Architect cloud data ingestion patterns
Cloud Data Ingestion and Serving Pattern Design
Design a comprehensive cloud-based data ingestion and serving pattern capable of handling both streaming and batch workloads. You may choose your preferred cloud provider (AWS, Azure, or GCP) for this proposal.
Your design must address the following key areas:
- Upstream Ingestion Strategy: Select and justify one of the following approaches for integrating with upstream systems: Change Data Capture (CDC), Event Sourcing, or Micro-batching.
- Data Organization: Explain and justify your strategies for data partitioning and compaction within the data lake/warehouse.
- Data Integrity & Evolution: Detail how you will ensure:
- Idempotency: Guaranteeing that data operations can be safely retried without unintended side effects.
- Schema Evolution: Managing changes to data schemas over time, including handling optional fields.
- PII Tokenization: Implementing robust mechanisms for tokenizing Personally Identifiable Information to meet security and compliance requirements.
- Cost Controls: Incorporate specific strategies to manage and optimize costs, such as storage tiering, Time-to-Live (TTL) policies, and target file sizes.
- Chaos Engineering: Propose a specific incident scenario that you would simulate as part of chaos testing to validate the resilience of your design.
AI interview prep
Core dilemma
Balancing the need for real-time streaming data processing with cost-effective batch analytics, while ensuring data integrity, security (PII), and adaptability (schema evolution) across diverse ingestion patterns.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Architect cloud data ingestion patterns
Cloud Data Ingestion and Serving Pattern Design
Design a comprehensive cloud-based data ingestion and serving pattern capable of handling both streaming and batch workloads. You may choose your preferred cloud provider (AWS, Azure, or GCP) for this proposal.
Your design must address the following key areas:
- Upstream Ingestion Strategy: Select and justify one of the following approaches for integrating with upstream systems: Change Data Capture (CDC), Event Sourcing, or Micro-batching.
- Data Organization: Explain and justify your strategies for data partitioning and compaction within the data lake/warehouse.
- Data Integrity & Evolution: Detail how you will ensure:
- Idempotency: Guaranteeing that data operations can be safely retried without unintended side effects.
- Schema Evolution: Managing changes to data schemas over time, including handling optional fields.
- PII Tokenization: Implementing robust mechanisms for tokenizing Personally Identifiable Information to meet security and compliance requirements.
- Cost Controls: Incorporate specific strategies to manage and optimize costs, such as storage tiering, Time-to-Live (TTL) policies, and target file sizes.
- Chaos Engineering: Propose a specific incident scenario that you would simulate as part of chaos testing to validate the resilience of your design.