10 Best Data Prep Tools in 2025

Preparing data used to be a behind-the-scenes task, handled by a few technical experts before anyone else could see the numbers. Today, it’s a team-wide priority. Whether you’re building dashboards, training AI models, or just trying to answer the next question faster, the quality of your data—and how easily you can shape it—make a big difference in your success.

That’s where data preparation tools come in. At its core, data prep helps teams turn scattered or inconsistent inputs into actionable data that everyone can use—the kind that supports timely decisions and delivers real results. And as more people from your sales, marketing, finance, and operations teams work directly with data, their demand for intuitive, flexible prep tools has only grown.

This guide covers everything you need to know: What data prep tools are, how they help, which features to prioritize, and which platforms are leading the way in 2025. Whether your team is just getting started or looking to level up, we’ll help you choose the right tool and get more value from your data, every step of the way.

What is a data prep tool?

A data preparation tool helps teams clean, structure, and enrich raw data so it’s ready for analysis, reporting, or modeling. Data prep tools can fix errors, standardize data formats, combine information from different sources, and organize it so it’s ready for dashboards, reports, or machine learning models. In short, it helps teams trust their data and use it with confidence.

These tools simplify tasks that used to require complex SQL queries or time-consuming spreadsheet work. Many data preparation platforms now offer drag-and-drop interfaces or guided workflows, so analysts and business teams alike can shape and refine data without writing code. This shift toward self-service analytics means teams can work more independently while still staying aligned on data definitions and sources.

Data prep tools vs ETL tools

Data preparation is often compared to ETL (extract, transform, load), but the focus is different. While ETL tools are typically used by engineers to move large volumes of data into a warehouse, data prep tools are built for ease of use and speed. They prioritize real-time exploration, team collaboration, and fast iteration over rigid pipelines.

As more teams take ownership of data in their day-to-day work, having the ability to clean and prepare it without depending entirely on technical resources has become essential. Data prep tools make that possible—and scalable.

Common use cases for data prep tools include:

• Preparing campaign performance data for marketing dashboards.
• Cleaning sales pipeline data for forecasting models.
• Structuring financial data for executive reporting.
• Enriching customer data for machine learning.

In each case, the goal is the same: help people work with data that’s clean, complete, and ready for action.

What are some of the benefits of using a data prep tool?

When teams spend less time cleaning data and more time using it, they can focus on what actually affects the business, like being able to answer questions, spot patterns, and make confident decisions. 

Here are five key benefits that make data prep tools essential:

1. Eliminate data silos

Modern teams pull data from dozens of sources, including CRMs, spreadsheets, cloud apps, and more. Data prep tools help connect those sources and combine them into a single view, so no one is stuck making decisions based on outdated sources or a partial picture.

2. Improve data quality and insights

Poor data quality can lead to misleading conclusions, costing organizations an average of nearly $13 million per year. Data prep tools catch errors, fill in missing values, remove duplicates, and help standardize inconsistent formats. Clean, trusted data gives teams a more accurate read on performance and enables better long-term strategies. 

3. Increase accessibility for non-technical teams

Data prep used to be limited to technical specialists. Today’s platforms make it easier for anyone to clean and shape data with self-service analytics. That means your marketing analyst or finance partner can get answers without waiting in an IT queue. 

4. Support AI and machine learning

The success of AI models depends on having clean, well-labeled, and properly structured data. Data prep tools play a central role in that process, whether you’re preparing customer segmentation data or training a predictive model. As teams explore more ways to use AI in data analytics, having reliable, well-prepared data becomes even more important for producing meaningful insights.

5. Boost operational efficiency

Cleaning and combining data by hand is repetitive and error-prone. With automation, version control, and reusable workflows, data prep tools help reduce busywork, improve accuracy, and keep cross-functional projects moving. Teams can spend more time analyzing trends and less time managing spreadsheets.

The bottom line: Data prep isn’t just a back-end task; it’s a foundational capability for teams that want to move quickly with fewer roadblocks.

What to look for and key features in a data prep tool

Not every team needs the most advanced tool on the market. The right data prep platform is the one that fits the way your people work; it’s based on your team’s skill level, their data demands, and how information is used downstream. To find the right fit, start by considering a few key factors that shape how your team interacts with data.

