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Enterprise Layer Intelligence
Categories/Data Platform & Integration/Artie
Artie

Artie

Replicate database changes directly into your cloud data warehouse in real time

Pricing model

Free Tier, Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Artie to stream database changes directly to your data warehouse with sub-minute latency without managing complex streaming infrastructure. It reads transaction logs from production databases like PostgreSQL, MySQL, MongoDB, and DynamoDB, delivering updates with exactly-once guarantees. The system handles schema evolution automatically and supports column-level data masking, encryption, and hashing to protect sensitive records. You can run replication pipelines to support fresh AI models, analytics dashboards, and testing frameworks without writing custom Kafka consumers or maintaining complex Debezium configurations.

What Artie does

Connect transactional databases to cloud data warehousesReplicate specific tables and columns in real timeConfigure data masking and encryption rulesTrack schema changes automatically between sources and destinationsMonitor replication latency and pipeline healthFan in data from multiple single-tenant databasesDeploy replication pipelines inside private cloud environmentsStreams database changes with sub-minute latencyGuarantees exactly-once data delivery at scaleHandles automatic schema evolution without breaking pipelinesMasks, hashes, and encrypts sensitive PII columnsEliminates the need to maintain Kafka or Debezium clustersConnects directly to operational databases like Postgres, MySQL, and MongoDBFans in single-tenant databases into unified destination schemas

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
FreeFree
Growth (annual billing)$500 / mo, billed annually
Growth$600 / mo

Tutorials & Demos

Frequently asked

PostgreSQLPostgreSQLMongoDBMongoDBSnowflakeSnowflake

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Artie, data replication, real-time data streaming, change data capture, CDC, database replication, PostgreSQL, Snowflake, MySQL, MongoDB, DynamoDB, schema evolution, sub-minute latency, data pipeline