Stepping into a data leadership role? Learn key strategies for the first 90 days to establish your team as a value center and drive business impact.

First 90 days as a data leader: Establishing data teams as value centres

Prevent stale dashboards and hidden data errors with data observability. Learn how automated detection, incident management, and data lineage help teams maintain trust in data-driven decisions.

Sneaky data quality issues, chaotic aftermath

In this article, we’ll explore Looker’s alerting limitations and how Validio fills the gaps—so you can move beyond just dashboards and into proactive, intelligent data monitoring.

Amplifying Looker dashboards with machine learning based data observability alerts

When dashboards aren't enough: amplifying Looker with machine learning based alerts

The first step to any successful data observability initiative is to set up the right validations. In our 4.5 release, we’ve made several updates to the workflow for setting up validators to make this even easier.

Feature update: Easier validator setup for improved data quality

Introducing Segment Filtering by Validio – the smart solution for data anomaly detection. Save time and enhance accuracy in targeting critical data segments with customizable filters, ensuring no anomalies slip through the cracks. Get precise insights, effortlessly.

Simplify your data monitoring with Validio's new Segment Filtering feature

Introducing Segment Filtering: Effortless application of custom SQL rules across datasets

Explore how Validio's precise anomaly detection stands out against Snowflake, BigQuery, and Databricks for improved data quality.

How Validio's anomaly detection compares to Snowflake, BigQuery, and Databricks

Effortless Anomaly Detection: How Validio Compares to Snowflake, BigQuery, and Databricks

Elevate your workflow with notification muting. Focus on your data tasks without distraction. Set alerts on your terms and never miss what matters.

Avoid alert fatigue with notification muting

Benjamin Franklin once said, “When the well's dry, we know the worth of water.” It’s a quote that sums up the world of data engineering pretty well.

Timestamps: small data engineering issues with massive impact

Validio introduces role-based access control to securely manage access and permissions for improved data quality

Introducing role-based access control in Validio

Detect anomalies anywhere in your data, in real time

Get to the root cause and resolve issues quickly

Discover data assets and understand how they are used

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Dema uses Validio to ensure the data quality for their prescriptive analytics

Sophia Granfors

Marketing Manager

The Validio 4.5 release includes updates to incident commenting, inherited ownership and an updated validator workflow - check the changelog for all details. For this blog post, we’ll dive deeper into the updated validator workflow. In short, this is what we’ve added:

Use case based validator setup: The validator wizard has been simplified and redesigned to be based around data quality use cases and categories of validations, such as pipeline health, data consistency, and completeness.

Duplicate and iterate on existing validators: Easily edit already configured validators, or use existing validators as a template for new data observability validators without having to recreate all of the work.

Editable debug query: Expand and edit data issue debug queries directly to make root-cause analysis easier.

Basic validator setup: Set up basic data quality validations like null filters and referential integrity checks without having to write everything with custom SQL.

If you’re ready to get started, this is all rolled out in the latest release. Or, read on for a deep dive on how we’ve made the validator workflow better.

When setting up monitoring for your data there’s generally two angles to consider: technical validations, such as null%, row count and freshness, and business validations, looking at the actual value of data. A good starting point is to think about your validator setup in terms of different use cases. To simplify this, we’ve restructured the library of Validio validators to align with this. Now, you can easily browse validators based on categories like pipeline health, data consistency, and completeness and set up the validators to support your needs.

We know things can change, and so can validators. That’s why we’ve made it possible to edit, duplicate, and iterate on existing validators. In addition to using this as a way to edit existing validators, you can also save yourself double work by using existing validators as a template when setting up new ones. Simply click a validator to open a new wizard, primed with the validator configuration. Edit based on the desired new validator and hit save.

Easier root-cause analysis with editable debug query

We’ve made the debug query editable, so you can easily pull in more information when doing root-cause analysis. For example, if you are monitoring a ratio of something you might want to be able to look at both the numerator and denominator to see how it behaves and now you can do that for better root cause analysis.

Sometimes you just want the simple data observability checks. Previously, anyone comfortable with writing custom SQL could easily do that, but with data consumers increasingly wanting to take control of data quality we needed to make this more straightforward. Now, you can set up “basic” validator use cases like null filters and referential integrity checks without having to write everything with custom SQL.

A guide to setting up your first Data Quality and Observability rules

Curious about automating your data observability?

All things data trust operational use cases, and abstracting complexity away from data engineering.

Stay on top of your data and metrics with automated monitoring, to boost data team productivity and make your data AI-ready.

Feature update: Easier validator setup for improved data quality

Discover the latest news and data trends

First 90 days as a data leader: Establishing data teams as value centres

Sneaky data quality issues, chaotic aftermath

When dashboards aren't enough: amplifying Looker with machine learning based alerts

Feature update: Easier validator setup for improved data quality

Introducing Segment Filtering: Effortless application of custom SQL rules across datasets

Effortless Anomaly Detection: How Validio Compares to Snowflake, BigQuery, and Databricks

Introducing notification muting

Timestamps: small data engineering issues with massive impact

Introducing Role-Based Access Control