Around 328.7 million terabytes of data is created each day. While an abundance of data can fuel  innovation and improve decision-making for businesses, it also means additional work of sifting through it before transforming it into insights. Thankfully, businesses now have data wrangling tools at their disposal to tame this data deluge.

Data wrangling tools are powerful solutions designed to simplify and automate the process of data preparation. They enable data professionals to  clean, transform, and organize raw data efficiently, saving countless hours of manual work while ensuring data quality and consistency.

In this blog, we will explore the benefits of data wrangling tools and the top contenders in the market.

Understanding Data Wrangling

The data wrangling process involves several key steps that transform raw data into a usable format. Here are the key stages in the data wrangling process:

  1. Collecting data from all sources
  2. Inspecting data for quality issues and inconsistencies.
  3. Cleaning data to fill missing values, remove duplicates, and correct errors.
  4. Transforming data to fit it into certain formats
  5. Integrating data from disparate sources

Data wrangling is the backbone of data science and analysis because it resolves errors, inconsistencies, and missing values in raw data. It also reduces biases and improves analysis accuracy, enabling discovery of hidden patterns and relationships.

Let’s look at how the leading tools in the market handle data wrangling.

8 Data Wrangling Tools to Choose From in 2023


Astera is a code-free, easy-to-use data integration tool that is designed for users with all levels of technical capabilities. Astera offers end-to-end data management from extraction to data integration, data warehousing and even API management. The tool can save you countless hours of manual work. Plus, you don’t need to hire experts to use this tool. Here are some noteworthy features of Astera, which make it an excellent for data wrangling:

Key Features of Astera

  • User-friendly interface: Astera is designed for business and technical users alike. Its simple, drag-and-drop interface empowers business users to prepare data themselves, without relying extensively on IT or coding.
  • AI-based Data extraction: You can use the tool to easily extract data from unstructured data sources within minutes. The AI algorithms can easily detect the field you want to extract, eliminating the need to make templates for different kinds of document formats.
  • Variety of Connectors: The tool supports a large library of on-premises and cloud-based sources and destinations including databases, data warehouses, and data lakes. You can also connect to any other source or destination easily through pre-built API connectors.
  • Data transformation: Astera offers various built-in transformations and functions that allow you to manipulate your data the way you want. All you need to do is simply drag and drop the required transformations and map them to your data pipeline. Astera makes it easier to work even with complex transformations such as normalization/denormalization, decision tree etc.
  • Data Quality features: The tool supports robust built-in data quality features that allow you to easily cleanse, profile and validate data. You can also specify data quality rules to flag erroneous records which you can review later.
  • Automation: Set up your workflows once and leverage Astera’s job scheduling features to automate the entire process.
  • Parallel-processing Engine: Astera is built on an industrial-strength parallel-processing engine which handles large  data sets seamlessly.

Tableau Desktop

Tableau Desktop is primarily a data visualization and analytics tool that allows you to create interactive visualizations and dashboards. While Tableau Desktop excels in data visualization, it also offers several features for data wrangling.

Key features of Tableau Desktop

  • Data Connection: Tableau Desktop supports connectivity to a wide range of data sources, including databases, spreadsheets, and online services. You can connect to your data source(s) and import the data into Tableau’s interface to work with your data.
  • Data Transformation: It provides a range of transformation options to convert your data. You can pivot data from wide to long format or vice versa, transpose rows and columns, and split or combine fields. These transformations help in preparing the data to match the desired format for analysis and visualization.
  • Automation and Scripts: Tableau’s JavaScript API (Application Programming Interface) and Tableau Prep Builder automation and scripting capabilities allow you to automate repetitive data preparation tasks and integrate Tableau with other tools or systems.
  • Data Cleaning: The tool is equipped with various cleaning capabilities, such as handling missing values, removing duplicates, and correcting inconsistent or erroneous data. You can use Tableau’s data quality functions and techniques to ensure your data is accurate and reliable for analysis.

Python Pandas

Python pandas is an open-source library used for data manipulation and analysis. Itprovides data structures and functions that are specifically designed to make working with structured data, such as tabular data, more efficient and intuitive. Pandas is built on top of the NumPy library, which provides support for mathematical and numerical operations in Python.

