Big Data can make big Excel users cry. On a good day, the good-old spreadsheet can process one hundred thousand rows of data without freezing. On most days, the spreadsheet is woefully slow. In fact, Excel can't open a basic CSV file with more than roughly 1M rows (as an aside, check this out if you're looking for a comparison of online CSV viewers).
Excel still works for many 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, does Excel make sense for Big Data analysis? Probably not.
There is a reason data is hailed as the new oil. We have thousands of users analyzing data in spreadsheets that are far too big for Excel. Gigasheet supports a wide variety of cases including analysis of SEO and SEM data, optimization of Shopify stores, exploration of healthcare transparency data, and sales and marking lead analysis.
Excel is the lingua-franca for knowledge workers globally, and is no doubt a powerful analytics tool. But perhaps it's time for a new approach. One that combines the ease of use of spreadsheets with the power of a big data analytics database. Enter Gigasheet.
Before we go further, let's make sure we're all on the same page when it comes to 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.
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.
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.
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 coding and command line interfaces, 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.
Don’t take our word for it, watch a quick comparison between Python and Gigasheet for a JSON data:
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.
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.