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DataCleaner

datacleaner.github.io

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Spec score 8.9 #2 of 31 in Data Cleansing Software

What it is

DataCleaner is an open-source data quality application for profiling, validating, and cleansing data. Its profiling engine identifies patterns, missing values, character sets, and other traits in data values. It works with CSV files, Excel spreadsheets, relational databases, and NoSQL databases. Users can create cleansing rules with search and replace, regular expressions, pattern matching, or custom transformations, and use internal or external reference data to check values. DataCleaner supports batch processing, visual workflows, standardization, validation, enrichment, scheduled runs, and desktop deployment on Linux, macOS, and Windows. Named integrations and connectivity options include Apache Hadoop, Apache Spark, Pentaho Data Integration, and Apache MetaModel. Developers can embed it in other applications or build plugins; the project also describes community extensions, integrations, and shared content. The code is licensed under LGPL. The community edition is free at free. The downloads page lists 5.9.0 as the latest release; release news says version 5.8.1 runs on Java 9 through 17 and is dated February 9, 2022.

Spec sheet

What it does
DataCleaner is a toolkit for profiling, correcting and enriching data, including ad-hoc analysis, recurring cleansing, matching and master-data-management work.Read 15 Oct 2026 · github.com
Purpose
DataCleaner is an open source data quality solution with a data profiling engine for discovering and analyzing data quality.Read 30 Sep 2026 · datacleaner.github.io
Integrations
The project advertises Apache Hadoop, Apache Spark, Pentaho Data Integration and Apache MetaModel integrations.Read 15 Oct 2026 · datacleaner.github.io
License
The site states that the code is licensed under the Lesser General Public License (LGPL).Read 30 Sep 2026 · datacleaner.github.io
Analyzers
Reference documentation lists completeness, character-set, date/time, number, pattern, referential-integrity, uniqueness, value-distribution and machine-learning analyzers.Read 15 Oct 2026 · datacleaner.github.io
Cleansing
Users can build cleansing rules using search and replace, regular expressions, pattern matching, or custom transformations.Read 30 Sep 2026 · datacleaner.github.io
Code execution
Developer documentation explains how to configure, build, execute and retrieve results from DataCleaner jobs through code.Read 15 Oct 2026 · datacleaner.github.io
Command line
The reference manual documents command-line execution, parameterized jobs and runtime configuration overrides.Read 15 Oct 2026 · datacleaner.github.io
Company ownership history
The survey states that DataCleaner was developed by Human Inference, later moved through Neopost and Quadient, and from 2019 was associated with EDM Media Group.Read 15 Oct 2026 · frontiersin.org
Data profiling
Its profiling engine identifies patterns, missing values, character sets and other characteristics of data.Read 15 Oct 2026 · datacleaner.github.io
Data sources
It handles CSV files, Excel spreadsheets, relational databases, and NoSQL databases.Read 30 Sep 2026 · datacleaner.github.io
Data wrangling
The application supports cleansing workflows using reference data, search-and-replace rules, regular expressions, pattern matching and custom transformations.Read 15 Oct 2026 · datacleaner.github.io
Datastore connectors
Documentation covers JDBC databases, CSV, fixed-width files, Excel, XML, Elasticsearch, MongoDB and CouchDB datastores.Read 15 Oct 2026 · datacleaner.github.io
Deployment model
Downloads are supplied as desktop archives for Windows, macOS and Linux, with source tags also available.Read 15 Oct 2026 · datacleaner.github.io
Discussion and support
Community discussion posts are powered by GitHub issues and use the Discussion or Question label.Read 30 Sep 2026 · datacleaner.github.io
Documentation depth
The documentation archive includes reference versions 5.7.0, 5.4.0 and 5.2, covering end-user, configuration, CLI, integration and developer topics.Read 15 Oct 2026 · datacleaner.github.io
Ecosystem
The project describes community driven extensions, integrations, and shared content.Read 30 Sep 2026 · datacleaner.github.io
Extensibility
The developer guide covers extension tutorials, packaging, annotated components, single-JAR extensions and embedding DataCleaner.Read 15 Oct 2026 · datacleaner.github.io
File and database inputs
The official site says it handles CSV, Excel, relational databases and NoSQL databases.Read 15 Oct 2026 · datacleaner.github.io
Java compatibility
The 5.8.1 release announcement says the software runs on Java versions 9 through 17.Read 15 Oct 2026 · datacleaner.github.io
Java support
The release news states DataCleaner 5.8.1 runs on Java versions 9 through 17.Read 30 Sep 2026 · datacleaner.github.io
Latest listed release
The downloads page identifies DataCleaner community edition 5.9.0 as the latest release.Read 15 Oct 2026 · datacleaner.github.io
Licence
The community edition code is licensed under the GNU Lesser General Public License.Read 15 Oct 2026 · github.com
Limit: independent review coverage
SoftwareReviews lists only 3 reviews and marks the product as having insufficient data for results.Read 15 Oct 2026 · infotech.com
Limit: scope expectations
The same survey notes that users might expect more pure cleansing functionality from a product named DataCleaner.Read 15 Oct 2026 · frontiersin.org
Limit: SourceForge support
SourceForge's category scores rate support at 3 out of 5, below ease, features and design.Read 15 Oct 2026 · sourceforge.net
Maintenance status
The repository says the project is maintained for modern Java and security fixes but is not actively developed; new features come through pull requests.Read 15 Oct 2026 · github.com
Maker headquarters
LinkedIn lists Human Inference as headquartered in Arnhem, Gelderland, Netherlands.Read 15 Oct 2026 · de.linkedin.com
Maker size
LinkedIn places Human Inference in the 11–50 employee category.Read 15 Oct 2026 · de.linkedin.com
Notable news
The project news page records Human Inference's acquisition by EDM on February 11, 2019, while stating that the DataCleaner community remained open-source focused.Read 15 Oct 2026 · datacleaner.github.io
Praise: independent usability assessment
A peer-reviewed survey reports that DataCleaner's interface was intuitive even though it targets technical users.Read 15 Oct 2026 · frontiersin.org
Praise: SourceForge users
SourceForge reviewers describe it as very good for data profiling and data cleansing, with another short positive review.Read 15 Oct 2026 · sourceforge.net
Profiling
Its profiling engine finds patterns, missing values, character sets, and other characteristics of data values.Read 30 Sep 2026 · datacleaner.github.io
Public API
The GitHub repository describes an api module containing public interfaces and annotations for building extensions.Read 15 Oct 2026 · github.com
Rating on G2
0.0 out of 5 from 0 reviews; G2 says there are too few reviews to provide buying insight.Read 15 Oct 2026 · g2.com
Rating on SourceForge
4.7 out of 5 from 3 reviews.Read 15 Oct 2026 · sourceforge.net
Reference data
It can use internal or external reference data to verify data values against the real world.Read 30 Sep 2026 · datacleaner.github.io
Release activity
The news page lists the DataCleaner 5.8.1 release dated February 9, 2022.Read 30 Sep 2026 · datacleaner.github.io
Security maintenance
The maintainers state that they update dependencies when vulnerabilities are detected and take security bugs seriously.Read 15 Oct 2026 · github.com
Transformers and outputs
Documented components include JavaScript and lookup transformations, with output writers for CSV, Excel, staging tables and database tables.Read 15 Oct 2026 · datacleaner.github.io

