𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗮 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗘𝘃𝗲𝗻𝘁 𝗦𝗼𝘂𝗿𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺

𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗮 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗘𝘃𝗲𝗻𝘁-𝗦𝗼𝘂𝗿𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺

You need to handle huge streams of data. You need correctness and the ability to replay events. Event sourcing is the answer.

Here is how it works:

• Event Sourcing: Save state changes as a sequence of immutable events.
• CQRS: Separate your write models from your read models.
• Event Store: Use an append-only log to save events.
• Projections: Create fast read models from your events.

Your architecture needs these parts:

• Ingestion layer: Receives events from your services.
• Event store: Saves events with a timestamp and ID.
• Command API: Handles writes.
• Read API: Serves data to dashboards.

Follow these rules for better results:

• Use unique IDs to stop duplicate data.
• Version your events to handle schema changes.
• Keep event payloads small.
• Use a durable log like Kafka.

Why use this approach?

• You get a full audit trail.
• You fix bugs by reprocessing old events.
• You scale reads and writes separately.

Monitor your system:

• Track event rates and latency.
• Check the lag in your projections.
• Validate your data quality.

Source: https://dev.to/therizwansaleem/designing-a-scalable-event-sourced-analytics-platform-10lf