Monday, June 2, 2025
News PouroverAI
Visit PourOver.AI
No Result
View All Result
  • Home
  • AI Tech
  • Business
  • Blockchain
  • Data Science & ML
  • Cloud & Programming
  • Automation
  • Front-Tech
  • Marketing
  • Home
  • AI Tech
  • Business
  • Blockchain
  • Data Science & ML
  • Cloud & Programming
  • Automation
  • Front-Tech
  • Marketing
News PouroverAI
No Result
View All Result

Streamline Big Data Analysis …With A Spreadsheet? 4 Transformations Made Easy

April 20, 2024
in Data Science & ML
Reading Time: 5 mins read
0 0
A A
0
Share on FacebookShare on Twitter



Data analysis is an essential part of most big data projects, and story-telling is a critical component of this process. In recent years, developers have created sophisticated tools to make the job of analyzing big data easier. Popular open-source tools for Python include Pandas, NumPy and of course there are math oriented applications like Matlab and R, as well as SQL for databases.

These powerful tools allow users to perform various data analysis operations, but these applications require a high level of technical acumen to accomplish even the most basic tasks. Often the stakeholders with business context don’t have the skills needed to analyze the data themselves, so they either go to an intermediary data team, bogging them down with the most banal of tasks, or they attempt feeble workarounds.

It’s no wonder newcomers to the big data world struggle. Without prior coding or database experience many find these highly technical tools overwhelming. Spreadsheets are widely used by business stakeholders, but Excel’s max row limit and reliance on loading the full dataset into the machine’s memory inhibits working on projects involving data analysis at scale.

So, what’s a business analyst to do when working with large volumes of data? I hear the detractors muttering “if you’re working with more data than Excel can handle, you should use a database.” To which I respond by reminding them that relatively few people in the world know how to use SQL (maybe 3 million), and there are 750 million Excel users. The fact is, Big Data solutions are becoming increasingly complex as data teams get more sophisticated, but this is leaving millions of part-time analysts underserved.

Enter Gigasheet, our no-code big data spreadsheet, that can be used for analyzing datasets that typically require extensive IT infrastructure and tech skills. Even at the Community level (free), Gigasheet makes it easy to explore and analyze big data, as well as identify trends and anomalies.

In this article I will walk through 4 common big data transformations, and show you how anyone with basic spreadsheet skills can do them with just a few clicks using Gigasheet.

1. Big Data Exploration In A Spreadsheet

In some cases, data sets can span multiple gigabytes and even terabytes. Exploring these data volumes requires powerful systems, efficient methods of data storage and retrieval, and advanced techniques to analyze the data. Commonly used approaches include file replication and splitting, data sharding, and distributed computing.

But what happens when you want to explore big data without all of this technological firepower? What if you’re not even sure what data a file contains? If only there were any easy way to visualize multi-gigabyte data files online, where complexity could be hidden from view, and the power and scale of the cloud could be leveraged.

Fear not, one of Gigasheet’s many use cases is as a free online CSV file viewer. Data not in CSV format? Not to worry – the system converts most structured data files on the fly. Simply upload your file and you’re on your way.


2. Combining Multiple Large Data Files

Large data files are often split into multiple parts to make them easier to store, transfer, and process. Splitting a large file into smaller parts also reduces the risk of data corruption and makes it easier to recover lost data. However, when it comes time to analyze the data it’s important to have a comprehensive view, so these pieces must be merged, appended, or otherwise combined.

The process of combining data from multiple sources into a single dataset can be done through process automation, data integration tools, or machine learning algorithms. While these methods are very powerful and capable, they are out of reach for the average business user.

Gigasheet makes it simple to join multiple files together, from CSVs or Excel workbooks to JSON. To do this, simply upload the files as a Zip. Once decompressed, just select two or more files in your library. Then, use the Combine button in the Library to merge the files of the same structure.

For instance, if you have 28 daily logs from the same server, you can easily merge them into one sheet using the Combine feature.


3. Removing Duplicate Data

Cleaning big data files of duplicates, aka de-duping, can be tricky, especially when you want to check for duplicates across multiple fields. Many users are familiar with techniques to remove duplicate rows in excel based on two columns, but few could tackle the task in SQL or Python.

Removing duplicates based on multiple values is easy in Gigasheet, and works similarly to popular spreadsheets. Unlike the typical spreadsheet, Gigasheet scales to billions of records.

Once data is loaded into Gigasheet, you’ll find a variety of Data Cleanup tools including a Delete Duplicates function. Simply select multiple columns when running Delete Duplicates and the cloud application will take care of the rest.

4. Extracting Structured Data From JSON

JSON (JavaScript Object Notation) is a popular data format for exchanging data between systems, applications, and services. It allows for storing and querying data in a structured and efficient manner. This is why most programming languages support reading and writing JSON data, and many APIs use JSON data.

