You have a massive data set to manage. What’s the best spreadsheet tool for the job?

For moderately large datasets, Excel is quite efficient, especially with its Power Query and Power Pivot features that enhance its data handling capabilities.

Microsoft Excel or Google Sheets are easy-to use tools that come in handy. You can leverage these as a starting point, then scale up to more sophisticated tools.

The best spreadsheet tool for managing large data sets depends on your specific needs and preferences. Microsoft Excel is a powerful choice, offering features like Pivot Tables and advanced functions for data manipulation. However, other options like Google Sheets and LibreOffice Calc also serve well for handling data analysis tasks.

In my experience, I have used Excel extensively to manage data. For data analysis, Excel is very helpful. When you have to present data, Excel can show what your audience is looking for. Excel is user friendly and will be my go to solution.

I use Excel everyday in my current role. The tool is a necessity and is something I think many should learn regardless of what type of business or functional role you're in. It's a universal tool that can help organize, parce, format, and visualize your data for whatever its needed for. I've been using Power Query within the tool and it has helped shape more of my understanding of cleaning and editing data and in turn using that data to present any findings that are important to the business I'm currently in.

I do not like Google sheets orjer that. You can export to excel. Problem i have with Google sheet lack if capability unlike excel where I can do more.

I'm not as savvy with Google Sheets as I am with Excel as it's not a tool I use on the job. I have used it for personal reasons but I'm not as in tuned with the functionalities. I'm sure if I worked at a company that used it, there could definitely be some potential benefits. I do like that multiple people can be in Google files at once as it helps with collaboration more than Excel does but I'm sure depending on where you are, security issues can arise. Any company needs security and safe-keeping especially for sensitive information so I think that's something to be mindful of when using Google Sheets.

Not many companies use Google sheets due 5o being cloud based, and yes lack functionality, I use to to convert my own bank reconciliations to excel

Converting excel to data base through excel converted to access or transferring to python. You can import tables. Formulas, pivot charts, charts, and even macros to a database even into oracle

Using excel when dealing with large data sets is the best way to go. From a wide variety of templates offered, to being able to create your own sheets for the data on hand. Excel also offer the tools to translate your data sets into visual charts, graphs, pivot tables and more.

For managing "massive datasets", a database management system like SQL, PostgreSQL, or a specialized tool like Apache Hadoop would be the best approach.

SQL Server is a relatively simple program that can be used to load, clean, manipulate, and analyze massive datasets. Additionally, it doesn't require a hugely technical background to get started. I would use SQL for any data set that is more than a couple hundred thousand rows.

As fond as I (an accountant) of spreadsheets, a spreadsheet is not always the best tool. Who was it that said (I don't recall the exact wording but something very much like) if your only tool is a hammer all your problems look like nails?

Alteryx allows for the handling and manipulation of massive datasets with a well structured GUI and a solid community of problem solvers that have likely attempted what you’re trying to solve. Couldn’t recommend it more.

There many softwares solution and also plugin to Excel, but SQL is the most affordable way to go. In the context of big data, SQL is often used in conjunction with distributed data processing frameworks like Apache Hadoop and Apache Spark. Here are some ways SQL can be used in big data environments: - Platforms like Apache Hive allow you to use SQL-like queries to analyze data stored in Hadoop Distributed File System (HDFS). - Query languages, such as HiveQL, provide a familiar SQL interface for users to interact with large-scale datasets in Hadoop clusters.