Best AI Tools for Data Engineering [Free + Paid]
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Ever feel stuck staring at numbers, not knowing what to do next? You’re not alone. Data can be confusing, but with the right tools, it gets a whole lot easier.
Today, AI is helping people understand data faster and better. You don’t need to be a tech expert. These tools do the heavy lifting for you.
Just upload your file, ask a question, and get smart answers in seconds.
In this guide, we’ll show you the Best AI tools for data engineers that are both free and paid. Whether you’re a beginner or a pro, these tools will help you save time and make better decisions.
Top 10 Beginner-Friendly AI Tools – Overview
Here’s an overview of the top 10 AI Tools for beginners:
| S.No. | AI Tool Name | Ease of Use | Pricing | Link |
|---|---|---|---|---|
| 1 | ChatGPT (Code Interpreter & GPTs) | Easy | $20 pm | Get Started |
| 2 | Dataiku | Moderate | $840 yearly | Get Started |
| 3 | Hevo Data | Easy | $239 pm | Get Started |
| 4 | Alteryx | Moderate | $5,195 yearly | Get Started |
| 5 | Einblick | Easy | $8.25 pm | Get Started |
| 6 | Tecton | Moderate | Custom | Get Started |
| 7 | Mozart Data | Easy | $1,200 pm | Get Started |
| 8 | Delphi Labs | Easy | $99 pm | Get Started |
| 9 | PromptLoop | Easy | $39 pm | Get Started |
| 10 | Keboola | Moderate | $250 pm | Get Started |
Top 10 AI Tools for Data Engineers
Here are the best AI tools for data engineers.
1. ChatGPT (via Code Interpreter & GPTs)
ChatGPT is an AI assistant that helps with code generation, data transformations, and natural language processing tasks.
It is mainly used in data engineering to generate ETL scripts, SQL queries, and automate documentation or data explanations.
Key Features:
- Code Interpreter (Advanced Data Analysis) for Python-based data workflows
- Natural language to SQL, Python, or documentation
- GPTs can be customized for specific data engineering workflows
- Integration with third-party tools via plugins
- Handles data cleaning, transformation, and statistical analysis
Use Cases:
- Automating ETL pipeline code generation
- Writing or debugging SQL queries
- Data profiling and transformation scripts
- Explaining and documenting datasets
Ease of Use: Easy
Pricing:
- Free version available with limited capabilities
- Paid plans range from $20/month to $60/month for Pro and Team access
Pros:
- Requires no installation or setup
- Excellent for non-programmers or junior data engineers
- Fast and versatile for many data tasks
Cons:
- Limited access to files and databases in the free version
- Can make logical or technical mistakes without human review
- Not ideal for real-time or large-scale production tasks
2. Dataiku
Dataiku is an end-to-end data science and machine learning platform that enables users to build, deploy, and manage AI applications.
It is primarily used for collaborative data preparation, automated machine learning, and operationalization of AI projects.
Key Features:
- Visual data preparation and AutoML capabilities
- Support for Python, R, and SQL scripting
- Integrated LLM Mesh for generative AI applications
- Enterprise-grade security and governance features
- Scalable deployment options across cloud and on-premises
Use Cases:
- Building and deploying predictive models
- Automating data pipelines and workflows
- Collaborative analytics across teams
- Operationalizing AI solutions at scale
Ease of Use: Moderate
Pricing:
- Free edition available with limited features
- Paid plans start at approximately $840–$1,260 per user per year
Pros:
- Comprehensive platform for end-to-end AI projects
- Strong collaboration and governance tools
- Flexible deployment options
Cons:
- Steeper learning curve for beginners
- Higher cost for enterprise features
- Requires infrastructure setup for on-premises deployment
3. Hevo Data
Hevo Data is a no-code data pipeline platform designed to automate data integration from various sources to destinations.
It is mainly used for real-time data replication, transformation, and loading without manual coding.
