[2024] 9 Best Data Cleaning Tools Rated by User!

This article introduce to you TOP 9 Best Data Cleaning Tools in order to boost X10 your productivity in Data Cleaning. These brands are carefully chosen based on user reviews and feedbacks.

Data cleaning and preparation is a crucial step in the machine learning, analytics, and business intelligence workflows. Raw data often contains errors, missing values, duplicates, and inconsistencies that must be addressed.

We will examine 9 leading data cleaning solutions picked by users based on reviews and ratings for capabilities, ease of use and effectiveness at tackling data cleaning challenges.

Summary Table of Data Cleaning Tools

1. Google Refine – Open source data cleaning

Google Refine – Open source data cleaning

Overview

Google Refine is a popular open source tool for cleaning and transforming raw datasets in preparation for analysis and machine learning. It provides capabilities for dealing with messy data like text parsing, multi-value cell editing, and formatting conversions.

Why use it?

Google Refine aims to support anyone working with raw data in standard formats like CSV, TSV, JSON etc. It simplifies the process of preparing unstructured datasets for downstream use through an intuitive graphical interface. Non-technical users can clean data without needing to code.

Main Features

• 1. Import data – Upload datasets in various file formats.
  2. Parse/edit cells – Identify and edit multi-value cells.
  3. Transform data – Trim, extract, convert and derive column data.
  4. Cluster and edit – Detect fuzzy duplicate rows for merging.
  5. Extend with APIs – Integration with Python, Clojure and more.
  6. Export results – Output cleaned datasets in standard formats.
  7. Undo actions – Revert changes if needed.
  8. Open source – Free and open source software.

Pricing

As open source software, Google Refine is free to use. Optional paid support plans are offered by third parties.

User Review

“With Google Refine I could easily prepare very messy raw datasets for analysis without needing to code transformations – hugely valuable free tool.”

Data Cleaning Tools

2. OpenRefine – Powerful data transformation tool

OpenRefine – Powerful data transformation tool

Overview

OpenRefine is an open source data cleaning and transformation tool useful for preparing datasets for analysis, machine learning, and visualization. It provides capabilities to filter, combine, edit and transform data.

Why use it?

OpenRefine aims to make data cleaning accessible for both technical and non-technical users. Its spreadsheet-like interface and powerful functions allow efficiently wrangling even very large, messy datasets.

Main Features

• 1. Import data – Upload datasets in a variety of file types.
  2. Identify types – Detect types of data like numbers, strings, dates.
  3. Filter data – Filter and sort data based on parameters.
  4. Transform data – Trim, extract, split, and derive new column data.
  5. Combine datasets – Merge and append data from multiple sources.
  6. Extend functionality – Write extensions in Python and Clojure.
  7. Export options – Output cleaned data to many formats.
  8. Open source – Free and open source for anyone to use.

Pricing

As open source software, OpenRefine is free to download and use. Third party support plans are optionally available.

User Review

“OpenRefine is my go-to tool for efficiently cleaning and transforming even very large, messy datasets without coding.”

3. Trifacta – Data preparation platform

Trifacta – Data preparation platform

Overview

Trifacta is an end-to-end data preparation and cleaning platform providing capabilities for discovering, structuring, cleansing and enriching diverse datasets for analytics. The tool integrates AI and machine learning techniques.

Why use it?

Trifacta aims to make self-service data prep accessible to users across any data skill level. Its intuitive visual interface simplifies discovering and addressing data quality issues to ready data for analytical use cases.

Main Features

• 1. Data discovery – Explore datasets and build metadata.
  2. Data profiling – Understand, filter and sample data.
  3. Data cleaning – Identify and fix missing values, outliers etc.
  4. Data enrichment – Merge disparate sources, derive new data etc.
  5. Intelligence – AI provides suggestions to clean and enrich data.
  6. Large data – Handles big data sources like Hadoop and Spark.
  7. Cloud infrastructure – Managed service available on AWS and GCP.
  8. Team collaboration – Share and discuss data projects with others.

