February 16th, 2026
The 13 Best Data Preparation Tools: A Complete Guide for 2026
By Simon Avila · 31 min read
What are data preparation tools?
Data preparation tools are software platforms that clean and organize raw data so you can use it for analysis, reporting, or modeling. You use them to fix errors, remove duplicates, standardize formats, join tables, and combine data from multiple sources before building dashboards or running queries.
These tools sit between your raw data and your dashboards or reports. Without preparation, small issues like formatting problems or duplicates can slip into your analysis and affect the results.
13 Best data preparation tools: At a glance
Tool | Best For | Starting price | Key strength |
|---|---|---|---|
Quick data prep and analysis in one workflow for non-technical users | Handles cleaning, joining, and reshaping with natural language prompts during analysis | ||
Repeatable visual workflows at scale | Drag-and-drop transformation logic | ||
Teams managing prep, modeling, and deployment together | Full platform with collaboration features | ||
Technical teams building custom pipelines | Open-source flexibility with wide connector support | ||
Excel and Power BI users | Included with Excel and Power BI (Power BI starts at $14/user/month) | Built into familiar Microsoft tools | |
Enterprise governance and compliance needs | Centralized control for large organizations | ||
Tableau users preparing data for dashboards | Included with a Tableau Creator license at $75/user/month | Tight integration with Tableau workflows | |
Cloud-first teams using Alteryx | Browser-based with cloud deployment | ||
Enterprises running complex ETL and large data workloads | Mature system for long-term environments | ||
Teams working inside AWS | Serverless data preparation across AWS services including S3, Redshift, RDS, and others | ||
Analysts building visual workflows on a budget | $19/month, billed monthly | Node-based automation with open-source flexibility | |
BI teams needing prep inside their dashboard platform | Combined prep and visualization environment | ||
Manual and project-based data cleanup | Local processing with full transparency |
1. Julius: Best for light data prep during analysis
What it does: Julius is an AI-powered data analysis platform that works with your existing data as you ask questions. It connects to databases like Postgres, Snowflake, and BigQuery, then generates the joins, aggregations, and formatting needed to return charts and summaries. With repeated use, it retains how your tables relate to reduce repeated setup.
Who it's for: Business teams managing structured data who need analytical insights without SQL expertise.
We built Julius to make analysis more direct while handling common data cleanup along the way.
When you ask "Show sales by region for last quarter," Julius identifies the relevant tables, applies joins, standardizes date fields, and returns a chart. You’re not designing separate transformation workflows first, because Julius focuses on handling the joins and formatting needed for each question rather than building standalone data pipelines.
Julius retains how your tables connect, reducing the need to manually map relationships each time. After a few queries, the platform recognizes that customers link to orders or that revenue ties to specific date columns, so similar questions require less setup.
Notebooks let you save these analyses and rerun them on a schedule or manually when new data arrives. This helps keep recurring reports consistent without rebuilding the same query logic each time.Key features
Prep during analysis: Applies joins and basic formatting while answering questions instead of requiring separate workflows
Connected data sources: Links to Postgres, Snowflake, BigQuery, and more
Reusable Notebooks: Save analyses with embedded prep logic and rerun on a schedule
Scheduled reporting: Set reports to update and deliver results by email or Slack
Retained context: Remembers table relationships to reduce repeated setup across queries
Pros
Prep happens while you analyze, not as a separate workflow
Clear visual summaries with minimal configuration
Less rework on repeated queries as context builds over time
Cons
Built more for business analysis than complex ETL (extract, transform, load) pipelines
Works best with connected, structured data sources
Pricing
Julius starts at $20 per month.
Bottom line
2. Alteryx: Best for repeatable visual workflows at scale
What it does: Alteryx is a desktop data preparation platform that lets you build visual workflows by dragging and dropping transformation steps. You can connect data sources, apply cleaning rules, join tables, and export results without writing code. Workflows save automatically so you can rerun the same prep logic on updated data.
