DataOps is a collection of methodologies that has been taking the data management domain by storm. As we know, DevOps is the natural result of applying lean principles such as broad focus and continuous improvement to application development and delivery. DataOps takes these principles and applies them to data science.
A world-renowned bank's CEO was recently quoted as saying that, while there are thousands of manual banks now, the future will see only a handful of digital banks. Along with the rest of the world's critical business sectors, an overwhelming number of banks are also shifting to virtual platforms. This migration is being orchestrated by some of the best DevOps resources that the global FinTech sector has to offer.
Software developers are always on the lookout for reliable open-source infrastructure alternatives for developing and deploying applications. That is where Kubernetes, a cloud-native cluster management software solution, comes in. It provides comprehensive solutions for automated app deployment and interoperability among microservices.
For handling complex client-server environments, IT teams swear by the trails provided by logs and metrics data. These trails have a proven record in significantly reducing the Time to Detect (TTD), Time to Mitigate (TTM), and Time to Remediate (TTR) whenever the server or the environment behaves sub-optimally.
Let’s imagine a situation. There is an eCommerce app that’s receiving high traffic during sales. It was observed that the load balancer wasn’t working as expected, thereby affecting the application performance and consumers’ buying experience as well.
For more than a decade now, DevOps has been bringing the development and IT teams together to release better software, faster. Despite its relative maturity, this practice still confronts roadblocks that hinder its progress.
The Agile development teams are frequently releasing application updates. To avoid delays or failures, they focus on every aspect of the delivery cycle. The DevOps team has started involving the database code in CI/CD pipeline, which was earlier limited to application code only. By doing this, it is ensured that changes in the DB (new features, updates, or bug fixes) reach users as fast as possible.
Biotech and pharmaceutical companies are required to comply with regulations enforced by the US Food and Drug Administration (FDA). Among these regulations is the Current Good Manufacturing Practice (CGMP) which governs the quality control of not just manufacturing practices and facilities but also the technology components of the drug production process.
It’s been more than a decade that DevOps as a practice has been bridging the gap between the development and operations teams. It’s breaking down the silos between the two and automating the delivery cycle. Just like we implement DevOps to the delivery pipeline (to deal with the varying development environment), it is time that we start doing the same for databases.
The COVID-19 pandemic accelerated the digitization of business processes. While numerous tools supported rapid digitization, it somewhere impacted security, observability, traceability, and compliance of the digital solutions.