The way we think about data and databases must adapt to fit with dynamic cloud infrastructure and Continuous Delivery. The need for rapid deployments and feedback from software changes combined with an increase in complexity of modern distributed systems and powerful new tooling are together driving significant changes to the way we design, build, and operate software systems. These changes require new ways of writing code, new team structures, and new ownership models for software systems, all of which in turn have implications for data and databases.
Update (2019): I have co-authored a book – Team Topologies – that adds brand new material to these (original) DevOps Topologies patterns. In the book we cover dynamic organization evolution, team interaction patterns, the strategic use of Conway’s Law, monolith decomposition, and many more topics.
Update (2016): A new version of these DevOps team topologies is now here: devopstopologies.com
The new version has many new topologies that we’ve encountered in the wild and we’re taking pull requests on Github for additions and changes.
The primary goal of any DevOps setup within an organisation is to improve the delivery of value for customers and the business, not in itself to reduce costs, increase automation, or drive everything from configuration management; this means that different organisations might need different team structures in order for effective Dev and Ops collaboration to take place.
So what team structure is right for DevOps to flourish? Clearly, there is no magic conformation or team topology which will suit every organisation. However, it is useful to characterise a small number of different models for team structures, some of which suit certain organisations better than others. By exploring the strengths and weaknesses of these team structures (or ‘topologies’), we can identify the team structure which might work best for DevOps practices in our own organisations, taking into account Conway’s Law.