Who it's for: Analysts and data teams that need structured, repeatable prep at scale.
Alteryx is the data prep tool that many teams compare everything else against. During testing, I built workflows that combined sales data from multiple spreadsheets, removed duplicates, standardized date formats, and exported clean files for reporting. The drag-and-drop canvas made it easy to see each transformation step in order.
Once I built the workflows, they scaled smoothly across new data. I reran the same prep logic on monthly updates without adjusting formulas or steps, which made it easier for teams to apply the same transformations across similar datasets.
The interface requires some learning time upfront, but the visual layout made troubleshooting easier than hunting through code. I could click any step and see exactly what changed in the data at that point.Key features
Visual workflow builder: Drag transformation steps onto a canvas to build prep logic without code
Repeatable processes: Save workflows and rerun them automatically on new data
Wide connector support: Pull data from databases, cloud platforms, spreadsheets, and APIs
Pros
Strong for structured, recurring prep tasks
Visual interface shows transformation logic clearly
Handles moderate to large datasets reliably
Cons
Licensing cost adds up for teams
Requires setup time and training upfront
Pricing
Bottom line
3. Dataiku: Best for teams managing prep, modeling, and deployment together
What it does: Dataiku is a full data platform that handles preparation, modeling, collaboration, and deployment in one workspace. You can clean and transform data through visual tools or code, build machine learning models, and share projects with team members. It supports both analysts who prefer drag-and-drop interfaces and engineers who write Python or SQL.
Who it's for: Teams that want a platform approach covering prep, analysis, and deployment rather than point tools.
I tested Dataiku on a project that required both data cleaning and predictive modeling. The platform let me switch between visual prep tools and code notebooks without leaving the workspace, keeping everything organized in one place. I could build transformation flows visually, then hand off prepared data to a data scientist who added Python-based models in the same project.
The collaboration features helped when multiple people needed access to the same workflows. I could see version history, leave comments on specific steps, and track who changed what. This made review cycles faster than emailing files back and forth.
Dataiku's scope means it takes longer to learn than single-purpose prep tools. The interface offers many options, which felt overwhelming at first but became useful once I understood how the pieces connected.
Key features
Combined prep and modeling: Handle data cleaning, transformation, and machine learning in one platform
Visual and code workflows: Switch between drag-and-drop tools and Python or SQL as needed
Team collaboration: Share projects, track changes, and review work with built-in version control
Pros
Covers prep, modeling, and deployment without switching tools
Supports both visual and code-based workflows
Strong collaboration features for team projects
Cons
Steeper learning curve than standalone prep tools
Platform scope may feel heavy for teams that only need cleaning
Pricing
Bottom line
4. Qlik Talend: Best for technical teams building custom pipelines
What it does: Qlik Talend is a data integration and preparation platform that lets you build custom pipelines to move and transform data across systems. You can connect databases, cloud platforms, and applications, then design transformation logic using visual tools or code.
Who it's for: Technical teams that want control over how data moves and transforms across systems.
Testing Qlik Talend showed me it assumes technical comfort from the start. I built pipelines that pulled data from a Postgres database, applied transformations, and loaded results into a cloud warehouse. The visual designer helped map out steps, but understanding how data flowed between components required familiarity with ETL concepts and data structures.
Talend offers a wide range of connectors for databases, cloud platforms, and business tools. This made it easier to test different pipeline setups without building custom integrations. While testing, I focused on how the pipelines handled transformations and schema changes rather than spending time on configuration work.
Key features
Custom pipeline design: Build data movement and transformation workflows across multiple systems
Enterprise-grade pipelines: Design and manage data pipelines with monitoring, logging, and error handling built in
Wide connector support: Connect to databases, cloud platforms, and business applications
Pros
Flexible for technical teams that need pipeline control
Works well for teams managing complex data flows across multiple systems
Strong connector library for common data sources
Cons
Assumes technical knowledge of ETL and data structures
Requires ongoing maintenance as data sources change
Pricing
Bottom line
5. Microsoft Power Query: Best for Excel and Power BI users
lets you clean and transform data before analysis. You can connect to databases, spreadsheets, and web sources. Then, you can apply data prep steps like removing duplicates, filtering rows, and reshaping columns. The steps save automatically so you can refresh data with one click.
