How do I move data from MySQL to BigQuery with minimal delay?

To move data from MySQL to BigQuery with minimal delay, you usually want a CDC (change data capture) pipeline rather than periodic batch exports.

Best approach: CDC / streaming replication

Use a tool that reads MySQL binlogs and continuously applies changes to BigQuery.

Common options

• Google Cloud Datastream + Dataflow + BigQuery
  • Best if you're on Google Cloud and want a managed path.
  • Captures inserts/updates/deletes from MySQL binlog with low latency.
• Debezium + Kafka + BigQuery sink
  • More flexible, more operational overhead.
  • Good if you already run Kafka.
• Fivetran / Airbyte / Matillion
  • Easier setup, often near-real-time depending on connector.
  • Managed, but costs can be higher and latency varies.

Recommended Google Cloud architecture

1. Enable MySQL binary logging

   • Use ROW-based binlogs.
   • Ensure retention is long enough for outages/replays.

2. Use Datastream to capture changes

   • Initial backfill plus ongoing CDC.
   • Low latency, typically seconds to minutes depending on load.

3. Land changes in Cloud Storage or stream via Dataflow

   • For BigQuery loading, a common pattern is:
     • Datastream → GCS → Dataflow → BigQuery
   • Or use the supported direct integration path depending on your setup.

4. Write to BigQuery in append/merge pattern

   • BigQuery is not an OLTP replica; updates/deletes usually need:
     • staging tables + periodic MERGE
     • or an event-log style table with latest-state logic

Important design notes

BigQuery is analytical, not transactional

If you need the latest row state, don’t just append blindly unless your downstream queries handle deduplication.

Typical patterns:

• Event table: keep every change event and derive current state in queries.
• Current-state table: stream into staging, then use MERGE into a target table.
• Hybrid: append events plus maintain a latest snapshot.

Minimize delay

To reduce latency:

• Use CDC instead of batch jobs
• Keep MySQL binlog healthy and accessible
• Avoid heavy transformations in the ingestion path
• Stream into staging and transform in BigQuery or Dataflow
• Partition/cluster target tables appropriately

If you only need near-real-time and low ops

A managed ELT tool is usually simplest:

• Fivetran: very low maintenance, near-real-time replication
• Airbyte Cloud: flexible, generally simpler than rolling your own
• Datastream: best if you want a GCP-native solution

If you want the lowest practical latency

Use:

• MySQL binlog CDC
• Datastream or Debezium
• Streaming sink into BigQuery
• MERGE into final tables if you need current-state records

If you tell me:

1. where your MySQL runs (Cloud SQL, self-managed, AWS, on-prem),
2. whether you need raw change events or current-state tables,
3. your latency target (seconds vs minutes),

…I can suggest the best exact architecture and steps.