𝗕𝘂𝗶𝗹𝗱 𝗔 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺

𝗕𝘂𝗶𝗹𝗱 𝗔 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺

Your analytics system needs to scale. High data volumes break standard databases.

Use event sourcing and CQRS.

This approach separates your write path from your read path.

Here is the blueprint.

The Ingestion Layer

• Use a message bus like Kafka.
• Partition data by user ID.
• Use a schema registry to manage versions.

The Write Model

• Save every change as an immutable event.
• Use an append-only log.
• Logic emits events instead of updating tables directly.

The Read Model

• Create projections for fast queries.
• Use a columnar store for dashboards.
• Store raw logs in a data lake like S3.

The Data Lakehouse

• Keep raw events in object storage.
• Use Delta Lake or Iceberg for ACID transactions.
• Transform events into clean tables for analysts.

Key Tips

• Handle double-counting with idempotency.
• Monitor lag and throughput.
• Mask PII for security.
• Use replay tests to verify data.

Source: https://dev.to/therizwansaleem/designing-a-scalable-event-sourced-analytics-platform-with-cqrs-and-a-data-lakehouse-pattern-j0c