Homeā€ŗCompaniesā€ŗArtie

Artie

Software that streams data from databases to warehouses in real-time

Artie is software that streams data from databases to data warehouses in real-time. Today, most companies run their ETL process every few hours or overnight, so their data warehouse is always out of date; with Artie, the warehouse always has live production data.
San Francisco, CA, US
$150K - $215K
0.50% - 1.00%
3+ years
Artie
Founded:2023
Team Size:7
Status:
Active
Location:San Francisco
Group Partner:Jared Friedman
Active Founders

Jacqueline Cheong, Founder

Co-founder and CEO at Artie. Building a real-time data replication platform. Previously a software investor at Balyasny.
Jacqueline Cheong
Jacqueline Cheong
Artie

Robin Tang, Founder

CTO @ Artie. Software engineer with a background in distributed systems.
Robin Tang
Robin Tang
Artie
Company Launches
šŸ˜ Artie for Enterprise - Data Replication at Petabyte Scale
See original launch post ā€ŗ

TL;DR

Artie streams data from databases to data warehouses in real-time and more reliably than traditional ETL solutions. Many companies are still running their ETL process every few hours, so their data warehouse is constantly out of date; with Artie, the data warehouse always has live production data.

Artie was founded to solve an enterprise data movement problem. Today, weā€™re launching Artie for Enterprise, for teams requiring high-volume, low-latency data replication that scales effortlessly. Whether dealing with massive volumes, strict security requirements, or mission-critical workloads, Artie ensures data is always live, accurate, and easy to manage. šŸš€

Problem

Enterprises need real-time, reliable, and secure data replicationā€”but existing solutions make this far more painful than it should be.

Traditional ETL pipelines often run on fixed schedules, updating data warehouses hours behind reality and leaving teams with outdated insights. Many replication tools try to brute-force data movement by running expensive, inefficient queries that increase load on production databases, slowing down applications and, in extreme cases, even bringing them down entirely. Scaling these systems across thousands of tables typically requires manual, error-prone configurations, forcing teams to spend countless hours debugging pipelines just to keep data flowing.

On top of that, many enterprises operate in highly regulated environments where data canā€™t be fully offloaded to the cloud. Traditional replication solutions struggle to support hybrid deployments, forcing teams to choose between security and ease-of-use.

Artie for Enterprise provides reliable, secure, and high-volume data replication

Terraform support

ā€œUm, we have thousands of tables to configure. Do I really need to click thousands of times on your dashboard to get connectors set up?ā€ We hear this question a lot and our answer is ā€œAbsolutely notā€, which is where our Terraform provider comes in.

Just because your data volume is high does not mean management needs to be equally complex. No more endless clicksā€”just code it, deploy it, and go.

Multi-step merge

Merging large, frequently updated tables can be expensive and slow. We wanted to solve this problem without forcing customers to increase compute costs or scale up their virtual data warehouses.

With multi-step merge, Artie now loads data into a staging table in multiple bursts, allowing updates to accumulate before merging into the target table. This reduces latency and improves efficiency. Customers can now control how often data lands in the staging table before triggering a final mergeā€”giving them real-time syncs with more flexibility and cost savings.

Hybrid deployment

Enterprise-grade replication means deployment flexibility is non-negotiable. There are many organizations out there that handle data containing sensitive information and are required to operate under strict regulations.

Our hybrid deployment model ensures the security of on-premise data processing with the ease of use of cloud services. Whatā€™s more? Artieā€™s fully-managed service allows for zero-maintenance, and removes the need to install and manage client-side agents.

MySQL Connector upgrade

Weā€™ve made major improvements to our MySQL connector, making it enterprise-grade with improved performance, efficiency, and automation. These updates streamline database operations, reduce disk usage and I/O load, and automates data synchronization without complex configuration requirements and management overhead.

Key improvements include full DDL and gh-ost migration support for seamless, non-disruptive schema changes, automatic fan-in for partitioned tables, and GTID support for reliable transaction replication.

PostgreSQL CTID scanning

Backfills can be disruptive and resource-intensive. Thatā€™s why Artie is built to seamlessly recover from errors and avoid backfills unless absolutely necessary.

However, in the event that backfills are required, how do we minimize disruption? Enter PostgreSQL CTID; an alternative backfill method that is 10-20x faster than traditional methods.

Datadog integration

At Artie, weā€™ve always been big on observability. What good are your data pipelines if nobody has visibility into them? We prioritize accurately monitoring your pipeline performance and making sure you know whatā€™s up.

With our Datadog integration, you can now track all Artie-exposed metrics directly from your Datadog dashboard. This allows you to proactively monitor pipeline performance, set up custom alerts, and build dashboards for deeper insights.

Stay on top of your data pipeline healthā€”effortlessly.

Curious to learn more?

Follow our journey on LinkedIn, X and reach out to us here to see how you can benefit from enterprise-grade replication!

Other Company Launches

Artieā€™s Analytics Portal - Real-time data pipeline observability

Real-time observability into database pipelines and peripheral infrastructure
Read Launch ā€ŗ

Artie - real-time data streaming for databases āš”

We transfer data from databases to data warehouses in real-time with CDC streaming
Read Launch ā€ŗ