Plans and prices

Community edition Free Open-source desktop toolkit · profiling, cleansing and enrichment · Windows, macOS and Linux downloads Read 15 Oct 2026 · datacleaner.github.io
DataCleaner community edition Free Open source · LGPL Read 30 Sep 2026 · datacleaner.github.io

Every DataCleaner plan and price →

Strong points and limits

Strong points

  • Profiles patterns, missing values, and character sets
  • Works with CSV, Excel, relational, and NoSQL data
  • Supports custom cleansing rules and reference data
  • Offers visual workflows and scheduled batch runs
  • Free community edition licensed under LGPL

Limits

  • Desktop deployment only
  • Latest listed release is community edition 5.9.0

Who it suits

DataCleaner suits teams that need to profile and clean data from files or databases, and developers who want to embed data-quality tools or add plugins. It is available for Linux, macOS, and Windows.

DataCleaner in depth

Read the full write-up

DataCleaner is an open-source desktop application for profiling, validating, and cleansing data across files and databases. It suits teams and developers who need rule-based, batch-oriented quality work with room for custom extensions. Its no-cost community edition is compelling for broad data-source coverage, but less suitable when cloud delivery or a fast release cadence is essential.

Overview

DataCleaner brings investigation and remediation into one workflow. The profiling engine exposes patterns, missing values, character sets, and other value characteristics, helping users see where quality problems are concentrated before writing transformation rules.

The project is licensed under the LGPL, which supports reuse and embedding while preserving an open-source foundation. Its ecosystem emphasizes community extensions, integrations, and shared content. Connectivity includes Apache Hadoop, Apache Spark, Pentaho Data Integration, and Apache MetaModel, making it more relevant to mixed data stacks than a tool restricted to local spreadsheets.

Release information points to community edition 5.9.0 as the newest download, with release news recording version 5.8.1 on February 9, 2022. Teams that require a visibly rapid update rhythm should weigh that history before standardizing on it. Community discussion runs through GitHub issues using Discussion or Question labels, so support is oriented toward public project interaction rather than a commercial service desk.