Who it's for: Excel and Power BI users who need repeatable cleaning steps without adopting new software.
I tested Microsoft Power Query by connecting it to sales spreadsheets that needed monthly cleanup. The interface opened inside Excel, so I didn't need to learn a completely new tool. I could filter out blank rows, split text columns, and change data types through the menu options. The same preparation steps also carry over when you use Power Query inside Power BI for shared reports.
Power Query recorded each step I applied, then let me refresh the data later without redoing the work manually. This saved time when the same cleanup needed to happen each month. The steps appeared in a list on the side, so I could see exactly what transformations were applied and adjust them if needed.Key features
Built into Excel and Power BI: Access prep tools directly inside familiar Microsoft applications
Repeatable transformations: Save cleanup steps and refresh data with updated files automatically
Multiple source connections: Pull data from databases, web pages, and cloud sources
Pros
No new software to learn for Excel users
Transformations save and refresh automatically
Works well with moderate-sized datasets
Cons
Performance drops with very large files
Advanced transformations may require M language knowledge
Pricing
Bottom line
6. Informatica: Best for enterprise governance and compliance needs
What it does: Informatica is an enterprise data management platform that handles preparation, integration, and governance across large organizations. You can build transformation workflows, enforce data quality rules, and track lineage to meet compliance requirements. It centralizes control over how data moves and transforms across systems.
Who it's for: Enterprise teams that need scale, governance, and strict data controls.
I used Informatica in a governance-focused environment where traceability mattered more than fast setup. The platform tracked transformation steps, data lineage, and access history, which helped teams meet compliance requirements that lighter tools couldn’t address.
It took me a while to learn how Informatica worked since it uses its own terms and layout. The setup was harder than tools like Alteryx, but it gave me centralized control and handled large data volumes reliably. I could set quality rules across multiple workflows so bad data got caught before it reached reports.Key features
Enterprise governance: Track data lineage, enforce quality rules, and maintain audit trails for compliance
Centralized control: Manage transformations and access permissions across the organization
Large-scale processing: Handle complex data volumes and enterprise workloads reliably
Pros
Strong governance and compliance features
Scales for enterprise data volumes
Centralized management across teams
Cons
Expensive licensing and maintenance costs
Steep learning curve and setup complexity
Pricing
Bottom line
Special mentions
The six tools above cover most common needs, but they’re not the only options. Some teams already work inside specific ecosystems or need something more specialized. Here are 7 other data preparation tools that can still be a good fit depending on your setup:
Tableau Prep: A data preparation tool for Tableau users who clean and shape data before building dashboards. It works best when prep and visualization stay inside the Tableau ecosystem and is rarely used on its own.
Alteryx Designer Cloud: A browser-based version of Alteryx for cloud-first teams that want visual workflows. It supports collaboration and cloud deployment, though it doesn’t yet match all desktop features.
IBM DataStage: A long-standing ETL platform used in many enterprise environments. It’s stable and reliable for large, established systems, but it isn’t built for fast setup or modern interfaces.
AWS Glue: A serverless data preparation service for teams already working in AWS. It fits S3 and Redshift workflows well, but it assumes cloud and engineering experience.
KNIME: A visual workflow tool for data prep, analysis, and machine learning using drag-and-drop nodes. Its open-source version lets analysts customize workflows and add code, making it a lower-cost alternative to Alteryx.
Domo: A business intelligence platform that includes data preparation to support dashboards and reports. The prep features work best when Domo is already your primary BI tool.
OpenRefine: A free, open-source tool for cleaning messy data on your local machine. It’s useful for one-off cleanup work but doesn’t scale well for ongoing pipelines or teams.
