Define, deploy, and manage SYNQ monitors from code

Bring your data quality intelligence directly into your AI and editor workflows with the new MCP server

Catch up on SYNQ’s August 2025 updates: new Model Context Protocol (MCP) for smarter data quality workflows, monitors as code, Atlan integration, and customer success stories.

Explore SYNQ’s July 2025 updates: launch of Scout Testing & Monitoring Advisor, Airflow integration improvements, new REST API endpoints, and community highlights.

We’ve evolved SYNQ’s AI capabilities to recommend tests and monitors to put you on par with industry-leading data teams

Discover how AI agents are transforming data observability, autonomously detecting issues, diagnosing root causes, and proactively reshaping workflows for modern data teams.

See what’s new in May 2025 at SYNQ: releases around data quality, MCP developments, integrations, performance improvements, and customer spotlight updates.

Scout is our data quality agent that’s proactively monitoring, analysing, and resolving data quality issues on your behalf.

Read SYNQ’s May 2025 product updates: new Scout agent features, improvements in monitoring & observability, integrations, performance enhancements, and customer highlights.

Bridging model and table-level concepts to make the best-in-class data observability and SQLMesh integration

Combine SQLMesh DataOps workflows with SYNQ’s Data Product Observability layer to deliver reliable data products

Reflecting on 2024, SYNQ introduced the Data Product Observability Workflow, enhancing data reliability through strategic testing, ownership activation, and quality metrics. Key developments include the launch of Data Products for streamlined monitoring and SLA tracking, advanced incident management features, and comprehensive data quality analytics. These innovations have empowered enterprises like IAG and Aiven to optimize their data operations effectively.

Discover how Synq developed its Incident Management system tailored for data teams, integrating error detection, ownership, and lifecycle management to enhance data reliability.

Explore Synq’s tailored approach to incident management for data teams, highlighting key stages like issue detection, triage, handling, and post-incident analysis.

Introducing how we built code-column lineage to reimagine the data practitioners experience dealing with lineage.

As column-level lineage becomes the standard, what is next?

A year of growth, learning, and shipping

Discover how Synq’s self-learning testing engine enhances data reliability by dynamically detecting anomalies, reducing false positives, and integrating seamlessly with existing dbt tests.