Introduction
Data Operations is the process of managing and processing data in a business. It involves all the activities that involve storing and retrieval information, as well as making decisions. Data Operations has been gaining popularity over the past few years because they are essential to ensure that organisations stay ahead of competitors in their respective sectors. The word “DevOps” means different things to different people, but broadly speaking it refers to several practices that are designed to facilitate collaboration between IT and development teams at all levels of an organisation, including project management, automation tools, continuous delivery pipelines (CD), code review processes — all of which help teams build better software faster than ever before. DevOps needs to be approached holistically in some ways, but it also requires attention to details specific to each part of a company’s infrastructure.
Proper integration of systems and processes for data management is essential for success.
Data integration is a process of bringing together various systems, databases, and applications. It’s necessary for companies to have a well-integrated data management strategy because it allows them to analyse their information quickly and effectively.
Data integration helps organisations:
- Create comprehensive reports from different sources in a single platform, which can then be used by managers or analysts for decision making purposes.
- Access real-time information about customer activity so that they can respond quickly if necessary — whether it’s via an app on their phone or an email notification sent out automatically when certain events occur within their business model (for example: when someone places an order).
Automation leads to rapid iterative improvement in performance and can be considered as important as cultural change when building out a DevOps or DataOps system.
Automation helps to reduce the time it takes to get a new feature or product out: you don’t need to wait for humans, who might be busy with something else, before you can start testing your software. It also helps reduce the time it takes to fix bugs and improve performance: automated tests are quick, so they allow you to catch errors much more quickly than if they were manually written by developers (who tend not want them). Finally, automation allows continuous improvement processes that keep code bases clean and free of defects at all times (which means fewer errors).
DataOps and DevOps are similar enough that you can use ideas from one to help with the other.
DataOps is a subset of DevOps, which is itself a subset of software development. It’s important to note that while both concepts aim at building better software, they do so in different ways: DataOps focuses on collecting data and analysing it so we can make better decisions about what kind of data we need; whereas DevOps focuses on delivering those products faster so our customers can get them faster too!
PS: Any views expressed on this blog are personal and belong solely to me as the blog owner, and do not represent the views of people, institutions or organisations that I may or may not be associated with in professional or personal capacity unless stated explicitly.
I am a data science practitioner and a strong promoter of making this wonderful field accessible for all.
Thoughts? Leave a comment and I’ll reply as soon as I could. Stay tuned for more posts.