer migration will eliminate those challenges. While the focus is on financial services, these gains of migration to Cloud Composer are true across any enterprise sector using legacy AutoSys.
The first version of AutoSys hit the market in the mid-90s and is still heavily used by the financial sector. While still heavily in use across different sectors, it’s got a dwindling user base and looks to have limited updates that make it somewhat inflexible for today’s cloud and digital enterprises.
Cloud Composer is a managed Apache Airflow service that helps you create, schedule, monitor and manage workflows. There are some big differences between Cloud Composer and AutoSys. A closer look shows why it may be a good time to consider migrating to Cloud Composer sooner rather than later.
AutoSys is a very structure-based, license-heavy application that has inherent complications because of its legacy status. More importantly, its future likely holds few if any substantive upgrades via versioning. This is a big problem for enterprises that must deal with unsustainable costs of IT maintenance and the skills gap needed for management.
AutoSys is a license-based model, so there is a license fee attached to each new job. That adds a lot of extra cost that goes into maintaining and scaling up if needed. Cloud Composer is a usage-based fee model, so as your jobs are scaling up, you’re only being charged based on the amount of resources you use.
Techolution recently collaborated with Google on a migration proof-of-concept (POC) project of legacy AutoSys to Cloud Composer for an education partner. Education is also one of several major sectors that still heavily use Autosys in addition to the financial sector. The POC revealed several major benefits of Cloud Composer over AutoSys, starting with Lower costs.
The cost analysis for this education partner project showed that composer is at least 45 percent cheaper based on historical GCP Cloud Composer project data. This is because the Composer usage model calculates costs based only on the number of machines needed for scalability targets to meet job needs and run data orchestration pipelines.
These costs were based on this education partner project use case for migration where upscaling was the primary goal. However, we can calculate a comparable level of cost savings across other Techolution-led workflow automation migration projects with other sector partners.
Personnel costs for admin and support teams is another important cost savings of Composer because AutoSys uses the Job Information language (JIL) for programming along with Shell scripting. These aspects make it difficult and costly to run and maintain AutoSys because it’s difficult to program and execute tasks in that Shell environment.
Costs become even higher when you need to replace your AutoSys experts that know JIL. The pool of JIL experts is dwindling as fewer young programmers are learning the language. That puts the remaining experts in higher demand, which means higher salaries, if you can get them when you need them.
Composer is based on the ubiquitous Python language, which is far more flexible and provides a growing pool of affordable experts. Python and Java also provide a greater number of efficient tools and technologies that aren’t necessarily available or easily integrated with AutoSys.
Things like ease of use and integration are always major considerations since time is money with workflow automation across different industries. Cloud Composer has a clear advantage over AutoSys in many ways because:
It’s important to remember that AutoSys is an application, while Cloud Composer is a web-based tool that abstracts Apache Airflow processes. This ties into making workflows more reliable, transparent, and easy to manage, which starts with the UI. AutoSys and Cloud Composer have very different approaches where:
Ease of scalability is a major differentiator between AutoSys and Cloud Composer because:
Even when migration becomes inevitable with your AutoSys workflow automation application, you ace other challenges. We can see these in terms of personnel, expertise, and discovery across the legacy AutoSys application. Equally important is having the experts that also know Cloud Composer, which often means finding a partner to support the migration.
Every migration project brings its own specific challenges and AutoSys to Cloud Composer is no different. While we’ve defined just some benefits of cost savings, scalability, and expertise with Composer, it takes an experienced cloud migration partner to realize them.
Techolution composed its team to be experts in all aspects of cloud services with deep experience in the financial services sector. While Techolution is a Google Cloud Premier Partner, they bring the same level of knowledge with AWS and Azure. That cloud-agnostic approach enables them to deliver the right solutions that fit the business outcome needs of every enterprise in financial services and beyond.
By working on a fixed-bid model, they can deliver projects within budget while also devoting any number of experts needed to make it successful. That includes projects where deep AutoSys experience is necessary.
To speak with an expert and learn more about why Techolution should be your migration partner, click here.