Recently, we hosted a live webinar to talk about the evolution of data observability: what’s changing, what’s broken, and how SYNQ is building for what’s next. Our CEO & Co-Founder Petr Janda joined by our Field-CTO Stepen Murphy walked through the macro trends reshaping data workflows, where we see AI adding the most value, and how Scout, our autonomous AI agent, is helping customers resolve data quality issues before they become incidents.

Discover how modern data teams can define, measure, and improve data quality across the stack. This guide covers key dimensions, practical metrics, common pitfalls, and the tools that help you build reliable, trustworthy data products at scale.

This guide, tailored to modern data & analytics engineers, breaks down the what, why and how of data observability, with practical insights to help you build reliable data.

What's new in SYNQ

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

This guide is designed to help data practitioners, engineers, and decision-makers thoroughly understand the key differences between dbt and SQLMesh.

Learn what data products are, why they matter, where to define them, and how to monitor them effectively. An introduction into data products for data engineers, analytics leaders, and modern data teams.

What's new in SYNQ

Taking the first steps to getting started with Data Products

What data from hundreds of top data teams tells us about team size, role distribution, data-to-engineer ratios, and salaries.

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

Examples of how data products are used to solve real-world problems and drive meaningful business impact in B2B SaaS, eCommerce, logistics, and fintech

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.

How and when to extend dbt with a data reliability platform, and key considerations for vendor selection

How treating issues and incidents differently has driven accountability and a trace of systemic issues

A deep dive into the ratio of data roles across insight, data engineering, and machine learning

Explore how data observability integrates into each stage of the data pipeline, from source systems to analytics, enhancing data quality and reliability.

How to drive impact, where to focus, and what skills are required to succeed in the best data governance teams

Building reliability by design for classical ML use cases and Gen AI

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

Learn why tracking data incidents, not just issues, is key to reliable SLAs. Explore best practices for detection, triage, and postmortems.

A toolset for determining if a data incident is a $1,000 or $100,000 problem

Building new software engineering-like workflows

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

A toolkit for defining and activating ownership across the data team, upstream teams, and business stakeholders

Practical steps for measuring and actioning data quality

Learnings from bringing ownership of data quality across the business

A year of growth, learning, and shipping

Combining dbt tests and anomaly monitors to proactively detect data quality issues on sources, features, and predictions

Synq Data Products create greater clarity, foster a stronger sense of ownership, and enhance reliability for your business-critical data within scaling data ecosystems.

Data reliability challenges fintech companies face with reconciliation, automated decisioning, regulation, and customer experience

How to build reliable data pipelines by combining domain knowledge and automated data checks

How to improve the ROI of your marketing campaigns by reducing upstream data quality issues

How leading companies drive value-driven ad spend allocation based on customer lifetime value and overcome costly data issues

Practical steps to identifying business-critical data models and dashboards and drive confidence in your data

How to use data contracts, tests and Synq to enforce quality and activate ownership between producers, consumers and data teams

Why data lineage workflows break with scale and how to fix it

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.

How asset sprawl in large data teams makes onboarding, development, monitoring and self-serve harder

What the number of data and product people relative to engineers can tell us about how top tech scaleups operate

Practical steps for managing incidents in data teams for faster resolution, better transparency and less hassle

The difficulty of building alerting workflows fit for scale

Four improvements to your dbt workflows to build reliable data for scaling data teams

A pay benchmark for data analysts, data scientists, analytics engineers and data engineers across hundreds of companies

10 practical steps from top data teams

Things to consider when setting severity levels, how to act based on different severity levels and simple steps to getting started

What to consider before defining ownership, how to manage ownership of upstream and downstream teams and a few simple steps to getting started

Fintech startups have multiple times the amount of critical metrics and data systems making the stakes higher than anywhere else.

What’s the right ratio of data roles in scaleups and why it should probably be higher than you think.