12 Open Source Data Profiling Tools for Software Developers

What is Data Profiling?

Data Profiling is the process of reviewing, examining, analyzing, and creating useful summaries of the data. This is an important tool that is used by businesses and organizations to make better data decisions. It is usually carried out by using statistical analysis in which the software draws conclusions about the content and its quality and determines whether the said data meets business standards.

In layman’s terms, data profiling helps us discover, understand and organize our data. And it is important for a business and organization to be able to handle their data. It involves collecting data types, length, and their recurring patterns; performing data quality assessment, discovering metadata and assessing its accuracy, performing inter-table analysis, etc.

Why Data Profiling?

Data profiling provides a lot of information about the quality and utility of a data set which requires very little human effort. It tells about what information is contained in which data set so that you can decide whether or not to retain it. It also talks about the amount of data cleansing effort that will be needed to merge one data set with another data set.

Following are some open source data profiling tools:

1. Aggregate Profiler

This open-source tool performs data profiling and analysis in file formats such as RDBMS, XLS, etc. This tool can also be used for data filtering, quality checks, enrichment; anomaly detection, looking into metadata, similarity checks, etc.

2. IBM Infosphere Information Analyzer

A data profiling tool by IBM helps in recognizing the data quality, content, and structure. It can carry out different profiling actions such as:

Column analysis: Also called the combination of columns is utilized to recognize details in each column of every source table

Primary key analysis: This function is used to recognize the columns that are qualified to be the primary key

Natural key analysis: This key allows you to uniquely identify distinct values in the column such as social security numbers. You can also review the natural key analysis results too.

Foreign key analysis: Foreign keys act as cross-reference keys. They are columns that point out the primary keys of another table.

Cross-domain analysis: The job of this key is to identify columns that have a common value. It does so by examining the content and its relationships across the tables.

3. Informatica Data Profiler

This data profiling tool helps find the data issues even before they become data problems. This is a technique used to analyze the quality, content, and structure of the course data. Its key features include metadata management, data standardization, enrichment, de-duplication, and consolidation; metadata management, etc. This tool uses powerful data profiling capabilities to scan each and every data record from literally any source and quickly finds out the hidden anomalies.

4. SAP BODS

SAP Business Objects Data Services (BODS) is an ETL (Extract-Transform-Data) tool used for delivering business-class solutions for data quality, data profiling, data integration, and data processing. It is used for extracting data from disparate systems, converting data into meaningful information and load it into a data warehouse. The benefits of this tool include analyzing cross-system data dependencies, validating data completeness, redundancy, and pattern distribution, and most importantly analyzing if the data matches the business expectations.

5. Talend Open Studio

Talend Open Studio is a free and leading open-source solution for Data Integration, Data Quality, Data Management, Data Preparation, and Big Data. Some of the features of this tool include time column correlation, fraud pattern detection, a pattern library, analytics with graphical charts, etc. With this tool, one can easily access a broad range of databases, applications, etc., all from one console. And it can address data deduplication, validation, and standardization.

6. Oracle Enterprise Data Quality

This is a data investigation and data quality monitoring tool that enables businesses to assess the quality of their data. Oracle Enterprise Data Quality is a comprehensive data quality platform that meets the most complex data quality requirements. It includes key features such as data profiling, auditing, and dashboards; integration with Oracle Master Data Management, automated match and merge, Parsing and standardization including constructed fields, misfiled data, poorly structured data, and notes fields, address, and product data verification, etc.

7. Melissa Data Profiler

Melissa Data Profiler is used to analyze a table’s data. This tool puts your data under a microscope, as it analyzes data thoroughly and ensures data quality. This tool is used to develop strategies to manage and employ your data. It carries out tasks such as identification and extraction of data and monitoring the data quality process. This software also analyzes a broad variety of data such as contact name, company, industry, address, city, state, etc.

8. Microsoft Docs

This data profiling task by Microsoft Docs provides data profiling functionalities such as extracting, transforming and loading the data. By using this you can analyze and understand the source data as well as prevent the data quality problems that are introduced into a data warehouse. This tool has a built-in feature of reading the broad data types and it also ensures you on the data quality.

9. SAS DataFlux

SAS DataFlux is a data management suite that combines data quality, data integration, and master data management. This tool can integrate and disparate data sets and ensure data quality. It can extract, profile, design, monitor, and verify the data in a significant and faster way.

10. Quadient DataCleaner

Quadient DataCleaner is a cost-effective data quality solution that provides everything we need to analyze, transform and improve the data, leverage quality data as a strategic asset to the organization and ensure on-time budget system up-gradation and implementation. The key features of this tool include data profiling, quality, and wrangling; complete analysis, reference data matching, character set distribution, detection, and merging of duplicates, etc.

11. SQL Server Integration Services (SSIS)

It is part of Microsoft’s SQL database that is useful in handling a wide range of data migration tasks. It is a top ETL tool that acts as a platform for data integration and workflow applications. It is also a fast and flexible data warehousing tool that is used for data extraction, loading, and transformation (ETL) like cleaning, aggregation, merging data, etc. This tool makes it easy to move data from one database to another one. The key features of SSIS include –

• Populating data marts and data warehouse
• Help clean and standardize data
• Building BIO into a data transformation process
• Coordinating data maintenance, processing, or analysis
• Identifying, capturing, and processing data charges
• Automates administrative functions and data loading
• Offering robust error and event handling
• Eliminating the need for hardcore programmers

12. TIBCO Clarity

Initially a data cleansing tool and now a data preparation tool that offers on-demand software services. It’s a data profiling function that checks and collects statistics and information about data by generating row or column analysis reports. TIBCO Clarity can be used to discover, profile, cleanse and standardize raw data collated from disparate sources and provide good quality data for accurate analysis and intelligent decision-making. It is also effective in validating, standardizing, transforming, addressing deduplication, cleansing, and visualizing all major data sources and file types.

Conclusion

Data profiling is the important and first step in data quality. These tools were originally designed to make managing data quality in a simplified manner. And these days there is a wide range of data profiling solutions such as the ETL and business intelligence software that has built-in Data Profiler. And there are also stand-alone data profiling solutions available too.

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