Ask On Data

Data engineering traditionally requires coding skills in SQL, Python or similar languages, which locks out business analysts, data scientists and other non-technical users from building their own data pipelines. Teams have had to rely on data engineers to write and maintain complex pipeline code for tasks like data integration, cleaning, wrangling and transformation. This creates bottlenecks and slows down time to insight, especially for smaller teams without dedicated engineering resources. Existing tools also carry cost and learning curve burdens that make pipeline development slow and expensive.

Ask On Data offers a chat based interface, powered by fine tuned LLMs, that converts plain English instructions into working data pipelines, so users can perform data integration, cleaning, wrangling and transformations without writing code. It supports varied data sources including flat files, APIs, databases, data lakes, data warehouses and log files, and generates Apache Spark jobs under the hood that can be orchestrated and scheduled with options for full load, incremental load or truncate and load. Users get real time data previews to validate each transformation, a full action history with undo functionality, and for more control they can inspect or edit the underlying YAML, enable a SQL plugin, or write full Spark/Python code for edge cases. The product is open source and can be self hosted for free, or used as a managed cloud service, with an enterprise tier priced by data volume.