Airbyte
Open-source data integration engine for ELT pipelines across data sources
Artie Transfer
Real-time CDC replication from OLTP to OLAP databases
Stream production data from operational databases to warehouses like Snowflake, BigQuery, Redshift, and Databricks with sub-minute latency using change data capture.
Overview
Real-Time Data Replication Without the Wait
Artie Transfer eliminates the hours-to-days lag inherent in traditional batch ETL pipelines. By leveraging change data capture (CDC) and stream processing, it delivers production data to your warehouse in under a minute—keeping analytics fresh as business happens.
Built for Scale and Simplicity
Designed for teams tired of managing complex DAGs and Airflow schedules, Artie requires only a simple configuration file to start replicating data. It automatically detects schemas, creates tables, and merges downstream changes without manual intervention. Idempotent processing and automatic retries ensure reliability, while built-in telemetry and error reporting provide visibility into every sync.
From Gigabytes to Terabytes
Whether you're moving 1GB or 100+ TB, Artie scales seamlessly across a wide range of sources—including PostgreSQL, MySQL, MongoDB, DynamoDB, and Oracle—to destinations like Snowflake, BigQuery, Redshift, Databricks, and S3. Written in Go and distributed as cross-compiled binaries and Docker images, it integrates with Kafka for robust message queuing and fits naturally into modern data stacks.
Highlights
Sub-minute latency through CDC and stream processing
Automatic schema detection, table creation, and change merging
Idempotent processing with automatic retries for reliability
Supports 7 OLTP sources and 8 OLAP/data lake destinations
Pros
- Dramatically reduces data latency compared to batch ETL
- Minimal configuration and automatic schema management
- Scales from gigabytes to 100+ terabytes of data
- Built-in telemetry and error reporting for observability
Considerations
- Requires Kafka infrastructure for message queuing
- Limited to supported source and destination databases
- CDC setup on source databases may require additional configuration
- Relatively new project with smaller community compared to established ETL tools
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Fit guide
Great for
- Teams needing real-time analytics on production data
- Organizations scaling beyond batch ETL performance limits
- Data engineers seeking simpler alternatives to Airflow DAGs
- Companies with 1GB to 100+ TB replication workloads
Not ideal when
- Projects requiring complex data transformations during replication
- Teams without Kafka infrastructure or CDC-capable sources
- Use cases where hourly or daily batch updates suffice
- Environments needing databases outside the supported list
How teams use it
Real-Time Business Intelligence
Analysts query live production data in Snowflake for up-to-the-minute dashboards and reports without waiting for nightly batch jobs.
Operational Analytics at Scale
E-commerce platforms replicate 50+ TB from PostgreSQL to BigQuery, enabling real-time inventory and customer behavior analysis.
Multi-Source Data Consolidation
Enterprises stream data from MongoDB, MySQL, and DynamoDB into a unified Databricks lakehouse for cross-system analytics.
Compliance and Audit Trails
Financial services maintain sub-minute replicas in Redshift for regulatory reporting and fraud detection without impacting transactional systems.
Tech snapshot
Go98%
Python1%
Makefile1%
Shell1%
Dockerfile1%
Frequently asked questions
How does Artie Transfer achieve sub-minute latency?
Artie uses change data capture (CDC) to stream database changes in real time via Kafka, processing updates continuously instead of waiting for batch schedules.
What databases are supported as sources and destinations?
Sources include PostgreSQL, MySQL, MongoDB, DynamoDB, DocumentDB, Oracle, and SQL Server. Destinations include Snowflake, BigQuery, Redshift, Databricks, S3, Iceberg, SQL Server, and PostgreSQL.
Do I need to manually create tables in my data warehouse?
No. Artie automatically detects schemas, creates tables, and merges schema changes downstream without manual intervention.
Is Kafka required to run Artie Transfer?
Yes. Artie currently uses Kafka as the default message queue for reliable, scalable stream processing between sources and destinations.
How do I get started with Artie Transfer?
Set up a simple configuration file specifying your source and destination, then run the binary or Docker container. Examples and a getting started guide are available in the repository.
Project at a glance
Active- Stars
- 793
- Watchers
- 793
- Forks
- 52
Repo age3 years old
Last commit18 hours ago
Primary languageGo
Last synced 3 hours ago