The collective sum of the world’s data will grow to a staggering 175 zettabytes by 2025. However, organizations are able to utilize only 50% of their existing data for decision making and this figure gets smaller as organization size increases. Given the trajectory of data generated to data utilized, the ability to refine and derive insights from this data will define the path to success.
In this article, we’ll walk you through the top 10 data wrangling tools that experts are using in 2023 to manage, clean, and transform their data.
Alteryx APA and Trifacta boast a high market share while Tableau is widely renowned for its ease-of-use. Open-source solutions like Talend also add to the plethora of choices, making the situation tricky. Add tools like xAqua to the list and you’ll have a plug-and-play, swiss army knife-like tool that can be used by almost anyone in your organization.
But selecting a data wrangling tool isn’t just about understanding its features. This approach can leave you vulnerable to problems ranging from a steep learning curve to integration challenges and scalability issues.
Thus, it’s also about recognizing the potential challenges you might face. We’ll explore these issues in detail to help you make an informed decision for implementing a data wrangling tool that best fits your needs.
Let’s begin:
Top 10 Data Wrangling Tools For A Smoother Data Analysis Journey In 2023
Before you can choose the best data wrangling tool, it’s essential to have a clear understanding of your specific needs. Ask yourself these questions:
- What type of data will you be working with? (e.g., structured, unstructured, or semi-structured)
- How large is the dataset? (small, medium, or big data)
- What is your technical skill level? (beginner, intermediate, or expert)
- What is your budget? (free, affordable, or enterprise)
Your answers will guide you in narrowing down your options and finding the perfect data wrangling tool for you from this ultimate list:
#1. xAqua
xAQUA, a Unified Data Platform (UDP), is a solution tailored to simplify intricate data challenges. Designed to democratize data, it ensures that every member of your organization can readily access and control data. It promotes data quality and trust while enhancing collaboration and delivering rapid data product outputs.
It obscures the complexities of technology through its intuitive and interactive user interfaces coupled with automation. This empowers even less tech-savvy users to perform self-service data operations. This platform also shines in its scalability, flexibly adapting to data requirements and processing data at scale.
xAQUA promotes a swift transformation of data into actionable insights. Its low-code/no-code platform, accompanied by a self-service drag-and-drop data pipeline, presents out-of-the-box capabilities for data engineering and data science.
The application areas for xAQUA are expansive, including data integration, data quality, data governance, data transformation, and data analytics. Top industries such as government, financial & banking, healthcare, and pharmaceuticals are leveraging xAQUA for use cases like data warehousing, customer data management, supply chain management, risk analysis, data monetization, and data asset management.
One of the standout features of xAQUA is its efficacy in managing both structured and semi-structured data. It achieves this by establishing connections with over 1100 diverse data sources, thereby positioning itself as a robust solution for big data wrangling tasks. Hence, it stands as an all-inclusive solution for the modern data-driven organization.
#2. Altair Monarch
Altair Monarch is a powerful data wrangling tool with a market share of 0.04%. It excels in managing structured and semi-structured data, offering a comprehensive suite for data preparation, cleaning, transformation, and integration. It stands out by its capability to extract data from diverse sources, including PDFs and text-based reports, making it highly valuable in industries like healthcare and accounting.
With an intuitive interface and machine learning capabilities, Altair Monarch streamlines and automates routine tasks. It offers over 80 pre-built data preparation functions and enables model exports to common BI or analytics platforms. The Altair Knowledge Hub, a browser-based platform, provides visual data preparation and machine learning suggestions for data enrichment and transformation.
However, the tool’s advanced functionality may present an unnecessary learning curve for users with less intensive needs. Performance issues with larger datasets and less reliable PDF import functions are some of its noted drawbacks. Despite this, the tool’s diverse range of functionalities often justifies its starting price of over $2000 for enterprise users, catering primarily to the finance, healthcare, retail, government, and insurance sectors.
Its key features include robust data integration capabilities, specialized PDF importing, and the ability to extract specific data fields in Excel. Altair Monarch effectively balances the simplification of complex data handling tasks while maintaining the depth and credibility necessary for expert-level data analysts.
#3. Talend
Talend is a free and open-source data wrangling software, and it holds a market share of 2.08%. It is widely used for managing both structured and semi-structured data. It’s an affordable choice for businesses, especially in the finance, healthcare, retail, manufacturing, and telecom sectors. However, it requires a high level of technical proficiency, limiting its usability for beginners and real-time data processing.
Talend’s browser-based platform offers a point-and-click interface, simplifying data manipulation. It’s highly flexible, allowing for coding from scratch and offering built-in processes for tasks like enrichment and integration.
Its key features include universal data integration from diverse sources, flexibility in building and running data pipelines anywhere, automatic data cleansing using machine learning, and support for Application and API integrations.
Despite a few drawbacks, like occasionally lacking machine learning functionality and being memory-intensive, its broad range of data manipulation capabilities outweighs these limitations.
