DataSecOps is the incorporation of DevSecOps practices and values into the world of data analytics. The DevSecOps approach emphasizes seamless collaboration between security engineering teams, developers, and IT Operations.

1. How Can You Leverage DevSecOps Approach For Secure Data Analytics?
DataOps (data operations) is a process-oriented agile methodology for developing and
delivering analytics.
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.

2. 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
development environments.
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 incorporating big data analytics tools, like Spark, log aggregator tools, and
oversight tools, like Jira.
DataSecOps:Leveraging DevSecOps Approach
for Secure Data Analytics
When you combine data analytics and DevSecOps, you get DataSecOps.
DataSecOps is the incorporation of DevSecOps practices and values into the world of data
analytics. The DevSecOps approach emphasizes seamless collaboration between security
engineering teams, developers, and IT Operations.
DataSecOps has a similar approach for the data engineers and admins who store, archive,
analyze, and deliver data.
In other words, DataSecOps keeps security in mind while streamlining the processes involved in
interpreting, storing, and deriving value from big data.
This means that security methods are at the forefront of the implementation, where information
is delivered and encoded as well as how it is retained.
It aims to break down the traditional norm of separate teams for data storage, data analytics,
and data security.

3. With better communication and collaboration between the various teams comes faster results
and better time management.
DataSecOps is a way for optimizing your data analytics and storage and security workflows in
the similar way that DevSecOps does for application development.
What’s The Future of DataOps?
In delivery rigor, data and analytics have long lagged software engineering. Still, the traditional
Business Intelligence world has been shaken up by new technologies and talented data
professionals with varied backgrounds.
To continue delivering business value, firms must embrace practices that enhance their
long-term maturity level. The term DataOps is essential because it emphasizes the fact that
Data and Analytics projects can make substantial improvements in capability and generate
more valuable business in the same way that software engineering and DevOps have.
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