Pricing

Trifacta offers a free community edition, along with custom enterprise pricing tailored to organizational needs.

User Review

“Trifacta provides an intuitive and powerful platform to tackle all my data preparation needs – from discovery to cleaning and enrichment.”

4. Pandas – Data analysis library for Python

Pandas – Data analysis library for Python

Overview

Pandas is an essential open source Python library used for data preparation, manipulation, cleaning, and analysis. It offers powerful data structures and data tools that make handling structured and time series data easy.

Why use it?

Pandas provides fast, flexible data manipulation capabilities in an easy-to-use package directly in the Python data science ecosystem. Both novice and expert Python programmers utilize Pandas for ETL and data preparation.

Main Features

1. 1. Data structures – Series for 1D, DataFrames for 2D data.
   2. Data alignment – Align and join datasets.
   3. Handling missing data – Identify, filter, fill or drop missing values.
   4. Data transformation – Aggregate, slice, transform, filter, sample datasets.
   5. IO tools – Read/write data from various file formats and data sources.
   6. Time series – Generate, manipulate, convert dates and times.
   7. Merging – Concat, join and merge datasets.
   8. Open source – Free open source library.

Pricing

As an open source Python library, Pandas is free to use. It is included in distributions like Anaconda.

User Review

“Pandas is absolutely essential to prepare, clean, align, merge and transform datasets in Python – flexible and extremely powerful.”

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5. SQL Power Architect – Visual data modeling

SQL Power Architect – Visual data modeling

Overview

SQL Power Architect is a visual tool for designing, documenting, and constructing relational database schemas for data analytics and warehousing projects. It generates editable SQL code for building databases.

Why use it?

SQL Power Architect enables users to visually model data structures, relationships, and constraints to develop optimized relational database schemas rapidly for downstream analytics usage.

Main Features

• 1. Visual modeling – Drag-and-drop interface to design database schema.
  2. Multi-database support – Model databases for MySQL, Oracle, SQL etc.
  3. Reverse engineering – Import existing database into visual model.
  4. Model documentation – Visually document models with attachments.
  5. Model transformation – Cleanse, reshape existing models.
  6. Code generation – Generates SQL code to build designed databases.
  7. Collaborative modeling – Lets multiple team members work together.
  8. Layout customization – Tailor modeling environment to preferences.

Pricing

SQL Power Architect has a free limited version. Paid licenses start at $197 per user.

User Review

“SQL Power Architect has been invaluable for visually planning and designing optimized databases for analytics – huge time saver.”

6. Data Ladder – Data quality management platform

Data Ladder – Data quality management platform

Overview

Data Ladder is an end-to-end data quality management and governance platform providing capabilities for ongoing data monitoring, auditing, issue resolution workflows and embedded data quality.

Why use it?

Data Ladder aims to help data teams ensure high quality, trustworthy data across their analytics and machine learning data pipelines through continuous validation and monitoring capabilities. A good Data Cleaning Tools.

Main Features

• 1. Data quality testing – Validate data at batch and row level.
  2. Scheduled monitoring – Set up regular automated tests.
  3. Centralized tracking – Customizable centralized issue logging.
  4. Collaboration workflows – Assign, track, and resolve issues.
  5. Real-time data quality – Embed rules into apps and BI tools.
  6. Custom validations – SQL, Python etc. to build custom validations.
  7. Data profiling – Interactive reports to understand data.
  8. API first – Build custom solutions leveraging API.

Pricing

Data Ladder offers a free trial. Paid plans start at $840 per month billed annually. Enterprise quotes available.

User Review

“With Data Ladder’s robust data quality testing and monitoring, our analytics pipelines now have built-in checks to ensure data integrity.”

7. WinPure – Excel data cleaning add-in

WinPure – Excel data cleaning add-in

Overview

WinPure is an Excel data cleaning tools and preparation add-in providing to profile, cleanse, process and transform data directly within Excel spreadsheets. The visual interface makes cleaning large datasets easier.