#4. Alteryx APA Platform
Alteryx APA, with a 7.46% market share, offers a comprehensive data analytics and science platform. It provides over 100 pre-built data wrangling functions and supports a vast range of data sources, all without sacrificing speed.
The platform stands out for its integration with other tools like Tableau and encourages collaborative work in its user community. With wide application in sectors like finance, healthcare, and retail, it excels in tasks such as predictive modeling, customer segmentation, and supply chain optimization.
However, its machine learning capabilities may not satisfy advanced users. Additionally, the interface may seem dated, and its license-based pricing model can be costly for small businesses. There’s also no free trial version. Despite these, Alteryx APA remains a powerful choice for many, balancing robust functionality with user-friendly features.
#5. Paxata Self-Service Data Preparation By DataRobot
Paxata’s Self-Service Data Preparation is a remarkable tool within the Adaptive Information Platform, providing a streamlined solution for transforming raw data into valuable insights. With a significant 10.55% market share and affordable pricing starting at $360/month, Paxata serves various industries, including healthcare, retail, telecom, and more.
This self-service application is designed with a user-friendly visual interface, mirroring familiar spreadsheet metaphors for easy adaptation. It offers flexible deployment and features Assisted Intelligence, a unique feature providing algorithms for data interpretation and leveraging machine learning for future data tasks.
While Paxata is highly versatile, it may not be suitable for real-time data processing, small-scale data preparation, and highly specialized tasks. It’s a powerful tool, but organizations with limited resources or technical expertise might find it challenging to fully utilize.
#6. Cambridge Semantics Anzo
Cambridge Semantics Anzo stands out in data wrangling, handling structured and semi-structured data with ease. It provides numerous connectors to different data sources, simplifying data discovery and integration. Users can effortlessly find, connect, and blend data from various internal and external sources, including cloud or on-premises data lakes.
Adding to its capabilities, Anzo incorporates machine learning, enabling users to build predictive models. However, these capabilities may not satisfy advanced users. Anzo’s standout feature is its data cataloging, using graph models to encode a Semantic Layer, helping users understand their data in a business context.
It excels in data integration, management, analytics, and governance. Anzo is widely used in industries like finance, healthcare, retail, telecom, and manufacturing, with applications in fraud detection, risk management, regulatory compliance, and supply chain optimization.
However, Anzo might not be suitable for all. It can be costly for small businesses, and real-time data processing is not its strong suit. Thus, it’s crucial to consider specific requirements and budgets before choosing Anzo.
#7. Tableau Desktop
Tableau Desktop has carved a niche in the data visualization domain, turning data into interactive and aesthetically pleasing visuals. While not a dedicated data wrangling tool, it offers fundamental data prep and cleaning features, sufficient for handling small to medium datasets.
Its intuitive interface allows users to create charts and dashboards with a simple drag-and-drop. The real-time sharing feature enhances collaboration, making it a favorite across industries like finance, healthcare, and retail, for varied use cases such as sales analytics and financial reporting.
Tableau’s data preview pane and interpreter tools help users identify key elements in datasets swiftly. It offers robust security with multiple authentication and authorization systems, making data handling a secure endeavor.
However, for those dealing with extensive data wrangling or unstructured data, Tableau might fall short. Additional tools like Alteryx or Python could complement it in these scenarios. Its cost might be a factor for some, particularly startups. Overall, Tableau Desktop excels in turning data into insightful visuals, making data interpretation more engaging and efficient.
#8. Datameer Enterprise
Datameer Enterprise, a SaaS Data Transformation platform, stands out for its ability to simplify the complexities of Data Munging and Integration. It provides software engineers with the capability to extract, manipulate, and load datasets into cloud data warehouses like Snowflake. Datameer’s broad compatibility is noteworthy, accepting a range of formats, including CSV and JSON, for data aggregation.
The platform’s comprehensive Data Profiling and catalog-like Data Documentation further enhance its data transformation capabilities. These features empower users to monitor and assess data quality, spotting faulty, missing, or outlying fields and values effectively. With Datameer’s detailed visual data profile, users can grasp the overall form of data, enhancing their ability to draw meaningful insights.
It offers an intuitive and interactive spreadsheet-style interface, enabling users to transform, blend, and enrich complex data, and create data pipelines with ease. This, combined with a multi-user environment, caters to both tech-savvy users and those less familiar with code.
Datameer also offers extensive documentation, using metadata and wiki-style descriptions, tags, and comments to provide both system and user-generated data documentation on top of a shared workspace.
It finds extensive use in data preparation, data integration, data analytics, and data visualization in sectors such as finance, healthcare, retail, telecom, and manufacturing that benefit from its functionalities. However, it’s essential to note that Datameer may not be ideal for users who require real-time data processing or those operating with limited budgets.
#9. Microsoft Power Query
Power Query is an all-inclusive data wrangling tool, providing a plethora of connections to various data sources, hence making the data import into Excel or Power BI seamless. It stands out owing to its intuitive interface that simplifies the process of data modification and cleaning, making it accessible to users of varied experience levels.
