Data quality tools for MariaDB
Data quality tools  measure how good and useful a data set is to serve its intended purpose. High quality data can lead to better decision-making and faster insights as well as reduce the costs of identifying and dealing with bad data in the system. It can also save time and allow companies to focus on more important tasks. 
Dataedo
Dataedo provides users with a comprehensive tool for maintaining, tracking, and improving data quality. Access selection of 150+ built-in data quality rules or define bespoke data quality rules using SQL queries. Schedule data quality checks for continuous monitoring. Use a DQ dashboard for quick issue detection and trend analysis.
| Commercial: | Commercial | 
|---|---|
| Data cleansing: |  | 
| Data Discovery & Search: |  | 
| Data Profiling: |  | 
| Data standarization: |  | 
| Free edition: |  | 
Ataccama ONE
Ataccama ONE offers self-driving data quality management by letting you quickly understand the state of your data, validate & improve it, prevent bad data from entering your systems, and continuously monitor data quality.
| Commercial: | Commercial | 
|---|---|
| Data cleansing: |  | 
| Data Discovery & Search: |  | 
| Data Profiling: |  | 
| Data standarization: |  | 
| Free edition: |  | 
Atlan
Atlan uses DataOps to create a new paradigm for building trust in your data. It auto-generates data quality profiles which make detecting bad data dead easy. From automatic variable type detection & frequency distribution to missing values and outlier detection, Atlan offers everything.
| Commercial: | Commercial | 
|---|---|
| Data cleansing: |  | 
| Data Discovery & Search: |  | 
| Data Profiling: |  | 
| Data standarization: |  | 
| Free edition: |  | 
Data quality tools are the scripts that support the data quality processes and they heavily rely on identification, understanding, and correction of data errors. Data quality tool enhances the accuracy of the data and helps to ensure good data governance all across the data-driven cycle. 
The common functions that each data quality tools must perform are: 
• Data profiling 
• Data monitoring 
• Parsing 
• Standardization 
• Data enrichment 
• Data cleansing 
Choosing the right data quality tool is essential and impacts the final results. To help you with the right selection, we prepared a list of tools that will assist you with maintaining a high level of data quality. 
 Amazon Redshift
                                
                                Amazon Redshift
                             Azure SQL Database
                                
                                Azure SQL Database
                             DBT
                                
                                DBT
                             Google Big Query
                                
                                Google Big Query
                             IBM DB2
                                
                                IBM DB2
                             MariaDB
                                
                                MariaDB
                             SAP HANA
                                
                                SAP HANA
                             Snowflake
                                
                                Snowflake
                             SQLite
                                
                                SQLite
                             Teradata
                                
                                Teradata
                             
                 
                 
                 
                 
                 
                 
                