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Data Architecture and Engineering
Building a scalable, flexible, and resilient data platform is essential to supporting both Operational and Analytical use cases. This involves designing an Architecture that is not only capable of managing diverse data types but one that is also able to adapt to ever-changing functional and technology trends such as
Data Mesh, Decentralized Domain-based ownership, Data Sharing, and Generative AI.
Our take on the Modern Data Architecture
Each of our proposed Building Blocks must have a business-driven strategic purpose to find a fit within the
Modern Data Architecture and must be examined against the recommended factors.
A few key Building Blocks are as listed with factors for consideration.
Data Ingestion and Transformation
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Scalability and flexibility to handle diverse data sources and formats, supported by both batch and real-time processing.
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Support various data types—Structured, Semi-Structured, and Unstructured.
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Mechanisms for data cleansing, validation, and quality control to ensure high data accuracy.
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Reliability and fault tolerance with built-in error handling, retries, and recovery processes to maintain continuous operation in the face of potential failures.
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Robust monitoring and observability features for tracking performance, diagnosing issues, and maintaining smooth operations as the platform scales.
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Transformations should be modular and reusable to streamline development and adaptability.
Decentralized Approach
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Each team is responsible for managing, storing, and securing its own data, fostering autonomy and agility, allowing teams to make decisions based on their specific needs and use cases without waiting for central authority approvals..
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To ensure consistency and collaboration, there must be standardized frameworks for governance, security, and interoperability.
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Data must remain accessible across teams through well-defined APIs, shared data models, and governance policies to prevent data silos, while maintaining security and compliance standards.
Data Products
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Refers to a well-defined, reusable dataset or data service that is treated as a product and designed for specific business needs or use cases.
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Includes tools, insights, and interfaces needed for users to consume, analyze, or act on the data easily.
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Key attributes of a data product include:
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Discoverability and Accessibility: It should be easy for users to find and access the data product, with clear documentation and interfaces.
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Quality and Reliability: It must ensure high data quality, accuracy, and reliability, along with proper governance and compliance standards.
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Scalability and Reusability: A well-designed data product is scalable to support different users and reusable across multiple use cases, promoting efficiency and consistency in data-driven decisions.
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Data Sharing and Data Exchange Internal/External
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Enable seamless data sharing across cloud environments, regions, and various stakeholders such as B2B and B2C partners, suppliers, and vendors.
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Employ advanced data-sharing frameworks that provide secure, real-time access to data without physically moving or duplicating it.
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Leveraging technologies such as federated queries, data virtualization, and cloud-native data sharing services, enterprises can allow various parties to access and analyze data directly from its source, maintaining data integrity and reducing latency.
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Embed Governance and Security features when planning for sharing:
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Data Masking Policies: Sensitive data should be automatically masked or encrypted when accessed by unauthorized users. For example, customer personal information can be obfuscated for marketing teams but left visible for legal or compliance teams.
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Cross-Domain Access Controls: As data is shared across different business units or domains, fine-grained access controls must ensure that users can only access the data necessary for their roles. This requires implementing Attribute-Based Access Control (ABAC) and\or Role-Based Access Control (RBAC) models to prevent unauthorized cross-domain data access.
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Data Product Sharing: Data products, which are curated datasets for specific use cases, should be shared with strict governance controls. Sharing policies must specify which teams or external partners can access particular data products and under what conditions, while ensuring that the data adheres to compliance requirements such as GDPR or CCPA.
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Industry specific Modern Data Architecture
Data Strategy and associated Architectural aspects such as Security Definitions, Real-time processing requirements, Data Complexities, and Regulatory Limitations are unique to Industries.
Adapting the Modern Data Platform Architecture to these industry standards guarantees that it not only facilitates day-to-day corporate operations but also generates competitive advantage data driven insights.
Here are some Key Elements that must be considered when architecting Modern Data Platforms specific to the Industry
Compliance and Regulatory Alignment
Finance: Compliance with regulations like GDPR, PCI-DSS, and SOX, which demand stringent security controls, auditability, and encryption.
Healthcare: Platforms must comply with HIPAA for privacy of medical records and data security, ensuring that patient data is handled according to strict standards.
Retail: In retail, adhering to data privacy laws such as CCPA or GDPR for customer data is critical.
A successful architecture integrates these compliance requirements at its core, automating Data Governance, Logging, and Reporting to ensure adherence without manual intervention.
Data Ingestion and Integration
Healthcare: Data can include Electronic Health Records (EHRs), imaging data, and real-time patient monitoring.
Manufacturing: Data from IoT sensors, ERP systems, and production lines must be ingested in real time.
Telecom: A telecom data platform must handle network performance data, customer usage patterns, and large-scale sensor data.
The architecture must support Diverse data formats, Structured and Unstructured data, and Real-time ingestion from sensors, devices, or external partners.
Real-Time Analytics and Insights
Finance: Real-time fraud detection and high-frequency trading require low-latency processing and advanced analytics capabilities.
Retail: Dynamic pricing, and real-time inventory management require instant insights from customer and operational data.
Telecom: Traffic management, and predictive maintenance rely on real-time analytics of vast amounts of operational data.
A modern data platform must support both streaming analytics (e.g., Apache Kafka, Apache Flink) and batch processing to address the varied real-time and historical analysis needs.
AI and Knowledge Management
A Modern Architecture must integrate with Machine Learning frameworks that support model training, deployment, and monitoring at scale. It should also accommodate industry-specific models for tasks like predictive maintenance in manufacturing or patient risk scoring in healthcare.
Knowledge Base Repositories: One of the most important element that is often overlooked in the AI universe is to promote the idea of building, aggregating, and storing the Enterprise Applications metadata in the form of an Enterprise Knowledge Base.
This is really industry agnostic and the most prominent use of this is for developing Generative AI analytical assistants that aid data engineering and business teams. The Knowledge Repository is intended to be dynamic and continuously evolving.
Data Security and Access Control
Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) to manage who can access sensitive data based on roles, locations, or other attributes.
Data masking and encryption for protecting sensitive information, such as patient records in healthcare or financial transactions in banking.
Data lineage and audit trails to track data usage and ensure compliance, particularly important in regulated industries like finance and healthcare.
A robust security framework is embedded in the platform, ensuring that data remains secure at all stages—ingestion, transformation, storage, and consumption.
Data Sharing and Collaboration
Healthcare: Sharing data between hospitals, research institutions, and insurers while complying with privacy laws.
Supply Chain and Manufacturing: Sharing data between suppliers, vendors, and logistics partners to improve efficiency and transparency.
B2B Retail: Retailers may share data with suppliers to optimize inventory management, improve demand forecasting, or manage promotions.
Industry-specific platforms enable secure, governed data sharing facilitated via API-driven data exchanges, data product sharing, and consent-based data access models.
Cloud Native and Hybrid Infrastructure
Financial institutions may store sensitive customer data on-premises while using cloud services for analytics.
Healthcare organizations may require hybrid cloud setups to manage regulatory restrictions while leveraging the cloud for large-scale data analytics.
Many industries are adopting cloud-native architectures to leverage the scalability, flexibility, and cost efficiency of the cloud while maintaining control over sensitive data. However, some industries, like finance and healthcare, may also require hybrid cloud or on-premises solutions to meet regulatory demands