Key Features:
- Support for 150+ data sources and destinations
- Real-time data replication with change data capture
- Automated schema mapping and transformation
- Built-in monitoring and alerting systems
- Scalable architecture for large data volumes
Use Cases:
- Setting up real-time data pipelines
- Migrating data across cloud platforms
- Automating ETL processes
- Data warehousing and analytics
Ease of Use: Easy
Pricing:
- Free plan available with up to 1 million events per month
- Paid plans start at $239 per month, varying based on data volume and features
Pros:
- User-friendly interface with no coding required
- Supports a wide range of integrations
- Real-time data processing capabilities
Cons:
- Limited customization for complex transformations
- Pricing can escalate with higher data volumes
- Some advanced features may require technical knowledge
4. Alteryx
Alteryx is a data analytics platform that combines data preparation, blending, and advanced analytics.
It is primarily used for automating data workflows and performing predictive analytics without extensive coding.
Key Features:
- Drag-and-drop workflow interface
- Integration with various data sources
- Built-in predictive and statistical tools
- Support for Python and R scripting
- Collaboration and sharing capabilities
Use Cases:
- Data cleansing and transformation
- Developing predictive models
- Automating reporting processes
- Customer segmentation and analysis
Ease of Use: Moderate
Pricing:
- Free trial available
- Paid plans start at $5,195 per user per year, with enterprise pricing varying based on features
Pros:
- Intuitive interface for building workflows
- Comprehensive analytics capabilities
- Strong community and support resources
Cons:
- High cost for individual users
- Steep learning curve for advanced features
- Limited real-time data processing capabilities
5. Einblick
Einblick is a collaborative data science platform that offers a visual interface for data exploration and modeling.
It is mainly used for rapid prototyping of data workflows and facilitating team collaboration on data projects.
Key Features:
- Visual canvas for data workflows
- Support for code and no-code interactions
- Real-time collaboration features
- Integration with popular data sources
- Built-in machine learning tools
Use Cases:
- Collaborative data exploration
- Rapid development of data models
- Educational purposes in data science
- Data storytelling and presentations
Ease of Use: Easy
Pricing:
- Free-forever plan available
- Paid plans start at $8.25 per user per month
Pros:
- User-friendly visual interface
- Facilitates team collaboration
- Quick setup and deployment
Cons:
- Limited scalability for large datasets
- Fewer advanced analytics features compared to competitors
- Relatively new in the market with evolving features
6. Tecton
Tecton is a feature store platform designed to manage and serve machine learning features in production.
It is primarily used to streamline the process of building, deploying, and monitoring features for real-time ML applications.
Key Features:
- Centralized feature management
- Support for real-time and batch data processing
- Integration with popular ML frameworks
- Automated data validation and monitoring
- Scalable architecture for enterprise needs
Use Cases:
- Managing features for ML models
- Ensuring consistency between training and serving data
- Accelerating model deployment cycles
- Monitoring feature performance in production
Ease of Use: Moderate
Pricing:
- Custom pricing based on enterprise requirements
- No public pricing information available
Pros:
- Enhances ML model reliability
- Supports both real-time and batch processing
- Facilitates collaboration between data and ML teams
Cons:
- Requires integration with existing ML infrastructure
- May have a learning curve for new users
- Pricing transparency is limited
7. Mozart Data
Mozart Data is a modern data platform that combines ETL, a data warehouse, and transformation tools.
It is mainly used to centralize and prepare data for analysis without extensive engineering resources.
Key Features:
- Pre-built connectors for various data sources
- Automated data cleaning and transformation
- Integrated data warehouse
- SQL-based interface for data analysis
- Monitoring and alerting for data pipelines
Use Cases:
- Setting up a modern data stack quickly
- Data consolidation for analytics
- Automating data workflows
- Enabling self-service analytics for teams
Ease of Use: Easy
Pricing:
- 14-day free trial available
- Paid plans start at $1,200 per month, with additional implementation fees
Pros:
- Quick setup with minimal engineering effort
- Comprehensive solution for data needs
- User-friendly interface
Cons:
- Higher starting price point
- May not be suitable for complex data transformations
- Limited customization options
8. Delphi Labs
Delphi Labs provides AI agents that write and maintain SQL queries, dbt models, and data documentation automatically.
It is mainly used to reduce manual workload in data pipeline maintenance and enhance SQL workflow automation.
