Data Validation Tools for Accurate Business Insights

Anomalo offers data validation down to the row level for your most important tables, with an easy-to-use UI for adding checks and rich visualizations for triaging issues.

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Enforce data integrity with less effort

Automated data quality checks assess accuracy, completeness, consistency, and integrity — without manual setup.

• Detect increases in NULL, zero, or duplicate values
• Pinpoint drops in segment records
• Ensure new data is on time and complete

Row-level validation when and where you need it

Easily define checks that ensure data in your key tables is 100% accurate and complies with strict thresholds.

• No-code interface for non-technical users
• Powerful customization with SQL and advanced configuration options
• Migrate your preexisting rules with our API

Integrate with ETL tools to test data pipelines

Validate your data-in-motion to stop pipelines from introducing inaccuracies.

• Plug Anomalo into your DAGs and workflows
• Built-in integrations with Airflow and dbt
• Rich API and Webhook support

Key Benefits

Data Validation Tools

Automated checks

Cover a wide range of common data quality issues with our built-in validation checks. Simply turn on data quality monitoring for a table and get notified when data is late, incomplete, missing, or anomalous.

Customizable validation rules

We understand how complex your data needs can be, so we offer over 40 kinds of no-code validation checks, plus infinite flexibility in SQL. If you have an edge case, we have an advanced configuration option for it.

Built-in root cause analysis

Go deeper than pass/fail. Anomalo’s visualizations and tools, such as samples of good and bad rows, help you quickly understand the severity of an issue and find the cause of the problem.

Data profiling

Generate a visual profile of your table’s columns, expected values, and historical changes. You can get even more insights by monitoring metrics about your data and its key segments.

Frequently Asked Questions

How often does Anomalo run data validation checks?

Anomalo checks to see if new data has arrived on a daily basis, starting at midnight. After the data freshness check passes, Anomalo’s data volume check runs every hour to see if the data is completely loaded into the table. Then, all other checks are run asynchronously after the data volume check passes.

What types of data validation tests does Anomalo perform?

Anomalo offers a variety of data validation testing techniques. We provide four categories of checks in the data validation process:

• Data freshness: Checks if new data has arrived on time
• Data volume: Checks if new data is complete
• Missing data: Looks for drops in segment records, increases in NULL values, or increases in zero values
• Table anomalies: Runs Anomalo’s AI-based anomaly detection to find anomalous records, and also checks for increases in duplicate data for columns that previously contained unique records

How customizable are the validation rules within Anomalo?

Very customizable! If you need to set up a rule for validating data in Anomalo, you can use our no-code UI or integrate with our API. Within the no-code UI, there are over 40 checks that each allow you to specify a range of thresholds, options, and constraints.

Does Anomalo support integration with different data sources?

Anomalo offers one-click integrations with all major data warehouses/lakes/lakehouses. You can also run Anomalo’s checks as part of your ETL pipeline via our integrations with orchestration tools like Airflow and dbt.

What reporting and visualization features does Anomalo provide?

Anomalo offers dozens of visualizations to bring you more insight into why a check might be passing or failing, as well as a high-level executive dashboard that visually tracks data quality progress and highlights problem areas across your entire data warehouse.

What algorithms does Anomalo utilize for anomaly detection?

Anomalo uses unsupervised anomaly detection techniques. Taking on a sample of 10,000 records from the most recent data and comparison samples from previous days, our machine learning algorithms will search for ways that the existing dataset is different from those previous days.

How much time do Anomalo’s models need to learn about my data?

Anomalo provides an automated and repeatable solution to validate data entry and monitor data movement processes. The machine learning algorithms need to run for 2 weeks to produce useful results. Results will continue to improve over the next 30–60 days.

How does Anomalo’s root cause analysis help me understand anomalous data points?

With automated root cause analysis, you can download a sample of a “good” and “bad” data point every time Anomalo flags an issue. Anomalo also will highlight the columns and data segments that contain the largest portions of anomalous data points, compared to normal data.

Ready to Trust Your Data? Let’s Get Started

Meet with our team to see how Anomalo transforms data quality from a challenge into a competitive edge.