Artie provides a real-time database replication solution using change data capture to synchronize only the modified data between databases and data warehouses. This technology ensures reliable, low-latency access to critical business data, eliminating the delays and inconsistencies associated with traditional batch processing methods.
Funding
$3.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
+3Founders
Product
Problem
Enterprises often need to move data from operational databases to analytics warehouses in real time, but building and maintaining change data capture pipelines is complex, error‑prone, and requires custom infrastructure to handle schema changes, exactly‑once delivery, and high‑volume workloads.
Solution
Artie provides a fully managed CDC streaming platform that captures changes from PostgreSQL, MySQL, and SQL Server and replicates them to destinations such as Snowflake, Databricks, Iceberg, and other warehouses with sub‑minute latency. The service automatically detects and applies schema evolutions, guarantees exactly‑once semantics, and resumes pipelines without manual intervention, eliminating the risk of duplicate or missing records. Users can configure sync frequency, exclude or hash sensitive columns, and enable history mode for audit‑ready slowly changing dimension tables. Artie offers both cloud‑hosted and BYOC/on‑premise deployment options, with built‑in monitoring, alerting, and Terraform‑based provisioning to support regulated environments at scale.
Target Audience
Artie targets data engineering and analytics teams at mid‑size to large enterprises that need reliable, low‑latency replication of transactional data into data warehouses for AI, BI, and real‑time analytics workloads.
Features
- Log‑based CDC ingestion from PostgreSQL, MySQL, and SQL Server with no impact on primary databases
- Exactly‑once delivery that preserves transaction boundaries across retries, backfills, and high write volumes
- Automatic schema detection, evolution, and change notifications without manual migrations
- History mode (SCD Type 2) for complete, audit‑ready row‑level change tracking
- Real‑time monitoring dashboard with lag, throughput metrics and native Datadog/PagerDuty integrations
- Flexible deployment: fully managed cloud service or BYOC/VPC deployment with dedicated Kafka/Kubernetes clusters
- Column‑level exclusion, hashing, and compliance features for privacy and regulatory requirements
- Infrastructure‑as‑code provisioning via Terraform for repeatable pipeline creation