Gigasheet: Effortlessly Analyse Upto 1 Billion Rows Without Any Code

The no-code Pandas alternative at scale.

Traditional Python-based tools become increasingly ineffective and impractical as you move towards scale.

Python-based solutions on small datasets vs. large datasets

Such cases demand:

• appropriate infrastructure for data storage and manipulation.

• specialized expertise in data engineering, and more.

…which is not feasible at times.

Gigasheet is a no-code tool that seamlessly addresses these pain points.

Think of it like a combination of Excel and Pandas with no scale limitations.

As shown below, I used Gigasheet to load a CSV file with 1 Billion rows and 47 GB in size, which is massive.

Loading 1B rows with Gigasheet.

You can perform any data analysis/engineering tasks by simply interacting with a UI.

Thus, you can do all of the following without worrying about any infra issues:

• Explore any large dataset — even as big as 1 Billion rows without code.

Loading 1 Billion Rows with Gigasheet.

• Perform almost all tabular operations you would typically do, such as:

  

  Execute tabular data operations

  • merge,

  • plot,

  • group,

  • sort,

  • summary stats, etc.

• Import data from any source like AWS S3, Drive, databases, etc., and analyze it, and more.

What’s more, using Gigasheet’s Sheet Assistant, you can also interact with your data by providing text instructions.

Lastly, Gigasheet also provides an API. This allows you to:

• automate any repetitive tasks

• schedule imports and exports, and much more.

To summarize, Gigasheet immensely simplifies tabular data exploration tasks.

Anyone with or without data engineering skills can use Gigasheet for tabular tasks, directly from a UI.

Isn’t that cool?

👉 Get started with Gigasheet here: Gigasheet.

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