Note:Create A Diagram.

As a cloud architect and engineer, design a secure cloud computing architecture (create a diagram) that will support a big data lake platform that collects data through APIs (millions of events per minute), scripted database queries (thousands of records per second), and file transfers (hundreds of gigabyte files per minute); processes the collected data in collective zones; and makes the data available to business intelligence users, data scientists, and site reliability engineers within a minute after it reaches the platform

Below are the information can be used to design a secure cloud computing architecture (create a diagram)

Cloud Design Overview:

Data Collection:

• APIs: Utilize scalable API gateways for real-time data collection, ensuring millions of events per minute are seamlessly ingested.
• Scripted Database Queries: Employ distributed database systems like Apache Cassandra or MongoDB to handle thousands of records per second from scripted queries.
• File Transfers: Leverage distributed file storage solutions such as Hadoop Distributed File System (HDFS) for handling hundreds of gigabyte files per minute.

2.2 Data Processing:

• Collective Zones: Implement microservices architecture with containerization (e.g., Docker, Kubernetes) to create collective zones for efficient and scalable data processing.
• Stream Processing: Use Apache Flink or Apache Kafka Streams for real-time stream processing, ensuring low-latency data analytics.

2.3 Data Dissemination:

• Data Availability: Implement a distributed data caching mechanism for quick access to processed data.
• Role-Based Access Control (RBAC): Utilize RBAC to manage data access for business intelligence users, data scientists, and site reliability engineers.

Tools Utilized:

Data Collection Tools:

• API Gateway: Implement tools like NGINX or Kong for API management.
• Distributed Database: Use Apache Cassandra or MongoDB for scalable and distributed database systems.
• File Storage: Leverage Hadoop Distributed File System (HDFS) for handling large file transfers.

Data Processing Tools:

• Containerization: Utilize Docker for containerization and Kubernetes for orchestration.
• Stream Processing: Implement Apache Flink for real-time stream processing.

Data Dissemination Tools:

• Distributed Data Caching: Use tools like Redis or Memcached for efficient data caching.
• RBAC: Implement RBAC using tools like Keycloak or Open Policy Agent.

User Access Provisioning:

Implement a centralized identity and access management (IAM) system to provision users based on their roles. Integrate IAM with RBAC for seamless and secure user access to the data lake platform. Use protocols like OAuth 2.0 for secure authentication and authorization.

Addressing Security Issues:

Network Security:

• Virtual Private Cloud (VPC): Create isolated VPCs to segregate network traffic.
• Encryption: Implement end-to-end encryption for data in transit and at rest.

Identity and Access Management (IAM):

• Multi-Factor Authentication (MFA): Enforce MFA to enhance user authentication.
• Regular Audits: Conduct regular audits of user access logs to detect and prevent unauthorized access.

Data Privacy and Compliance:

• Data Masking: Apply data masking techniques to protect sensitive information.
• Compliance Monitoring: Regularly monitor and update compliance measures to adhere to data protection regulations.

Incident Response:

• Security Incident and Event Management (SIEM): Implement SIEM solutions for real-time monitoring of security events.
• Automated Response: Use automation for rapid response to security incidents.