SQL Server
Oracle
MySQL
PostgreSQL
Amazon Redshift
Azure SQL Database
DBT
Google Big Query
IBM DB2
MariaDB
SAP HANA
Snowflake
SQLite
Teradata
Vertica
Data profiling tools for Firebird
Data Profiling tools allow analyzing, monitoring, and reviewing data from existing databases in order to provide critical insights. Data profiling can help organizations improve data quality and decision-making process by identifying problems and addressing them before they arise.
Dataedo
Dataedo is a metadata management & data catalog tool with a data profiling feature. It allows you to use sample data to learn what data is stored in your data assets. You can browse min, max, average and median values, see top values, as well as value and row distribution to understand the data better before using it.
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| Commercial: | Commercial |
| Desktop/Cloud: | Desktop |
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| Runs on: (for desktop): | Windows |
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| Statistics of data: | Avg,Max,Min,Stdev |
| Tagging data: | - |
DataCleaner
The heart of DataCleaner is a strong data profiling engine for discovering and analyzing the quality of your data. Find the patterns, missing values, character sets and other characteristics of your data values.
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| Commercial: | Free |
| Desktop/Cloud: | Desktop |
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| Runs on: (for desktop): | Linux,Mac OS,Windows |
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| Statistics of data: | - |
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Alation Data Catalog
Alation’s data profiling capabilities help reduce the time spent in the data exploration phase. With Alation’s data profile, data consumers have the metrics they need to easily discern the quality of any data object. Alation displays important characteristics, statistics, and numerical graphs about the data — enabling data scientists and data engineers to quickly take action. The data profiling now also includes new charts and customizations.
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| Commercial: | Commercial |
| Desktop/Cloud: | Cloud |
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| Runs on: (for desktop): | - |
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| Statistics of data: | - |
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The use of data profiling tools can lead to higher-quality, more reliable data or eliminating errors that add costs to data-driven projects. Eliminating these costly errors involve processes such as:
• Collecting descriptive statistics.
• Collecting data types, length and recurring patterns.
• Tagging data with keywords, descriptions or categories.
• Performing data quality assessment.
• Discovering metadata and assessing its accuracy.
The most efficient way of handling the data profiling process is to automate it with a data management solution. We prepared a list of open-source data profiling tools that help you carry out the analysis of your data and identify the issues.