Durable streaming, not file staging
A Kafka-backed log decouples source and destination. No S3 backlog, no silent latency growth, and no data loss during destination downtime. Pipelines behave the same at peak as off-peak.
Artie vs. AWS DMS
AWS DMS gets CDC live. Artie keeps it running when latency, schema changes, and data correctness actually matter. Durable streaming, automatic schema handling, and one place to see what's happening.
DMS gets CDC running. Artie is for teams who can’t afford CDC breaking.
| Factor | AWS DMS | Artie |
|---|---|---|
| Built for | One-time database migrations | Real-time data replication |
| Architecture | DMS task → S3 → Snowpipe / custom MERGE | Durable streaming on a Kafka-backed log |
| Replication latency | Near-real-time, degrades silently under load | Seconds, predictable under traffic spikes |
| Pipeline ownership | DIY: S3 staging, MERGE logic, monitoring, recovery scripts you own forever | Fully managed end-to-end Capture, buffer, merge, monitor, alert |
| Schema evolution | Manual handling – a common cause of breakage | Auto-detected DDL, deletes, and type changes |
| MERGE / upsert logic | Hand-written and owned forever | Built-in, no glue code |
| Failure recovery | Restart task; often forces a full backfill | Kafka offset replay, exactly-once delivery |
| Backfill behavior | Full re-sync that can take days; downstream products go offline | Online, parallel with CDC, replica-aware – no downtime |
| Ordering & correctness under load | Out-of-order writes and missing rows under traffic spikes | Exactly-once delivery, ordered per primary key |
| Postgres replication-slot impact | Multiple slots/tasks → WAL retention, disk pressure, primary CPU/IO impact | Single slot, replica-aware reads – no impact on the primary |
| Observability | Stitched together: CloudWatch + S3 inspection + warehouse logs | Per-table lag, throughput, and alerts via Datadog / PagerDuty |
| Failure mode | Pipelines fail quietly – broken dashboards are the first signal | Alerts fire before downstream notices |
| Pricing model | DMS instance + S3 + Snowpipe + permanent engineering time | Predictable usage tier Backfills always free |
| Deployment options | AWS only | Cloud or BYOC (your VPC) |
| Enterprise compliance | Inherits AWS posture – you wire up the controls | SOC 2 Type II, HIPAA Column-level PII controls included |
| Best fit | One-time migrations into AWS | Mission-critical real-time database pipelines |
Where Artie wins
Built-in merge & schema handling
Artie auto-detects DDL, deletes, type changes and handles MERGE for you. No hand-written upsert logic, no schema-drift babysitting, no 2 a.m. pages when a column gets added upstream.
End-to-end ownership & observability
One place to see capture, buffering, merge, lag, and throughput. Per-table alerting via Datadog / PagerDuty. We know it's broken before your dashboards do.
Predictable cost, deploy where your data lives
Fixed pricing with free backfills. Run Artie Cloud or in your VPC (BYOC). SOC 2 Type II on every plan, with column-level PII controls included.
Switching from DMS to Artie takes hours, not weeks
What stays the same
- Your AWS-hosted databases: RDS, Aurora, EC2-hosted Postgres / MySQL, DynamoDB
- Your warehouse: Redshift, Snowflake, BigQuery, Databricks, Iceberg
- Your destination schemas: same tables, same columns
- Your dbt models, dashboards, and BI tools: keep working unchanged
- Your security model: VPC, IAM, encryption, network isolation
The cutover playbook
- Run Artie in parallel with DMS during a validation period. Both write to the same warehouse without conflict.
- Compare row counts, latency, and correctness side by side on real production traffic.
- Cut downstream views or dbt models over to Artie tables once validated.
- Stop the DMS task. No rip-and-replace event, no destination migration.
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