horizontal lines
Gigasheet Primary logo
  • Payal Gusain

Alternatives to Excel for Big Data

Big Data can make big Excel users cry. On a good day, the good-old spreadsheet can process hundred thousand rows of data without freezing.

On most days, the spreadsheet is woefully slow.

It still works for simple use cases, ranging from accounting to project management. But at a time when we are generating 2.5 quintillion bytes of data every single day? Probably not.

There is a reason data is hailed as the new oil. In the industrial context, big data can make big waves. We’re talking the ability to unravel entire DNA sequences within a few minutes, the ability to provide value-based healthcare, and the ability to hack the human mind with algorithms.

And so, for knowledge workers from across educational backgrounds, Excel is a powerful analytics tool. But probably because they haven’t used Gigasheet before.

Before we explain why, let’s first address the elephant in the room.

Hold up! What Do We Mean When We Say Big Data?

We mean what Wikipedia means.

Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software.

We mean data files too big to be handled by a single computer, even with a 1 terabyte hard drive in possession. And not necessarily data files “too big for excel”.

We’re talking data points in millions and billions. The kind that’ll give your M1 Pro MacBook a run for its worth. Sad, but true.

Or as Roger Magoulas defines it:

Big data is when the size of the data becomes part of the problem.

And that’s a problem Excel cannot solve for many.

The Problem with Excel

For a majority of folks, there is no tool quite like Excel. It lets you manipulate and visualize data down to a single cell, and in the formats you prefer.

Yet — it is horribly limited.

In MS Excel, the maximum row limit is 1,048,576 and the maximum column limit is 16,384. It’s the same for Microsoft 365, Excel 2021, Excel 2019, Excel 2016, Excel 2013, Excel 2010, and Excel 2007.

This limitation, given the burgeoning speed at which data is not only generated but appended, is a poorly delivered joke.

Now wait till you read this: Public Health England blamed on Excel’s row limit for a data error, because of which nearly 16000 track and trace records for Covid positive tests were left out from the official figures.

Excel is not only slow and crashes, but it’s a scapegoat as well. On top of it all, it’s not a database, which means you can’t scale as well as you’d like with Excel. You’d have to rely on a Microsoft SQL server and Power BI if millions of rows are involved.

Other Alternatives

The other common alternative for big data analysis is the many Python libraries like Pandas. While R and Java are good too, they are not as flexible. With Python, you can handle and manipulate large sets of data efficiently.

But if you’re not used to the animal, you will be in a lot of pain. And regretting not taking that coding class after all.

On the contrary, even if you do know the languages, remembering all the good syntax and formula is impossible. And often, it won’t just be SQL you’re dealing with. It will be Oracle SQL Vs. Microsoft SQL Vs. PostgreSQL. Too much hoopla and none of the work.

You can always Google for help. Go to the superior web beings to put you out of your misery. But this road is long, winding and might we add — unnecessary? Especially when you have a spreadsheet-like but incredibly powerful tool to analyze, manipulate, and query big data in less than 5 seconds.

Enter Gigasheet.

Process Big Data in Less Than 5 Seconds with Gigasheet

Don’t believe our word for it. Watch a quick comparison between Python and Gigasheet for a JSON data analysis to understand.

From humongous LOG files to CSV files, Gigasheet can crunch large datasets into valuable insights with the same flexibility and convenience as Excel. No code or database required. And it only takes 3 steps to get started.

  1. Create a FREE account on Gigasheet. No credit card needed.

  2. Import the data file using the File Upload option, either from your computer or a cloud storage option like Dropbox.

  3. Then, filter, group, and explore data as much as you like.

To learn more, check out the resources below:

If you’d like to try out the tool, no strings attached, you can look up the public datasets ready for exploration in our Data Community:

Author Bio: Payal Gusain leads growth initiatives at Ukti. When she is not weaving interesting narratives for clients, she is usually annoying her dog or dissecting lyrics by Frank Ocean.

Recent Posts

Explore sample data from IMDB