DevOps for Data

DevOps can help you get better results quicker and faster. And it’s ideal for databases too.

Opportunity won’t wait

You need to meet your customers’ needs and stay ahead of your competitors.

So you need to bring new services, products and channels to market as quickly as possible.

As the business evolves, so do its IT applications. As applications evolve, so do the data stores and the data they contain.

Developers have to deliver what the business needs, so it can give customers what they want. But staying up to speed can be a challenge.

And that’s what DevOps is all about.


What is DevOps for Data?

‘DevOps’ = ‘Development’ + ‘Operations’

As its name suggests, DevOps is all about deeper collaboration and co-ordination between software development and IT operations teams.

Getting the data element right is key to the success of any application, and many organisations are realising that it is a vital factor in achieving the overall business benefits promised by DevOps. Triton’s DevOps for Data services extend the traditional DevOps project to ensure the data requirements are met.

The DevOps for Data partnership lasts right through the service lifecycle, from design and development through to support.


Learn about the benefits of DevOps for Data.

Accelerate Innovation

How you can use DevOps for Data to improve your agility and power digital transformation

Increase Quality

DevOps for Data improves the quality and suitability of the end product

Reduce Costs

DevOps for Data brings virtualisation, automation and standardisation allowing better utilisation of existing assets

Integrate Platforms

DevOps for Data makes any database a first-class citizen in the DevOps process


Why do DevOps for Data?

DevOps is about doing things better… but it’s also about doing better things.

The main aim of DevOps is to shorten the development lifecycle and improve software quality. However, it’s not just about technology. It’s a whole philosophy of software delivery, encompassing people, processes and tools (such as Lean and Agile).

Teams who use DevOps create higher-quality, more reliable products. But they do it quicker, with less risk and at lower cost. So they can deliver exactly what the business needs, when it needs it.

We’ve helped many large organisations adopt DevOps for Data. With our help, they’ve accelerated test and delivery cycles and achieved multi-million-pound savings. We’re proud to say that our DevOps for Data work has brought us, and our customers, IT industry awards for innovation.

Whether at the start of your development project or looking to modernise existing processes, we evaluate your current capabilities and objectively compare against industry best practice to highlight specific opportunities for improvement. With a set of clear well-defined objectives, we can work with the owners and practitioners of the current services to plan implementation of DevOps tools, processes and culture change.

Just as DevOps doesn’t end with development of an application, our DevOps for Data service doesn’t end with development of the plan. Implementation services ensure the objectives of DevOps are achieved, working closely with a client’s staff to assist with the delivery of DevOps for Data practices, tools and processes.


Get Started


To talk to our expert team about how DevOps could help you get more from your mainframe, contact us now.




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Fast-changing markets demand better products, launched quickly. But it can be hard for development teams to keep pace with the demands of the business whilst satisfying the need for data integrity and database performance.

How DevOps for Data drives innovation

  • By integrating the work of development and operations teams, DevOps for Data helps them work together to get better results, faster.
  • The DevOps approach promotes collaboration throughout your organisation, not just technical teams. With increased business knowledge in the development phase, you get the solution your organisation needs.
  • With DevOps for Data, you can advance development on multiple tracks at once. Even code that affects the database can be deployed safely hundreds or even thousands of times per day via continuous integration and deployment (CI/CD) techniques, enabling your organisation to react to changing markets and customer needs faster than the competition.

Delivering quickly is no good if the end product is faulty, slow or does not meet the needs of the customer or business requirements. But manual quality checks and traditional working practices add time and can cause friction between teams.

How DevOps for Data improves quality

  • By moving to an iterative development process, product owners, developers and operators can regularly review progress of each feature being developed to ensure the result matches the vision. These small, frequent reviews promote agile co-operation between the teams.
  • With more frequent and granular reviews, bugs can be found sooner and the impact to resolve them is minimised with less dependent code changes and retesting required. The technical debt accumulated with each release can be substantially reduced.
  • With the use of both open source and proprietary tools, code can automatically be reviewed not just for functional defects, but security, performance and stylistic problems can be detected, measured and reported. Existing tools used for code quality such as code linting and static code analysis can be extended to support the database artifacts and integrated as part of the overall build process.
  • Subject to the appropriate checks and controls, database code can be promoted across all environments up to production using reliable tooling to remove the risk of human error. Often this can be done by automating existing tooling, enabling proven tools to offer further value.

With a faster pace of development comes a requirement for more resources to develop and test with, but the business pressure to ‘do more with less’ has never been greater.

How DevOps for Data reduces costs

  • Virtualisation is a core component of DevOps for Data. Instead of running expensive, underutilised environments for development and test they can be started, stopped and reset as needed, and can run on more cost-effective infrastructure – from just a database service in the cloud to a fully virtualised mainframe machine running on commodity hardware.
  • With expert knowledge of data systems, the DevOps for Data service enables the same standard software tools used for orchestrating application development to be securely and reliably used to control the database components – reducing setup, support and maintenance costs. Open source toolsets such as Git, Jenkins and Ansible are now a possibility.
  • Automating the mundane tasks of deployment and code review allows skilled staff to concentrate on adding value to enhance system design and performance, increasing their productivity, engagement and job satisfaction.
  • By integrating systems based on traditional technologies such as UNIX or mainframes that might otherwise be considered ‘legacy’ into the DevOps process, they can continue serving the business and enhance return on investment.

For many businesses, integrating the database is the last piece of the DevOps puzzle.  As the cornerstone of any application, the mix of art and science required to produce a performant, secure, reliable database can seem impossible to master with modern DevOps techniques.

How DevOps for Data facilitates platform integration

  • With the capability and experience to integrate all types of databases into the DevOps pipeline, systems as disparate as mainframes and cloud platform-as-a-services can work together as integral parts of the enterprise.
  • With the database a part of the DevOps CI/CD pipeline, new architectural styles can be more easily supported – microservices, event-based processing, hybrid cloud developments all become a realistic prospect, enabling existing data systems to provide services to more applications.
  • In addition to enabling agile development, DevOps for Data makes the platform itself agile using modern tools such as Ansible to build even mainframe environments. And it need not stop with the database itself, other related components such as transaction managers can be included to provide a complete technology stack.
  • Introducing modern, cross platform graphical and command line tools for creation and maintenance of database objects and jobs provides a familiar and easy to learn environment for the next generation of staff.