Data quality management tool for databases

Data Quality Management

  • Business rule validation
  • ETL & data pipeline testing
  • Regression & unit testing
  • Data profiling & anomaly detection
  • Data reconciliation


"All-in-one data quality solution for our Vertica data warehouse and its source databases."
Starship Technologies
"Trust is hard to earn and easy to lose. LiTech makes it easier to earn and harder to lose."
"Convenient and systematic way to set up and manage your data quality queries."
Registrite ja Infosüsteemide Keskus
"An enthusiastic and motivated team to listen to the client's ideas and wishes."
Holm Bank
"Flexible and quick solution to discover and analyze data quality issues. The application is easy to install and use. Professional and friendly client support. Overall a positive experience with the pilot project."
Up to 50% of data practitioner's time is spent on data quality issues

Save time

Creating test cases manually and sampling data row by row are not efficient or reliable methods for testing large datasets. Reduce manual effort and save time by automating data testing.

Detect errors

The use of continuous integration in development and constant change in data requires systematic testing and monitoring to detect errors and ensure data quality.

Avoid delays

Poor data often means business reports are not reliable and additional development is required. Our tool helps you to detect errors in data preventing negative impact on customers and delays in business.

Features and attributes

Supported data sources

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  • Oracle
  • PostgreSQL
  • SQL Server
  • MySQL
  • MariaDB
  • Redshift
  • Vertica
  • Teradata
  • Snowflake
  • BigQuery
  • SAP IQ
  • SAP Hana
  • Cassandra
  • Databricks
  • RESTful API
  • CSV files

Data source comparison

Compare datasets from different data sources by comparing rows one-to-one using our built-in compare engine. This allows users to check millions of rows to find errors caused by problems in ETL. Our DQM also allows to compare aggregated values from both sides to check for inequalities in data.

Automated test creation

Automatically generate test cases using reusable dynamic rules. Generated tests are automatically changed in the background to be in accordance with datasource metadata. This means adding or removing columns from a view or table keeps the test cases up to date and checks for errors in new columns. LiTech DQM allows users to generate tests in bulk to save time and reduce manual effort.

Data profiling

Detect sudden changes, deviations and variations in data by systematically profiling objects and analysing profiled data. Use machine learning to predict changes in values, detect possible errors and find anomalies.

Anomaly detection

Automatically analyse test execution results to learn how your aggregated data changes over time. Find errors in dataset by predicting next execution result and comparing it with actual result within error threshold.

Test scheduling

Schedule test suites and profiling rules to automatically execute and send alerts when errors in data are found. Executions can be scheduled to run daily/weekly/monthly or on selected days/custom time. Test suite executions can also trigger SQLs or API endpoints.


The tool’s API can be used to execute test suites, get test results and request other data from DQM. This allows simple integration with external tools to automate data quality management in data pipeline.

Automatic alerts

LiTech DQM sends automatic alerts by e-mail or using webhooks (Slack, Teams, Jira etc) if error threshold is exceeded on automated executions. Sending alerts is integrated into DQM’s scheduler and API to notify responsible users as soon as data errors have occurred.

Role based users

Our DQM uses built-in user management system which also integrates with LDAP/Active Directory and OAuth2. Roles can be created and assigned to only allow users access to specific databases and assign other privileges within the tool.

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