One of the main data structures in pandas is the DataFrame, which is a two-dimensional table-like data structure with labeled axes (rows and columns). DataFrames allow you to store and manipulate data in a way that resembles working with spreadsheets or SQL tables. It provides various functionalities for indexing, slicing, filtering, grouping, and aggregating data.

Here is how you can use Pandas as a data wrangling tool:

Data manipulation: Pandas provides powerful tools for cleaning, and transforming data. You can perform operations like merging and joining datasets, filtering rows based on conditions, adding or removing columns, and handling missing data.

Key features of Python Pandas

  • Data analysis: It offers a wide range of statistical and analytical functions to explore and summarize data. You can perform descriptive statistics, apply mathematical operations, perform grouping and aggregation, and generate various visualizations.
  • Input/output support: Pandas supports reading and writing data in various formats, including CSV, Excel, SQL databases, JSON, and more.
  • Time series analysis: It includes tools for resampling, time shifting, rolling windows, and handling time-based data formats.
  • Integration with other libraries: The tool integrates well with other scientific computing and data analysis libraries in the Python ecosystem. OpenRefine


OpenRefine, formerly known as Google Refine, is an open-source data wrangling tool. OpenRefine is an easy to use tool with a widewide range of features that help users work with messy and inconsistent data to make it more structured and useful. OpenRefine is designed to handle large datasets and allows users to perform complex with ease.

One of the primary functions of OpenRefine is data cleaning. It allows users to explore and clean data by identifying and fixing inconsistencies, errors, and missing values.

Key Features of Openrefine

  • The tool provides various methods to transform data, such as splitting cells, merging columns, and correcting values using regular expressions.
  • OpenRefine also supports clustering and faceting features to detect and reconcile similar values within the data set.
  • It provides a record linkage feature that helps users match and merge data from multiple sources.

Apache Spark

Apache Spark is an open-source distributed computing system that provides a fast and general-purpose framework for large-scale data processing and supports a wide range of data analytics tasks

Key Features of Apache Spark

  • Distributed Computing: Apache Spark is built for distributed computing which means it can process and analyze large datasets across a cluster of machines,, enabling parallel processing and high scalability.
  • Data Loading: Apache Spark supports various data sources, including file systems (such as Hadoop Distributed File System (HDFS), S3, and local filesystems) databases.
  • Data Transformation: It provides a wide range of transformation operations, such as filtering, mapping, aggregating, joining, and sorting.
  • Data Splitting and Sampling: It also allows users to split datasets into subsets or perform random sampling for data exploration and analysis purposes. This functionality is useful for tasks like data validation, model training, and testing.


Datameer is a data preparation and analytics platform designed to simplify and accelerate the process of exploring, transforming, and analyzing large volumes of data.

Key features of Datameer

  • Data Integration: The tool offers built-in connectors for various data sources, including databases, HDFS, cloud storage platforms, and more. It allows users to easily import and integrate data from multiple sources into a centralized workspace for analysis.
  • Data Transformation: Datameer features a familiar, spreadsheet-like interface that makes it easy for users to navigate, explore, and manipulate data. Users can interact with data directly, apply formulas, and perform ad-hoc analysis within the intuitive interface.


Alteryx is primarily a data analytics and data science platform that empowers you to extract valuable insights from their data. The tool provides a comprehensive sets of features for data preparation, blending and analysis, which makes it a good data wrangling tool as well.

Key features of Alteryx

  • Data Preparation: Alteryx provides a wide range of tools for data cleansing, transformation, and enrichment. You can format data, handle missing values, merge and join data sets, and perform calculations or aggregations.
  • Data Blending: You can combine data from multiple sources and systems, regardless of their format or location. Alteryx supports various data sources, including databases, and cloud services.
  • Data Connectors: It provides connectors for a wide range of data sources, such as databases, cloud platforms, file formats, and applications.
  • Data Profiling and Quality Control: Alteryx also offers data profiling capabilities to assess the quality, structure, and content of datasets. You can identify data issues, validate data integrity, and ensure data quality throughout the analytics process.
  • Version Control: Alteryx provides version control functionality, allowing users to track changes made to workflows, collaborate on different versions, and revert to previous versions if needed.