Start with your team

Who will be using the tool day-to-day? If it’s a marketing analyst building campaign dashboards or a finance partner checking data before a forecast, look for visual workflows and no-code options. These features make it easier for non-technical teammates to work independently. If your team includes technical analysts or data scientists, consider tools that also support SQL, Python, or R for more flexibility.

Understand your data environment

Think about how many sources you’re working with, how often your data changes, and how complex your transformations will be. If your team works with streaming data or high volumes, you’ll want a tool that scales easily—something often offered through analytics-as-a-service models. These platforms are designed to grow with your team and adapt as data demands evolve.

Check for integration

The best tools don’t sit in a silo. They connect easily to your cloud storage, BI tools, or machine learning workflows. Look for platforms that simplify getting clean data into the tools your team already uses without extra steps or custom code.

Prioritize governance

Even with self-service tools, governance matters. Teams should know where data came from, how it’s been changed, and who’s touched it. Look for built-in features that support data governance best practices, such as role-based access, audit trails, and version history.

Essential data prep features

Even the most intuitive platform needs the right features to support day-to-day work. As you evaluate your options, look for tools that offer the following capabilities to help your team move efficiently from raw data to reliable insights.

• Connectors: Directly pull in data from spreadsheets, cloud platforms, databases, and APIs.
• Data validation: Automatically flag missing values, duplicates, or incorrect formats.
• Cleansing tools: Standardize dates, fix typos, and clean inconsistent inputs.
• Data enrichment: Join external data, add lookups, or create calculated fields.
• Visual workflows: Use drag-and-drop steps to map out transformations.
• Low- and no-code options: Empower more team members to prep data without technical help.
• Data preview: See how changes affect your data in real time.
• Collaboration tools: Share, comment, and coedit workflows with teammates.
• Lineage and version tracking: Understand where data came from and how it’s changed.
• BI and AI integration: Push clean data directly to dashboards, reports, or ML models.
• Governance features: Control access, set roles, and maintain accuracy at scale.

Choosing a tool with these features helps your team spend less time cleaning data and more time putting it to work.

10 best data prep tools in 2025

With more teams working directly with data, choosing the right data preparation tool has become an essential part of building a modern analytics workflow. While the right fit depends on your team’s goals, technical experience, and ecosystem, the following tools are among the most widely used and recognized options available.

These platforms help teams transform raw data into analysis-ready formats, whether through visual workflows, automation, or built-in intelligence. This list will help you explore the features and approaches each platform offers for data preparation.

1. Domo

Domo provides an end-to-end platform to connect, prepare, visualize, and act on data, all in one place. Its Magic ETL tool allows teams to build visual data pipelines with drag-and-drop functionality, making it accessible to both technical and non-technical roles. Teams can combine data from spreadsheets, cloud services, and databases, apply transformations, and push results directly to dashboards, apps, or models.

Domo supports augmented analytics, offering built-in AI and machine learning capabilities for more advanced use cases. With collaboration tools, governed access controls, and real-time updates, it enables fast decision-making without sacrificing oversight. Domo also integrates with hundreds of connectors, helping teams work with data where it already lives.

2. Alteryx Designer Cloud

Alteryx Designer Cloud offers a visual interface for building data workflows, allowing analysts to blend, clean, and enrich data from various sources. It supports cloud-based access and scalability, making it easy for distributed teams to collaborate. Features like AI-assisted suggestions and smart transformation tools help accelerate the prep process.

The platform is especially useful for teams preparing data for analytics or modeling. It offers advanced transformation functions, built-in automation, and export options to BI tools or data science environments. Designer Cloud is designed to help teams spend less time on manual prep and more time on insight-driven work.

3. Tableau Prep

Tableau Prep is a data preparation solution designed to work alongside Tableau’s data visualization tools. It gives teams a visual, interactive interface for cleaning, shaping, and combining data before analysis. With a flow-based layout, Tableau Prep lets analysts and other team members see each step of the process and preview changes in real time.

The platform supports joins, unions, pivoting, and calculated fields, all within an approachable drag-and-drop environment. For teams already working in Tableau, Prep offers an easy way to get data into shape before building dashboards or sharing insights. It’s often used by marketing, operations, and finance teams looking to explore and refine data without relying on manual spreadsheets.

4. Microsoft Power Query

Microsoft Power Query is a built-in data preparation tool available in Excel and Power BI. It enables teams to connect to various sources, filter and shape data, and automate repeatable steps—all within a familiar interface. With point-and-click actions and formula-driven options, Power Query supports both beginner and advanced use.

