SQL Server
Oracle
MySQL
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Amazon Redshift
Azure SQL Database
DBT
Google Big Query
IBM DB2
MariaDB
SAP HANA
Snowflake
SQLite
Teradata
Vertica
Data profiling tools for Qlik View
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.
Talend Data Fabric
Talend Data Fabric combines data integration, integrity, and governance in a single, unified platform. Talend Data Fabric's capabilities allow you to extract, process, and profile data from virtually any source to your data warehouse. Data profiling lets you quickly identify data quality issues, discover hidden patterns, and spot anomalies through summary statistics and graphical representations.
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| Commercial: | Commercial |
| Desktop/Cloud: | Desktop |
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| Runs on: (for desktop): | Mac OS,Windows |
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| Statistics of data: | - |
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Informatica Data Profiling
Informatica’s data profiling solution, Data Explorer, is available in two editions—Standard and Advanced—that employ powerful data profiling capabilities to scan every single data record, from any source, to find anomalies and hidden relationships. It works regardless of complexity or of the relationship between your data sources.
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| Commercial: | Commercial |
| Desktop/Cloud: | Cloud |
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| Runs on: (for desktop): | - |
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| Statistics of data: | Avg,Max,Min,Stdev |
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DQLabs
DQLabs platform has a data profiling platform that is AI-driven and accepts data from multiple sources in different formats if necessary. The user interface is user-friendly and will allow the user to track the data profiling process and make adjustments where they feel it’s necessary. The platform algorithms will detect deep insight into the source data and increase the quality of the profiled data.
| Access control: |
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| Commercial: | Commercial |
| Desktop/Cloud: | Cloud |
| Excel workbooks: |
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| Flat files: |
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| Free edition: |
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| Metadata identification: |
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| NoSQL sources: |
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| Runs on: (for desktop): | - |
| Sensitive data discovery: |
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| SQL sources: |
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| Statistics of data: | - |
| Tagging 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.