Overview

DataCleaner helps users discover data-quality issues and apply transformations in a single workflow. Its profiling engine identifies patterns, missing values, character sets, and other traits in values, giving teams a way to investigate data before deciding how to clean it.

The project is open source under the LGPL and describes an ecosystem of community extensions, integrations, and shared content. The latest listed download is community edition 5.9.0, while the news page dates release 5.8.1 to February 9, 2022. That gap makes release recency worth weighing for organizations that prioritize frequent updates.

Key features

DataCleaner accepts CSV files, Excel spreadsheets, relational databases, and NoSQL databases. That range is useful when quality work spans spreadsheets and structured data stores, rather than one narrow source type.

After profiling, users can build cleansing rules with search and replace, regular expressions, pattern matching, or custom transformations. Standardization, validation, enrichment, and visual workflows are supported, and processing is batch-oriented. This suits repeatable data-preparation jobs, but not a need for real-time processing.

Reference data can be internal or external, allowing values to be checked against real-world references. The project names Apache Hadoop, Apache Spark, Pentaho Data Integration, and Apache MetaModel as integrations or connectivity options. Developers can also embed the tool in other applications or build plugins for specific use cases, which makes it more adaptable than a fixed desktop-only workflow.

DataCleaner supports scheduled runs and desktop deployment. The release news says version 5.8.1 runs on Java 9 through 17. Community discussion uses GitHub issues labeled Discussion or Question.

Pricing

DataCleaner community edition costs 0.00 USD per free and is open source under the LGPL. It provides the product’s profiling and cleansing capabilities without a paid tier to weigh against a free cap. The license and desktop deployment suit organizations comfortable running and managing open-source software; no separate commercial plan is part of the offer described here.

Platforms

DataCleaner is available for Linux, macOS, and Windows. Its desktop deployment and batch processing make it a fit for local or managed data-quality workflows, rather than a browser-first service.

Who it's for

Choose DataCleaner if you need to profile and cleanse data across spreadsheets, CSV files, relational databases, or NoSQL stores, and value extensibility or reference-data checks. Developers who want to embed data-quality functions or create plugins have a particular reason to consider it. Look elsewhere if you need real-time processing, a cloud-first interface, or a more recently dated release cadence.

Pros and cons

Pros

Broad input coverage: CSV, Excel, relational, and NoSQL support can bring varied data sources into one quality workflow.
Flexible rule building: Search and replace, regex, pattern matching, and custom transformations support different levels of cleansing complexity.
Extensible and free: LGPL licensing, plugins, embedding, and named ecosystem integrations give technical teams room to adapt it without a software fee.

Cons

Batch and desktop focus: It is not the right fit for real-time processing or a cloud-first user experience.
Release recency concern: The news page dates version 5.8.1 to 2022, which may give buyers seeking frequent releases pause.

Alternatives

qsv is another free option for Linux, macOS, web, and Windows. Its free plan limits spreadsheet imports to 1MB and includes free recipes, one CKAN instance, and qsv slice from Flow; consider it if those constraints and its web availability fit better.

EasyMorph offers a free desktop plan for Windows, web, API, and self-hosted environments, with limits of 20 actions and 20 loop iterations per project. It may suit a workflow that fits within those caps.

OpenRefine is free, open source under the BSD 3-clause license, and supports Linux, macOS, self-hosted, web, and Windows use. Choose it if that licensing and platform mix better match your needs.

Frictionless Framework is free, open-source MIT software installed with pip, for Linux, macOS, self-hosted, and Windows environments. It is an option if that installation model suits your workflow.

Amazon SageMaker Autopilot uses pay-as-you-go pricing with no minimum fees or upfront commitments, and is available through API and web. It is a different choice for readers seeking that usage-based model.

Zoho DataPrep has a free plan capped at 20,000 rows per month and one user, with manual imports and exports and one month of audit retention. Consider it if those limits work for your data-preparation needs.

Tableau offers Creator at 75.00 USD per month billed annually and Explorer at 42.00 USD per month billed annually. It is an alternative for readers considering those paid plans.

DeepTable is a paid option for API and Linux platforms.

Browse more options in Data Preparation Software and Data Cleansing Software.

Verdict

DataCleaner is a strong choice for technically capable users who need free, extensible, batch-oriented data profiling and rule-based cleansing across varied sources. Its main reasons to choose it are the breadth of input types, reference-data checks, and developer extension options. Look elsewhere if cloud-first or real-time operation is essential, or if release cadence is a priority.

	DataCleaner	timeseriesflattener
Free plan	Yes	No
Free trial	No	No
Paid from	Free	—
Open source	No	No
Platforms	Linux, macOS, Windows	api, Linux
Free plan	Yes	Yes
Data profiling	Yes	—
Visual workflows	Yes	—
Deployment	desktop	self_hosted
Scheduled runs	Yes	—

DataCleaner vs timeseriesflattener

DataCleaner

timeseriesflattener