Key Features:
- AI-generated SQL and dbt code
- Automatic updates and maintenance of queries
- Version control and Git integration
- Real-time collaboration with team members
- Integrated with Snowflake, BigQuery, and more
Use Cases:
- Automating dbt and SQL query generation
- Refactoring legacy SQL pipelines
- Generating documentation for data models
- Reducing dependency on manual data engineering
Ease of Use: Easy
Pricing:
- Free trial available
- Paid plans typically start around $99–$499/month depending on usage and team size
Pros:
- Saves time by automating repetitive SQL tasks
- Simplifies data pipeline maintenance
- Supports modern data stacks (dbt, Snowflake, etc.)
Cons:
- Still evolving—may lack support for niche use cases
- Limited to SQL/dbt-focused environments
- Might need manual review for complex logic
9. PromptLoop
PromptLoop is an AI-powered tool that integrates with spreadsheets to automate data classification, transformation, and enrichment.
It is primarily used by data professionals to use LLMs inside Excel or Google Sheets for analysis and modeling.
Key Features:
- AI formula integration in Excel/Sheets
- Data classification, summarization, and prediction
- Custom prompts and templates
- Zero setup—no coding required
- Bulk data processing capabilities
Use Cases:
- Classifying product categories or sentiment
- Cleaning and transforming spreadsheet data
- Generating insights and summaries from text
- Automating repetitive spreadsheet operations
Ease of Use: Easy
Pricing:
- Free version available with limited usage
- Paid plans start at $39/month and go up to $199/month for teams
Pros:
- Extremely user-friendly
- Seamlessly integrates into spreadsheet workflows
- Powerful for light to moderate data tasks
Cons:
- Not suitable for large-scale or complex data pipelines
- Dependent on spreadsheet performance limits
- Limited control over model internals
10. Keboola
Keboola is a cloud-based data operations platform that helps automate data ingestion, transformation, and orchestration.
It is mainly used to unify data engineering workflows and manage complex data pipelines in one place.
Key Features:
- End-to-end data pipeline orchestration
- Pre-built connectors and transformations
- Metadata management and versioning
- AI-enhanced data catalog and lineage
- Collaboration and governance features
Use Cases:
- Automating data workflows across teams
- Data integration from multiple sources
- Managing ETL and reverse ETL operations
- Enhancing data quality and traceability
Ease of Use: Moderate
Pricing:
- Free tier available for small teams
- Paid plans start around $250–$1,000/month based on volume and features
Pros:
- Full-stack solution for data operations
- Scalable and flexible for growing teams
- Transparent and version-controlled workflows
Cons:
- Slightly complex initial setup
- Interface can feel technical to non-engineers
- Pricing increases with usage and connectors
Final Words
These best AI tools for data engineering that can change the way you work with data. Pick one that feels right for you and give it a try. Most of them are easy to use and super helpful. You’ll be surprised how much easier data becomes when AI has your back.
Frequently Asked Questions
1. What are the best AI tools for data engineers?
The best AI tools for data engineers include Apache Spark, Databricks, Apache Airflow, dbt, and ChatGPT for automating and optimizing data workflows.
2. How can AI tools help in data engineering?
AI tools help by automating ETL processes, enhancing data quality, predicting pipeline failures, and improving the scalability of data systems.
3. Are these AI tools suitable for beginners in data engineering?
Yes, many AI tools like ChatGPT, dbt, and Airflow have beginner-friendly interfaces, documentation, and community support.
4. How do I select the best AI tool for my data engineering project?
Choose based on your project needs, such as data volume, real-time vs batch, tool integrations, and your team’s technical skillset.
5. Are there free AI tools available for data engineering?
Yes, open-source tools like Apache Spark, Airflow, and dbt Core are free and widely used in the industry.
6. What skills do I need to start using AI tools for data engineering?
You need basic skills in Python/SQL, data pipeline concepts, and familiarity with cloud platforms or orchestration tools.
7. How can I learn to use AI tools for data engineering?
You can learn through online courses (like Coursera, Udemy), documentation, tutorials, and hands-on practice with open-source tools.
Thirumoorthy serves as a teacher and coach. He obtained a 99 percentile on the CAT. He cleared numerous IT jobs and public sector job interviews, but he still decided to pursue a career in education. He desires to elevate the underprivileged sections of society through education
Thirumoorthy serves as a teacher and coach. He obtained a 99 percentile on the CAT. He cleared numerous IT jobs and public sector job interviews, but he still decided to pursue a career in education. He desires to elevate the underprivileged sections of society through education
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