Go deeper on your data

Benchmark Your Data Team

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

Building our SQLMesh Integration

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

5 real-world data product use cases

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

SQLMesh and SYNQ integration

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

SYNQ’s 2024 Highlights

Building Incident Management at SYNQ

Supercharging data quality: Using a data reliability platform alongside dbt

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

Learnings from running hundreds of data incidents at SYNQ

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

How top data teams are structured

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

How data observability fits into the different stages in the data pipeline

High-impact data governance teams

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

ML & Gen AI for data teams

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

Unpacking Synq’s approach to incident management for data teams

Why incidents – not issues – must be the basis of data reliability SLAs

The cost of data incidents

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

How we built code-column lineage to reimagine the data practitioner’s experience

Building new software engineering-like workflows

Why we’re taking column-level lineage to the next level

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

Data ownership: A practical guide

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

Measuring data quality: bringing theory into practice

Practical steps for measuring and actioning data quality

LendInvest webinar

Learnings from bringing ownership of data quality across the business

2023 recap

A year of growth, learning, and shipping

Building reliable machine learning models in the data warehouse

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

Helping data teams improve reliability of their critical data

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

Building reliable data in fintech

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

Anomaly monitors and dbt tests to ensure the quality of business-critical pipelines

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

The importance of data quality for automated customer engagement

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

The hidden cost of data quality issues on the return of ad spend

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

How to identify your business-critical data

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

Activating ownership with data contracts in dbt

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

Untaming the spaghetti lineage

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

New capabilities to manage data reliability at scale

The struggles scaling data teams face

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

Data and product to engineer ratio at 50 tech scaleups

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

Incident management for data teams

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

Data alerts are hard

The difficulty of building alerting workflows fit for scale

Analytics engineering workflows with dbt and Synq

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

Europe data salary benchmark 2023

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

The complete guide to building reliable data with dbt tests

10 practical steps from top data teams

Designing severity levels for data issues

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

Defining ownership and making it actionable

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

The unique challenges faced by fintech data teams

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

Data team as % of workforce: A deep dive into 100 tech scaleups

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