Need for
transformation
A global corporation in the food and beverage industry
recognized the critical need for a centralized data framework. This framework
was necessary to unify and streamline data ingestion and modeling processes
across their operations. With extensive data coming from various sectors, there
was a pressing need for a robust, enterprise-grade solution. This centralized
framework would serve as the single source of truth for all organizational
data. It would ensure consistency, accuracy, and security in data handling.
The
challenge
Establishing a central framework for data management posed
significant challenges. Ensuring data security both at rest and in transit was
a top priority. The organization needed to set up strict access controls,
consumption patterns, and security standards to protect sensitive information.
Managing data from isolated sources, each with different security requirements
and access protocols, added complexity. Without a unified solution, the
organization faced inconsistencies, potential security risks, and inefficiencies
in data management.
Personas
impacted
Several key roles within the organization were impacted.
These included data modelers, data engineers, data scientists, data stewards,
and product owners. Each role required secure and efficient access to data,
tailored to their specific needs and security clearance levels. The lack of a
centralized framework led to challenges with isolated data sources and
disparate solutions. This caused delays and potential errors in their work.
The ask
The organization required a scalable and secure solution for
data ingestion and consumption within their enterprise data foundation
framework. The solution needed to integrate with their identity management
system, enabling secure access to data based on users' security classification
levels. Supporting multiple platforms, including Synapse, Databricks, and Data
Lake, while maintaining stringent security controls, was essential.
Tackling
the task
To address the requirements, an in-depth analysis of the
existing data infrastructure was conducted. The approach involved the following
aspects:
· Exploration: Potential solutions for
securing data at rest in storage accounts and data in transit through platforms
like Databricks and Synapse were explored. Collaboration with Microsoft’s
product team helped identify security design limitations and customization
opportunities within the platform’s role-based access control (RBAC).
· Design: A solution was designed to
integrate the organization’s identity management system with Azure AD groups,
allowing for seamless and secure access to data. This included implementing
nested groups to enable access inheritance based on business requirements.
· Implementation: Custom roles in Azure
were created to limit the actions an identity could perform. These roles were
assigned to AD and Azure Security groups. A conditional access policy was also
developed. This policy allowed data access based on specific metadata and organizational
structure. To further improve security, tables in Synapse were organized under
schemas based on security classification. Read access was also only granted to
the appropriate groups.
Challenges
and solutions
Challenges arose during the implementation. Customizing the
cloud platform’s role-based access control and integrating it with the
organization's complex identity management system proved difficult. These
challenges were overcome by developing custom solutions. Close collaboration
with Microsoft’s product team ensured secure and scalable access to data.
The
solution and outcome
The final solution provided the organization with a
centralized, secure, and scalable data framework. Key components of the
solution included:
· Integration of identity management with Azure AD
groups and Azure Security groups.
· Implementation of custom roles and permissions
to ensure data security and compliance.
· Streamlined access setup with minimal manual
intervention, reducing errors and improving efficiency.
· Conditional access policies based on security
classification to prevent oversharing of data.
Results
The implementation of this solution brought impactful
benefits:
· Consistency: The centralized framework
ensured consistency across various applications, reducing discrepancies and
improving data accuracy.
· Efficiency: Automated access provisioning
reduced time and manual effort, allowing teams to focus on more strategic
tasks.
· Security: Improved data security through
role-based access control and security classification-based access, minimizing
the risk of data breaches.
Future
outlook
With this robust and secure framework in place, the organization is well-positioned to onboard more sectors into their central enterprise framework. This will enable more comprehensive data cataloging and further improve the security and efficiency of their data management processes. The solution has not only addressed the initial challenges but has also set the stage for future growth and innovation in the organization’s data strategy.