Key features to look for in data preparation tools
The features you need depend on whether you're cleaning spreadsheets occasionally or building pipelines that run daily. Some tools focus on visual interfaces for business users, while others assume you'll write code or manage enterprise-scale workflows.
Here are the core features to look for:
Data source connections
Transformation logic
Error detection
Workflow automation
Collaboration features
Data quality validation
Visual workflow builders
Export and scheduling
How I tested these data preparation tools
I tested each tool using mock datasets that matched its target users. Business-focused platforms have marketing and sales data with common formatting issues. Technical tools got database connections and multi-source joins that required more control.
Here's what I evaluated:
Setup speed: How long it took to connect data sources and start cleaning without technical help or documentation
Transformation clarity: Whether I could understand what each step did to my data and troubleshoot when the results looked wrong
Error handling: How well platforms caught duplicates, missing values, and format problems before I had to hunt for them manually
Repeatability: Whether I could save my work and apply the same cleanup steps to updated datasets without starting over
Performance under load: How tools handled datasets ranging from 10,000 rows to several hundred thousand rows
Learning curve for business users: How quickly someone comfortable with Excel could build useful workflows without coding experience
Which data preparation tool should you choose?
Your choice of data preparation tool depends on your team's technical skills, how often you need to clean data, and whether you're building one-off cleanups or repeatable pipelines.
Choose:
Julius if you want to clean data as you analyze it by asking natural language questions, without redoing setup work for similar requests.
Alteryx if your team needs visual workflows that apply the same transformations repeatedly and you have the budget for enterprise licensing.
Dataiku if you want a platform that handles prep, modeling, and deployment together rather than using separate tools for each step.
Qlik Talend if your technical team needs to build custom pipelines with full control over how data moves between systems.
Microsoft Power Query if you already work in Excel or Power BI and need repeatable cleaning steps without learning new software.
Informatica if your organization requires enterprise governance, audit trails, and centralized control over data transformations.
Tableau Prep if you're already using Tableau for dashboards and want prep tools that integrate directly with your visualization workflows.
Alteryx Designer Cloud if you prefer browser-based workflows and cloud deployment over desktop installations.
IBM DataStage if you're working with legacy enterprise infrastructure that requires long-term stability.
AWS Glue if your data lives in S3, Redshift, or other AWS services and you want serverless prep built for that ecosystem.
KNIME if you need visual workflow automation on a budget and want open-source flexibility with node-based design.
Domo if you're using Domo for business intelligence and need prep features inside the same platform.
OpenRefine if you have a one-off messy dataset to clean and want a free tool with full transparency over transformations.
My final verdict
I found that Alteryx works well for teams that want repeatable visual workflows, while Informatica and Qlik Talend fit organizations that need enterprise governance or custom pipeline control. Dataiku sits in the middle for teams that want both data preparation and modeling in one platform.
Julius takes a different approach by handling data preparation as part of analysis rather than as a separate workflow. You ask business questions and get clean results without rebuilding the same transformation logic each time. I think this approach works best when you’re analyzing connected databases and need answers that stay up to date as data changes.How Julius helps with data preparation
Data preparation tools usually require you to clean and transform data as a separate step before analysis. Julius handles cleaning and formatting while you ask questions, so you're not building transformation pipelines first and then querying later.
Here’s how Julius helps:
Direct connections: Link databases like Postgres, Snowflake, and BigQuery, or integrate with Google Ads and other business tools. You can also upload CSV or Excel files. Your analysis can reflect live data, so you’re less likely to rely on outdated spreadsheets.
Smarter over time with the Learning Sub Agent: Julius's Learning Sub Agent learns your database structure, table relationships, and column meanings as you use it. With each query on connected data, it gets better at finding the right information and delivering more accurate answers without manual configuration.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Ready to see how Julius can help your team make better decisions? Try Julius for free today.