Trifacta Wrangler

Trifacta is a commercial data wrangling tool that provides a visual interface for data preparation and cleaning

Key Features of Trifacta Wrangler

  • Visual Data Exploration: Trifacta allows you to interactively explore and understand their data. You can preview the data, visualize distributions, and identify patterns and outliers to gain insights into the dataset.
  • Data Cleaning and Transformation: It comes with built-in functions and transformations to clean and preprocess data. You can handle missing values, remove duplicates, standardize formats, and correct errors. The tool also supports transformations like splitting columns, merging data, and deriving new variables using expressions and formulas.
  • Data Profiling and Quality Assessment: You will also find data profiling capabilities that analyze the dataset and provide statistical summaries, data quality assessments, and data lineage information.
  • Data Integration and Connectivity: Trifacta supports integration with various data sources and formats, including databases, files (such as CSV, Excel, JSON), cloud storage platforms, etc.

Why Invest in a Data Wrangling Tool?

Raw data is often ridden with missing values, outliers, and inconsistencies. The sheer volume and velocity of raw data often make it challenging to clean and manipulate it at the speed required by the data-driven world today. Data wrangling tools make the process easier through automation:

Here are some of the benefits of using data wrangling tools:

  1. Efficiency: Data wrangling tools come with intuitive interfaces, drag-and-drop functionalities, and pre-built functions that simplify and accelerate data cleaning, transformation, and integration.
  2. Improved Data Quality and Consistency: The built-in functions and algorithms in data wrangling tools enhance data quality, ensure accuracy, completeness, and consistency across datasets. They enable you to easily fix missing values, outliers, inconsistencies, and formatting issues.
  3. Data Integration and Compatibility: The tools support various file formats, databases, APIs, and data connectors, which simplify data integration from diverse sources.
  4. Enhanced Data Exploration and Visualization: Many data wrangling tools provide visualization capabilities, allowing you to explore and visualize data during the wrangling process. This feature helps you in understanding data distributions, identifying patterns, and detecting outliers or anomalies.
  5. Scalability : The best part about data wrangling tools is their ability to handle large data volumes, allowing seamless scalability. These tools employ optimized algorithms and parallel processing techniques, enabling faster data processing and analysis.
  6. Repeatability and Documentation: You can easily create automated workflows or scripts to capture the steps performed during the data preparation process and then repeat them for consistency and reproducibility in analysis.

How to Choose the right Data Wrangling Tool?

Ultimately, the data wrangling tool you choose for your business depends on its requirements , your budget, and the type of data sources you deal with. Here are some common factors that you should keep in mind:

  1. Data Requirements: Consider the types and volumes of data you will be working with. Some tools may excel at handling structured data, while others may be better suited for unstructured or semi-structured data. Evaluate whether the tool can handle the data formats, sizes, and complexities specific to your use case.
  2. Ease of Use: Look for a tool with an intuitive and user-friendly interface. Data wrangling often involves complex operations, so a tool that offers a visual and interactive approach to data wrangling, such as a drag-and-drop interface or a graphical workflow designer, can make the process more efficient and accessible for your teams.
  3. Functionality: Evaluate the tool’s data wrangling capabilities and the host of features it offers. You should also consider the range of data transformation and cleansing functions it offers, such as data merging, splitting, filtering, and aggregation. Look for features that can automate repetitive tasks and streamline your data preparation workflows.
  4. Data Connectivity: Assess the tool’s ability to connect to various data sources and systems. Ensure that it supports the data formats and protocols relevant to your organization, such as databases, cloud platforms, APIs, or file formats. The tool should enable seamless data integration and extraction from diverse sources.
  5. Scalability and Performance: Consider the tool’s ability to handle large volumes of data efficiently. Evaluate its performance capabilities, including processing speed and memory management, to ensure it can handle large data volumes within acceptable time frames.

Next Step – Transform Your Data With Astera Centerprise

Astera Centerprise

The ever-increasing amount of data today warrants the use of code-free data wrangling tools that make data preparation and cleaning easier. These tools are the key to getting timely insights, So, if you want to equip your business with a competitive edge, it only makes sense to invest in a future proof data wrangling tool that all your team members can use easily. However, ensure you keep your business requirements at the front and center when deciding on the tool.

Want to accelerate data wrangling? Download 14-day free trial of Astera Centerprise today.

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