Power Query’s features include connectivity to a wide range of data sources, from text files to CSV files and Excel Workbooks. Its ‘Merge’ option stands as a powerful alternative to Excel’s VLOOKUP function, especially when handling large datasets.
However, the tool may not serve as the perfect solution for everyone. Power Query shines in synergy with other Microsoft products like Power BI and Power Automate, which could be its Achilles heel as well. If you are not a frequent user of Microsoft’s other offerings, there might be more suitable options available in the market. Similarly, for those seeking advanced data wrangling capabilities or working predominantly with unstructured data, Power Query might fall short of expectations.
#10. Trifacta
Trifacta is a cloud-based platform that intelligently applies machine learning and analytics models to complex datasets. This is made possible through its key features such as deduplication and linear transformation techniques, which enhance data cleanliness by eliminating duplicates and filling blank cells.
Trifacta’s user-friendly design utilizes machine learning to detect outliers and erroneous data, facilitating a faster data preparation process. Even with its limitations, like lack of external data store connectivity and limited output formats (CSV and JSON), Trifacta remains a popular choice due to its accessibility and ease of use.
Its users like the fact that it allows data ingestion from any cloud or hybrid environment while also supporting multiple mechanisms for detecting and standardizing data patterns. Data preparation tasks are organized inflows, each consisting of datasets and their associated transformation steps, making the process more tractable.
While Trifacta excels in several areas, it does have some limitations. Notably, it lacks support for external data store connectivity and backend data storage integrations, requiring users to upload and download all files directly through the application. Additionally, Trifacta is limited in its output capabilities, with results only able to be written in CSV and JSON formats. It also doesn’t support the compression of outputs. Despite these constraints, Trifacta remains a strong contender in the data wrangling sphere due to its other robust features.
Problems With Data Wrangling Tools In DIY Data Stack
Here are the common problems associated with using data wrangling tools in a DIY data stack:
- Lack of Standardization: Since each tool in a DIY data stack might have been chosen for its unique strengths, there may be a lack of standardization across the stack, leading to compatibility issues.
- Overall Steep Learning Curve: Each tool comes with its own syntax, features, and limitations. Learning to use multiple tools effectively can be time-consuming and complex.
- Integration Challenges: Making different tools work together seamlessly can be a difficult task. This includes data format mismatches, API limitations, and versioning issues.
- Maintenance Overhead: Each tool in the stack needs to be regularly updated and maintained. This can be a significant overhead in terms of time and resources.
- Limited Scalability: Some tools that work well on a small scale may not scale well when data volumes increase. This can limit the overall scalability of the data stack.
- Dependency on Expertise: The effectiveness of the DIY data stack is often heavily dependent on the skills and knowledge of the team. If the team lacks the necessary expertise, it could lead to inefficiencies and errors.
- Security Concerns: Ensuring data security across multiple tools and platforms can be challenging. DIY data stacks may lack the robust security measures provided by an integrated data platform.
- Costs Can Add Up: While individual tools might be free or cheap, the total cost of ownership of a DIY data stack can be high, especially when considering the time spent learning, integrating, and maintaining the tools.
- Time-consuming Data Preparation: Data wrangling often takes up a significant amount of time, leaving less time for actual data analysis.
- Insufficient Data Quality Measures: DIY tools might not have comprehensive data quality checks in place, leading to issues with the accuracy and reliability of the data.
The xAqua UDP platform, on the other hand, is designed to accelerate time-to-delivery and improve data quality. It offers data pipeline automation, enabling automated CI/CD for the data pipeline lifecycle. This includes composing, modifying, versioning, scheduling, building, deploying, running, and monitoring data pipelines.
xAqua UDP simplifies enterprise data and analytics challenges in terms of time, cost, quality, trust, and risk. It empowers less technology-savvy users, providing a holistic solution to integrate, share, analyze, and manage enterprise data assets. It also promotes an insight-driven culture through the democratization of enterprise data assets.
It is designed to deliver ready-to-use, reliable, and trusted live data as a product (LDaaP) to everyone in the business, whenever and wherever they need it. This includes a live data catalog that allows users to search, access, explore, understand, and analyze enterprise data assets as LDaaP.
Wrap Up
As organizations grapple with the daunting task of cleaning and organizing massive volumes of unstructured or semi-structured data, data wrangling becomes an integral part of their IT infrastructure. The right tools are crucial in efficiently transforming this data, extracting valuable insights, and driving informed decision-making.
However, a DIY approach to creating a data stack can present significant challenges, including a steep learning curve, integration issues, scalability limitations, and heightened security concerns. While all-in-one unified data platforms like xAqua, offer compelling solutions for the effective monetization of enterprise data assets.
So, are you ready to see the difference xAqua can make in your data operations? Don’t hesitate to take a demo call for xAqua and discover how it can transform your data like a pro.
