Ask On DataOpen-source GenAI chat-based tool for data engineering and pipeline workflows.

4.8 (6)

Reviewed by Daniel Nikulshyn·Updated July 2026

Open Source Data Engineering GenAI ETL/ELT Self-Hosted No-Code Conversational AI

Overview

Ask On Data is an open-source, GenAI-powered chat-based tool for data engineering and pipeline workflows. It allows users to create, manage, and optimize data pipelines using a simple AI-powered chat interface, without the need for coding skills. The tool offers a range of features, including data pipeline mastery, managed service on cloud, action history and undo functionality, data preview, and cost-effective pipelines. It also supports varied data sources, such as flat files, APIs, databases, data lakes, and data warehouses. With options to write SQL, Python, and YAML, users can have more control and make changes as needed. Ask On Data aims to revolutionize data engineering by making it accessible, intuitive, and incredibly powerful for users of all backgrounds.

Key features

Chat-based data workflow creation
GenAI-assisted query and transformation generation
Support for multiple data sources and destinations
Data loading, cleaning, and transformation tasks
Open-source codebase for customization
Self-hosted deployment option

Pricing

Model: Free
Category: Data Analysis
Rating: 4.8 / 5 (6)

Use cases

Build ETL pipelines via chat

Data engineers can describe extraction, transformation, and loading steps in natural language to quickly assemble pipelines without writing extensive scripts.

Enable analysts to move data

Non-coding analysts can load and transform data across sources using a conversational interface, reducing dependence on engineering teams for routine tasks.

Self-hosted data workflows

Teams with strict governance needs can deploy the open-source tool on internal infrastructure and adapt it to their existing data stack and compliance requirements.

Clean and prepare datasets

Use GenAI-assisted transformations to clean, reshape, and standardize data from multiple sources before sending it to warehouses or analytics tools.

Pros & Cons

Pros

Open source and self-hostable
Natural language interface lowers technical barrier
Covers common data engineering tasks like ETL and transformations
Flexible for integration with existing data stacks

Cons

Requires setup and infrastructure to deploy
GenAI outputs may need validation for production pipelines
Smaller community compared to established ETL platforms

Reviews

4.8

Average from 6 ratings.

Ethan Brooks

Mar 21, 2026

Does the job

Pretty happy overall. Data loading, cleaning, and transformation tasks just works and flexible for integration with existing data stacks. but no dealbreakers — I'd recommend it to a friend without hesitating.

Liam O’Connor

Mar 11, 2026

Does the job

Pretty happy overall. Self-hosted deployment option just works and covers common data engineering tasks like ETL and transformations. but no dealbreakers — I'd recommend it to a friend without hesitating.

Grace Okafor

Dec 26, 2025

Solid for our team

We rolled this out across the team last quarter and open source and self-hostable. Self-hosted deployment option fits neatly into how we already work, and data loading, cleaning, and transformation tasks removed a step we used to do by hand. but it has held up under daily use.

Beatriz Costa

Dec 8, 2025

Does the job

Pretty happy overall. Self-hosted deployment option just works and covers common data engineering tasks like ETL and transformations. GenAI outputs may need validation for production pipelines can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Sanjay Gupta

Nov 7, 2025

Use it every day

Honestly didn't expect to like it this much. Data loading, cleaning, and transformation tasks is exactly what I needed, and flexible for integration with existing data stacks. I do wish genAI outputs may need validation for production pipelines, but I reach for it almost every day now and it just clicks.

Frank Müller

Oct 11, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: open-source codebase for customization and natural language interface lowers technical barrier. Where it lags: smaller community compared to established ETL platforms. On balance the feature set — especially chat-based data workflow creation — justifies the 4 stars for our use case.