ask-on-data

ask-on-data

Ask On Data is an open source, GenAI-powered chat-based data engineering tool that enables users to create and manage data pipelines using natural language commands, eliminating the need for coding skills. It is designed for both technical and non-technical users to simplify and accelerate data engineering tasks.

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Freemium Enterprise
#230 in Business Intelligence (230 tools)
Added 0 year ago
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Quick Overview

Best for: Business Intelligence

What it does

Business Intelligence software for decision-makers comparing workflow fit and alternatives.

Best fit

Business Intelligence

Pricing snapshot

Freemium from Free

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ask-on-data

Ask On Data is a pioneering next-generation data stack tool that leverages natural language processing (NLP) and GenAI to provide a chat-based interface for data engineering. It allows users to create, manage, and optimize data pipelines simply by conversing in plain English, removing the traditional complexities of coding. The platform supports users ranging from data scientists, engineers, analysts, to non-technical business users, democratizing access to data engineering capabilities. It also offers options for advanced users to write SQL, YAML, and Python for greater control. The tool supports various data sources including flat files, APIs, databases, data lakes, and warehouses, and provides managed cloud hosting as well as self-hosted open source options.

AI-powered, open source chat-based ETL tool for data engineering.

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Key Features

Data Pipeline Mastery

Create, manage, and optimize data pipelines seamlessly using an AI-powered chat interface without coding.

Chat Interface

A simple, intuitive chat-based interface that allows users to build data pipelines with no technical knowledge or learning curve.

Managed Service on Cloud

Available as a managed cloud service, removing the need for infrastructure management, with options for cloud deployment.

Action History and Undo Functionality

Comprehensive history tracking of all data actions with the ability to undo changes.

Data Preview

Real-time data preview with every transformation instruction to validate changes before committing.

Job Scheduling

Create end-to-end data pipelines and schedule jobs with options for full load, incremental load, truncate and load, etc.

Cost Effective

Offers over 80% cost savings compared to other data engineering tools.

Code Control

Advanced users can write and edit SQL, Python, and YAML files for handling edge cases and custom logic.

Support for Multiple Data Sources

Supports flat files, APIs, databases, data lakes, data warehouses, and log files with custom data source support available.

Pricing

Free Tier Available

Open source self-hosted version is fully free with community support and scheduling features.

Open Source Self Hosted

Free
  • Supports all databases as source and destination
  • Scheduling feature available
  • Community support
  • Manual deployment of patches and upgrades

Managed Cloud Hosting (Free Tier)

Free
  • Excel & CSV support only (5MB limit)
  • No job scheduling
  • Community support
  • Automatic upgrades, patches, backups, and monitoring

Enterprise

Contact [email protected] for pricing
  • Managed cloud hosting
  • Supports all databases as source and destination
  • Job scheduling
  • Enterprise support

Use Cases

Data Pipeline Creation for Non-Technical Users

Enables business analysts and decision-makers to create and manage data pipelines without coding knowledge.

Streamlining Data Engineering Workflows

Allows data engineers and analysts to accelerate pipeline development using natural language commands.

Data Preparation for Data Scientists

Empowers data scientists to preprocess and transform data independently for machine learning and AI tasks.

Cost-Effective Data Integration

Helps organizations reduce data engineering costs by over 80% compared to traditional tools.

Managed Cloud Data Engineering

Provides managed cloud hosting for organizations that prefer not to handle infrastructure.

Integrations

Apache Spark

Data pipelines are created as Apache Spark jobs for scalable data processing.

Langchain

Used for natural language processing and AI capabilities.

Olama

Part of the AI technology stack supporting the platform.

Airbyte

Supports data integration and connectors.

SQL Plugin

Enables writing full SQL code within the platform for advanced data operations.

Benefits

Eliminates the need for coding skills with a natural language chat interface.
Accelerates data pipeline development, saving time and resources.
Accessible to users of all technical backgrounds, democratizing data engineering.
Supports a wide range of data sources and complex data operations.
Offers cost-effective solutions with significant savings over traditional tools.
Provides managed cloud hosting to reduce infrastructure overhead.
Includes undo functionality and real-time data previews for error reduction.
Allows advanced users to customize pipelines with SQL, Python, and YAML.

Limitations

Managed cloud free tier supports only Excel and CSV files up to 5MB with no job scheduling.
Advanced custom data source support requires contacting support.
The GitHub repository is currently empty and will be uploaded shortly.

Frequently Asked Questions

What is Ask On Data?
Ask On Data is an open source, GenAI-powered chat-based data engineering tool that allows users to create data pipelines using plain English commands without coding.
Who can benefit from Ask On Data?
It benefits data scientists, data engineers, analysts, business users, and even non-technical individuals by simplifying data pipeline creation and management.
How does the chat-based interface work?
The interface uses fine-tuned large language models to interpret natural language commands and convert them into actionable data pipelines.
Can I perform complex data operations?
Yes, you can perform data integration, cleaning, wrangling, custom calculations, and transformations through the chat interface.
Is the data processing real-time?
You get real-time data previews during pipeline creation, but actual data processing happens when jobs are triggered or scheduled.
How can I trust the AI-generated pipelines?
You can view, validate, and edit the generated YAML files, write SQL or Python code, and preview data transformations before running jobs.
What is the pricing model?
The open source self-hosted version is free. Managed cloud hosting has a free tier with limitations, and enterprise pricing is available upon request.
What technologies is Ask On Data built on?
It is built on Python and React, using Apache Spark for data processing, and incorporates Langchain, Olama, and Airbyte.

Getting Started

  1. 1 Step 1: Register for a free trial or download the open source self-hosted version.
  2. 2 Step 2: Connect your data sources such as databases, files, or APIs.
  3. 3 Step 3: Use the chat interface to create and customize data pipelines using plain English commands.
  4. 4 Step 4: Preview data transformations in real-time and validate changes.
  5. 5 Step 5: Schedule and orchestrate data pipeline jobs as needed.
  6. 6 Step 6: Optionally, write or edit SQL, Python, or YAML for advanced control.

Support

email

Contact support via email at [email protected] for demos, pricing, custom implementations, or partnerships.

docs

Documentation is available on the website under the Docs section.

API

Available: No
Documentation:

No specific API documentation mentioned.

Rate Limits:

Not available.

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