DevOps for Big Data

All our solution makes use of DevOps practices in a cloud native environment. Our fundamental practice is to perform very frequent but small updates during data engineering phases. These updates are usually more incremental in nature than the occasional updates performed under traditional release practices. Frequent but small updates make each deployment less risky. It helps teams address bugs faster because teams can identify the last deployment that caused the error.

We further follow micro-services architecture to make applications more flexible and enable quicker innovation. The micro-services architecture decouples large, complex systems into simple, independent projects. Applications are broken into many individual services with each service scoped to a single purpose or function and operated independently of its peer services and the application as a whole. We use infrastructure as code practice to provision and manage services using code and software development techniques, such as version control and continuous integration.

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DevOps for Big Data

Salient features:

  1. Continuous integration and delivery (CI/CD)
  2. Agile
  3. Infrastructure as Code (IaC)
  4. Site reliability
  5. Multi data centre active-active / active passive fault tolerant and self-healing architecture
  6. DataOps