Why use it?

WinPure brings data preparation capabilities directly into users’ familiar Excel environment, allowing processing messy datasets powerfully without leaving Excel. Less technical users can clean data without coding.

Main Features

• 1. File interrogation – Scan files to profile and audit data.
  2. Missing data management – Find and fix missing values.
  3. Anomaly detection – Detect outliers and data errors.
  4. Matching – Identify duplicate records within/across files.
  5. Parsing and splitting – Break up data into separate columns as needed.
  6. Patterns – Leverage regex to extract patterns and segments.
  7. Statistical analysis – Analyze distributions to inform data prep.
  8. Workbook management – Track data flows across sheets and files.

Pricing

WinPure offers a free trial period. Paid licenses start at $590 per user annually for Pro version.

User Review

“With WinPure, I have powerful data preparation capabilities directly within Excel – no need to export data elsewhere to clean it.”

8. DataCleaner – Open source data quality analysis

DataCleaner – Open source data quality analysis

Overview

DataCleaner is an open source data profiling and data cleaning tool useful for analysis, reporting on, and improving the quality of datasets, especially for extracting value from big data sources.

Why use it?

DataCleaner provides over 60 validators and transformers along with easy profiling of very large datasets to identify quality issues, useful for both one-off data exploration and ongoing data pipelines.

Main Features

• 1. Powerful profiling – Understand, describe and visualize dataset content and structure.
  2. Customizable transformers – Filter, standardize, deduplicate, merge datasets.
  3. Clustering – Identify fuzzy duplicate records.
  4. Extensible – Java libraries to build custom components.
  5. Automation – Schedule and run data quality jobs.
  6. Big data support – Integrations with Hadoop, Spark etc.
  7. Platform independent – Run on Windows, Linux and MacOS.
  8. Open source software – Licensed under LGPL.

Pricing

As open source software, DataCleaner can be used free of cost. Commercial support plans are offered by Human Inference.

User Review

“DataCleaner meets all my data quality analysis and transformation needs as an open source platform – great for profiling, cleansing, deduplicating huge datasets.”

9. DataPrep Studio – Visual data preparation tool

DataPrep Studio – Visual data preparation tool

Overview

Dataprep Studio is a visual data preparation tool providing an intuitive interface for cleaning, combining, and transforming messy, complex data into analysis-ready datasets without coding.

Why use it?

Dataprep empowers novice to expert users to rapidly clean and process data of all types and volumes into high quality, structured formats for analytics and ML without taxing IT resources.

Main Features

• 1. Import data – Load structured, semi-structured and unstructured data.
  2. Intuitive visual interface – Clean, parse, transform via drag-and-drop.
  3. Custom formulas – Build custom data transformations as needed.
  4. Machine learning – AI recommends parse steps.
  5. Scalable – Handle small to extremely large datasets.
  6. Team collaboration – Share and re-use data preparation recipes.
  7. Package and deploy – Export cleaned, prepared datasets.
  8. Governance – Tracking, versioning, permissions for enterprise.

Pricing

DataPrep Studio is sold via Unifi, starts at $1500 per user.

User Review

“With Dataprep Studio we could visually clean, prepare and transform endless rows of raw data rapidly without any coding – amazing time saver.”

Conclusion of Data Cleaning Tools

Robust data cleaning and preparation tools are indispensable for ensuring quality, analysis-ready datasets that produce trustworthy insights. Solutions like Google Refine, Trifacta, Pandas, and others featured provide powerful capabilities to tackle all types of data cleaning and preparation challenges efficiently.

Key aspects users evaluate include visual vs coding interfaces, data volumes supported, profiling and quality analysis functionality, transformation capabilities, big data integration, collaboration features, and ease of use for target user personas in the workflow. As data integrity becomes more crucial, continuous validation and monitoring capabilities are also emerging in tools. Integrated, scalable, and automated data cleaning enables deriving value from data.

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