Co-Founder/CEO
Artie
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.
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Updated 2 days ago
Funding
Raised to date
$3.8MRaised 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.
+3Funding rounds are not available yet.
Founders
Product
Problem
Traditional methods of replicating data between databases and data warehouses often rely on batch processing, leading to delays, inconsistencies, and increased latency in accessing critical business data. These outdated approaches can hinder real-time analytics, slow down decision-making, and increase operational costs due to inefficient data pipelines.
Solution
Artie provides a real-time data replication platform that leverages change data capture (CDC) to synchronize only modified data between databases and data warehouses. This approach ensures low-latency access to consistent data, enabling faster analytics and more informed decision-making. The platform offers features such as schema evolution, automatic alerts for schema changes, and built-in monitoring to maintain data integrity and pipeline health. Artie's architecture is designed for reliability and scalability, guaranteeing eventual consistency and fault tolerance without storing processing data.
Target Audience
Artie is designed for data engineers, data scientists, and analytics teams who need real-time, reliable data replication between databases and data warehouses to power analytics, reporting, and data-driven applications.
Features
- Real-time data replication using change data capture (CDC)
- Support for various source databases including PostgreSQL, MySQL, MongoDB, SQL Server, DocumentDB, Oracle, and DynamoDB
- Compatibility with destination data warehouses such as Snowflake, BigQuery, Redshift, Databricks, Iceberg, S3, PostgreSQL, and SQL Server
- Automatic schema evolution and drift support with notifications
- Built-in monitoring and alerting for pipeline performance and schema changes, with Datadog integration
- Non-intrusive, parallelized backfills that do not require table locking
- History mode for creating slowly changing dimension (SCD) tables to maintain historical records
- Secure data transfer with encryption in transit and at rest, without storing processing data
- Terraform support for managing deployments via infrastructure as code