Key features

Source coverage spans CSV, Excel, relational databases, and NoSQL databases. That combination is useful for analysts consolidating files with operational stores, and for developers building a repeatable quality process across heterogeneous inputs.

Cleaning rules can use search and replace, regular expressions, pattern matching, or custom transformations. This gives straightforward fixes a quick path while allowing more precise logic when values need context-specific handling. Standardization rules, validation, and enrichment are supported, so the product can normalize values, check them, and add information rather than merely report defects.

Reference data may be internal or external, letting users verify values against real-world reference sets. Visual workflows make batch processing easier to map, while scheduled runs suit recurring jobs. The processing model is batch, not an interactive streaming service, so it fits periodic pipelines better than continuously changing data feeds.

Developers can embed DataCleaner in other applications and create plugins for specific use cases. That extensibility is a meaningful advantage for engineering teams with bespoke rules, though it also makes the product a stronger fit for organizations prepared to manage their own implementation work.

Pricing

DataCleaner community edition

The community edition costs nothing and is open source under the LGPL. It includes the desktop product and its profiling, cleansing, validation, enrichment, visual-workflow, scheduling, and batch capabilities. With no seat charge or usage quota stated, it is a practical starting point for individual practitioners, development teams, and organizations evaluating a quality workflow without a software budget.

There is no paid tier described in the available plans, so buyers seeking contracted support, enterprise service terms, or a separately packaged hosted edition should look elsewhere. The free plan also does not change the desktop deployment model.

Platforms

DataCleaner runs on Linux, macOS, and Windows. Deployment is desktop-based, which works for local or controlled batch operations but will not by itself provide a browser-first or managed cloud workspace. Release 5.8.1 supports Java versions 9 through 17, an important compatibility consideration when selecting a runtime for existing environments.

Who it's for

Choose DataCleaner for data-quality teams handling a mixture of spreadsheets, flat files, relational systems, and NoSQL sources; for developers who need embeddability or plugins; and for organizations that prefer an LGPL-licensed, no-cost tool for scheduled batch jobs.

It is a weaker fit for teams that need cloud-native collaboration, continuous processing, or a commercial support contract. Organizations prioritizing frequent releases should also examine the recorded release cadence before committing.

Pros and cons

Pros

Broad source support: CSV, Excel, relational, and NoSQL inputs cover common mixed-data estates.

Deep rule options: Regular expressions, pattern matching, search and replace, and custom transformations handle both routine and specialized cleanup.

Beyond cleansing: Profiling, standardization, validation, enrichment, reference-data checks, and scheduling support a fuller quality cycle.

Extensible foundation: LGPL licensing, embeddability, and plugin support suit teams building tailored workflows.

No license cost: The community edition is free for organizations starting or scaling batch quality work.

Cons

Desktop-only deployment: It does not address teams seeking a hosted, browser-first service.

Batch processing: Continuous or streaming quality requirements need another approach.

Release-cadence concern: The dated 5.8.1 news entry may trouble buyers who depend on frequent updates.

Community-led support: Discussion through GitHub issues is less predictable than a vendor support agreement.

Alternatives

OpenRefine is the closest free, open-source desktop alternative when transformation work and a BSD 3-clause license are preferred.

Pick Melissa Data Quality instead when address verification, API access, web delivery, or a free allowance of 250 records per month matters.

BlueData suits a small web-and-mobile workflow, with its freemium plan limited to one web user, one mobile user, 1 GB of storage, and 500 submissions per month.

Choose Plauti Deduplicate for Salesforce-focused deduplication and API, self-hosted, or web deployment.

Firstlogic Data Quality IQ Suite is aimed at enterprise deployments with yearly licensing and unlimited records.

Infosys Data Workbench is a paid option spanning Linux, web, and Windows.

For enterprise statistical modeling on SAS Viya, consider SAS Visual Statistics.

DataMatch Enterprise targets enterprise-scale cleansing and fuzzy matching across millions of records.

Browse Data Cleansing Software and Data Preparation Software for broader category comparisons.

Verdict

DataCleaner is a strong choice for technically capable users who want free, extensible, desktop batch processing across varied data sources. Its main reasons to choose it are the breadth of profiling and rule-based quality features, LGPL foundation, and plugin and embedding options. Look elsewhere if you need cloud-first deployment, streaming workflows, contracted support, or confidence in a more active release cycle.

Verdict

DataCleaner brings profiling, cleansing, validation, and enrichment into a free desktop application. Its data-source coverage and extensibility are useful, while desktop deployment is the listed deployment option.

Where Specifiction places it

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