How Can You Leverage DevSecOps Approach For Secure Data Analytics?
DataOps (data operations)
is a process-oriented agile methodology for developing and
It collaborates DevOps teams with data scientists and data engineers to provide the tools,
processes, and organizational structures for supporting a data-focused enterprise.
The goal of DataOps is streamlining the design, development, and maintenance of data-based
applications and data analytics. It aims to improve the way data is managed, products are
designed and coordinate these improvements with the business goals.
What Are DevSecOps And DataOps?
DevSecOps is a software development methodology that incorporates continuous delivery to
the SDL by combining operations teams, development teams, and security teams into a single
unit responsible for a product or service.
DataOps builds on the concept of DevSecOps by adding data specialists — data developers,
data analysts, data scientists, and/or data engineers- for focusing on the collaborative
development of data flow and the continuous utilization of data across the organization.
The DataOps Infrastructure
Implementation of DataOps requires more than a change in workflow or mindset; it also needs
infrastructure modifications. For example, new architecture patterns focus on agile feedback
loops and automation.
DataOps also requires implementing next-generation technologies designed for storage and
analytics. For example, teams need to adopt redundant, cluster-based storage for ensuring that
data processing pipelines are scalable and highly available.
Environments may also need configuration and deployment to ensure compliance with data
privacy regulations. This is true for both test environments, production environments, and
The DataOps teams may need to address another change- the workload diversity that is
supported. For pipelines to offer agility, solutions should not be distributed by task or team but
integrated into a single infrastructure.
This requires incorpo