Crafting a Whiteboard Architecture for a Data Platform

To Nha Notes | March 19, 2024, 10:16 a.m.

1. Data Ingestion:

   • Consider using a streaming platform (such as Apache Kafka or Amazon Kinesis) alongside batch-based ingestion tools. Streaming allows real-time data processing and reduces latency.
   • Integrate schema validation during ingestion to ensure data quality and consistency.

2. Data Transformation:

   • Instead of a single “Clean Zone,” consider a more modular approach:
     • Raw Zone: Store raw data as-is for historical purposes.
     • Staging Zone: Perform initial data cleaning, validation, and enrichment.
     • Curated Zone: Processed data ready for consumption.
   • Implement data lineage tracking to understand transformations and lineage across zones.

3. Data Storage:

   • Explore cloud-native storage solutions like Amazon S3 or Google Cloud Storage for scalability and cost-effectiveness.
   • Use columnar storage formats (e.g., Parquet) for efficient querying and compression.

4. Data Catalog and Metadata:

   • Establish a centralized metadata repository to track data assets, lineage, and business context.
   • Leverage tools like Apache Atlas or AWS Glue for automated metadata management.

5. Data Access and Querying:

   • Consider using a data virtualization layer (e.g., Presto, AWS Athena) to provide unified access to data across zones.
   • Enable fine-grained access control to restrict data access based on roles and permissions.

6. Monitoring and Observability:

   • Implement real-time monitoring for data pipelines, including alerts for failures or anomalies.
   • Use tools like Prometheus or Datadog for observability.

7. Security and Compliance:

   • Integrate encryption at rest and in transit for data security.
   • Ensure compliance with data privacy regulations (e.g., GDPR, CCPA).