The tool is particularly useful for individuals or departments already working in Microsoft environments. Teams can pull in data from spreadsheets, cloud apps, or databases, apply logic to transform it, and feed it directly into dashboards or reports. As part of Microsoft’s broader analytics ecosystem, Power Query helps teams create more consistent, self-service analytics workflows without starting from scratch.

5. Talend Data Preparation

Talend Data Preparation is a browser-based platform that allows teams to clean, enrich, and structure data through an intuitive point-and-click interface. It offers real-time previews, rule-based formatting, and smart suggestions, making it easier for data analysts and business partners to collaborate throughout the prep process.

Talend supports connections to spreadsheets, databases, and cloud applications and enables filtering, joining, and transforming data sets in a guided environment. Teams can create reusable prep workflows and share them across departments to promote consistency and save time. It’s commonly used in sales, customer experience, and data operations workflows that require clean inputs for reporting or integration.

6. Informatica Cloud Data Preparation

Informatica Cloud Data Preparation offers a scalable, cloud-native solution for transforming data before it’s used in analytics or modeling. It provides intelligent recommendations using machine learning to suggest common transformations, and allows teams to prepare data through a mix of visual steps and scripting.

Often used in enterprise environments, the platform supports large-scale data prep, integration, and quality control. Teams can import data from structured or semi-structured sources, apply cleansing and enrichment rules, and export directly to BI platforms or data warehouses. Built-in audit trails and access controls help teams stay aligned with internal policies and compliance requirements—an important consideration in industries with more complex data environments.

7. Dataiku

Dataiku offers a collaborative environment for preparing data, building models, and operationalizing AI. Its visual flow interface allows teams to clean, join, enrich, and transform data with minimal coding—while also giving more technical teams access to notebooks for Python, R, and SQL. This flexibility supports collaboration between analysts, data scientists, and domain experts working on the same project.

With built-in version control, automation, and integration across common cloud and on-premises systems, Dataiku helps teams accelerate machine learning workflows and streamline data prep at scale. It’s commonly used in predictive modeling, segmentation analysis, and forecasting projects where teams need both transparency and control. It also supports AI planning and forecasting, helping teams prepare historical data for forward-looking models.

8. Qlik Sense

Qlik Sense is a self-service analytics platform that includes built-in data preparation capabilities in its associative engine. Teams can load and combine data from different sources, apply transformations, and explore results in memory for faster performance. The built-in prep layer helps teams shape information as they build data visualizations—without switching between tools.

Qlik Sense supports calculated fields, data modeling, and visual pipelines, making it useful for those who want to prep and analyze data in the same environment. With a focus on autonomy and guided analytics, Qlik helps people work with data more directly and discover relationships that may not be visible in linear queries.

9. Datameer

Datameer is a cloud-native data preparation and exploration tool that helps teams working in modern data stacks—especially those using cloud warehouses—shape and organize data with more flexibility. It offers a spreadsheet-style interface combined with SQL support, allowing both technical and non-technical team members to collaborate on prep tasks.

With features like version control, data profiling, and preview capabilities, Datameer supports transparency and iteration throughout the data lifecycle. Teams can build reusable transformation pipelines and publish clean data sets directly to analytics platforms. It’s often used by analytics engineers and operations teams working with large or complex cloud-based data sets.

10. AWS Glue DataBrew

AWS Glue DataBrew is a visual data preparation tool built within the AWS ecosystem. It enables teams to clean and normalize data using more than 250 built-in transformations without writing code. With guided steps for filtering, joining, parsing, and formatting, DataBrew is designed for analysts and data engineers who need to prep data at scale.

DataBrew integrates directly with Amazon S3 and other AWS services, making it easy to prep data where it already lives. Teams can automate workflows, monitor job history, and publish outputs to analytics or machine learning pipelines. Its compatibility with AWS Glue and other cloud-native tools supports use cases that require scale, structure, and repeatability.

From raw data to insights with Domo

As data volumes continue to grow and real-time decisions become the norm, your teams deserve more than just a basic data prep tool—they need a complete solution. Domo brings the entire journey together, from data preparation to dashboards to AI-driven insights, all together in one user-friendly platform. Built for every role and skill level, Domo is how your teams move with clarity and confidence.

‍Contact us to learn how Domo can support your team’s data prep goals.