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10 Data Profiling Tools Every Developer Must Know

10 Data Profiling Tools Every Developer Must Know

Srishti Deoras
W3Schools

Data Profiling is one of the most crucial and the first step in data quality assessment. It involves the evaluation of data values within a given set of data for uniqueness, consistency and logic, which are the three key quality metrics. As described in our previous article, it differs from data mining, which is a process of identifying patterns in a pre-built database. 

Here we list 10 data profiling tools that you can use to bring about data quality and accurate assessment of your structured, unstructured or real-time data. 

The list is in chronological order and includes both paid and free tools. 



1| Aggregate Profiler

An open-source data quality and data profiling tool, Aggregate Profiler carries out data profiling and analysis in file formats such as RDBMS, flat files, XML and XLS. Aggregate Profiler tools can be used for data quality check, corrections and profiling as it can perform cardinality checks between different tables within one data source. It can also be used for random generation of data, populating database values, looking into database metadata, and more. Some of the other tasks it can carry out are metadata discovery, anomaly detection, basket analytics, similarity checks and more.

2| Atlan

An autonomous data profiling tool, Atlan provides a modem data profiling solution. With an auto-generated profile, crowdsourced metadata, data dictionary and README editor, the tool offers a solution for every need at one place. It can do data profiling from a variety of different sources and connect with the software you choose. It is one of the most accessible data profiling tools to get the data quality corrected in the required context and format while facilitating BI software integrations, native Excel plugins and more. 

3| IBM InfoSphere Information Analyser

This data profiling tool by IBM helps in identifying the data quality, content and structure. It can carry out various profiling functions such as column analysis, primary key analysis, natural key analysis, cross-domain analysis, natural key analysis and more. It works perfectly for big data, business intelligence, data warehousing, data management and more while analysing different kinds of formats. It also has machine learning capabilities that can auto-tag data and identify potential issues. It offers more than 200 built-in data quality rules that control the ingestion of bad data.

4| Informatica Data Explorer

This tool provides data profiling and data quality solutions allowing developers to carry out a faster and thorough analysis of data in the repository. Available in standard and advanced modes, it can scan every single data record from any source to detect anomalies and hidden relationships. It can work on highly complex datasets with much ease and find connections between data sources. This tool also comes with pre-built rules that can be applied to the data for profiling. It supports all types of structured and unstructured data. 

5| Melissa Data Profiler

It can carry out functions such as data profiling, data enrichment, data matching and data verification. Relatively easy-to-use, this tool can get all kinds of data analysed efficiently while carrying out general formatting, content analysis and more. Its profiling capabilities help to check data before it arrives into the data warehouse and helps ensure consistency and quality of data. It can carry out tasks such as identification and extraction of data, monitoring data quality process and more. It can enhance data governance, create a metadata repository, maintain data standardisation, and convenient management of data. 

6| Microsoft DOCS

The Data Profiling task by Microsoft DOCS provides functionality such as data extractions, transformation and loading data. It allows for an efficient analysis of source data while understanding it better and preventing data quality problems before they are introduced into the data warehouse. With built-in features such as reading broad data types, it does an efficient job of ensuring data quality. 

7| SAP BODS

Business Objects Data Services (BODS) is one of the best and popular data profiling tools to carry out analysis of inconsistencies in data and other data problems. It provides features such as data quality monitoring, metadata management, and data profiling in one package. It can undergo redundancy, sparseness, pattern distribution, analyse cross-system data dependencies, among others. The standard profiling using BODS allows an understanding of the unique values present in the columns, whereas relationship profiling carries out detailed profiling.

8| SAS DataFlux

DataFlux combines data quality, data integration and master data management. It provides high-performance environments to create and explore a data profile, design data standardisation schemes, and more. It can extract data, profile it, standardise it, monitor it and verify the data in a significantly faster and secured way. It ensures high-quality data in every business process.

9| Talend Open Studio

A free downloadable tool, Talend Open Studio offers deep visibility into organisations’ data. It is a flexible tool which can carry data quality analysis of different types of fields, databases and file types. This is one of the best free data profiling tools that offers a sophisticated framework that includes pre-built connectors and monitoring tools. The tool can address data deduplication, validation and standardisation. 

10| TIBCO Clarity

Essentially a data cleansing tool, it provides a data profiling function to check and collect statistics and information about data by generating row or column analysis reports. It can efficiently work on large volumes of data to analyse them accurately for data quality. The tool is also efficient in validating, standardising, transforming, addressing deduplication, cleansing and visualising all major data sources and file types. The application supports strong editing functions that let users manage columns, cells and tables, apart from allowing users to analyse and regroup data according to numerous criteria.

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