However, if spreadsheets are your go-to analysis tool, analyzing large datasets with JSON records can be tricky. You can of course open moderately sized JSON files in tools like Notepad++, but if you’re working with highly nested JSON structures that are multiple Gigabytes in size, you’ll need to use a database…until now.

Gigasheet converts, or “flattens,” huge JSON files on the fly, and they can easily be pared down, exported to CSV, and opened in typical spreadsheet software. Gigasheet accepts two possible JSON file structures: either an entire file as a JSON object, or JSON where there is one object per line. In the case of the latter, each JSON object becomes a row.

Gigasheet handles the varying structure by creating a column for each leaf node of the nested sub-objects and sub-lists within an object. This results in a way to create a tabular representation of varying structured data where common fields are represented in the same column across rows and unique fields just show up in their own column. It’s quite possibly the easiest way to convert JSON to a CSV.

Wrapping Things Up

We all know big data analysis is an essential part of modern businesses. I hope this article has presented some of the most commonly used solutions and techniques for exploring, combining, and analyzing mega-sized datasets with a free no-code alternative.

With these tools and techniques, it’s possible to uncover valuable insights and create unique experiences for users who have limited or no coding experience.



Source link

Tags: AnalysisBigdataEasySpreadsheetStreamlineTransformations
Previous Post

המיתוסים הגדולים ביותר בשיווק בדיגיטל

Next Post

Label Studio Customized Backend for Semiautomatic Image Segmentation Labeling | by Alison Yuhan Yao | Apr, 2024

Related Posts

AI Compared: Which Assistant Is the Best?
Data Science & ML

AI Compared: Which Assistant Is the Best?

June 10, 2024
5 Machine Learning Models Explained in 5 Minutes
Data Science & ML

5 Machine Learning Models Explained in 5 Minutes

June 7, 2024
Cohere Picks Enterprise AI Needs Over ‘Abstract Concepts Like AGI’
Data Science & ML

Cohere Picks Enterprise AI Needs Over ‘Abstract Concepts Like AGI’

June 7, 2024
How to Learn Data Analytics – Dataquest
Data Science & ML

How to Learn Data Analytics – Dataquest

June 6, 2024
Adobe Terms Of Service Update Privacy Concerns
Data Science & ML

Adobe Terms Of Service Update Privacy Concerns

June 6, 2024
Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart
Data Science & ML

Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

June 6, 2024
Next Post
Label Studio Customized Backend for Semiautomatic Image Segmentation Labeling | by Alison Yuhan Yao | Apr, 2024

Label Studio Customized Backend for Semiautomatic Image Segmentation Labeling | by Alison Yuhan Yao | Apr, 2024

After VW plant victory, UAW sets its sights on Mercedes in Alabama By Reuters

After VW plant victory, UAW sets its sights on Mercedes in Alabama By Reuters

How To Save Money On Your Next Trip – Internet Parrot

How To Save Money On Your Next Trip – Internet Parrot

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

  • Trending
  • Comments
  • Latest
Is C.AI Down? Here Is What To Do Now

Is C.AI Down? Here Is What To Do Now

January 10, 2024
Accenture creates a regulatory document authoring solution using AWS generative AI services

Accenture creates a regulatory document authoring solution using AWS generative AI services

February 6, 2024
Managing PDFs in Node.js with pdf-lib

Managing PDFs in Node.js with pdf-lib

November 16, 2023
23 Plagiarism Facts and Statistics to Analyze Latest Trends

23 Plagiarism Facts and Statistics to Analyze Latest Trends

June 4, 2024
Azul cloud service spots dead code in Java apps

Azul cloud service spots dead code in Java apps

October 7, 2023
The 15 Best Python Courses Online in 2024 [Free + Paid]

The 15 Best Python Courses Online in 2024 [Free + Paid]

April 13, 2024
Can You Guess What Percentage Of Their Wealth The Rich Keep In Cash?

Can You Guess What Percentage Of Their Wealth The Rich Keep In Cash?

June 10, 2024
AI Compared: Which Assistant Is the Best?

AI Compared: Which Assistant Is the Best?

June 10, 2024
How insurance companies can use synthetic data to fight bias

How insurance companies can use synthetic data to fight bias

June 10, 2024
5 SLA metrics you should be monitoring

5 SLA metrics you should be monitoring

June 10, 2024
From Low-Level to High-Level Tasks: Scaling Fine-Tuning with the ANDROIDCONTROL Dataset

From Low-Level to High-Level Tasks: Scaling Fine-Tuning with the ANDROIDCONTROL Dataset

June 10, 2024
UGRO Capital: Targeting to hit milestone of Rs 20,000 cr loan book in 8-10 quarters: Shachindra Nath

UGRO Capital: Targeting to hit milestone of Rs 20,000 cr loan book in 8-10 quarters: Shachindra Nath

June 10, 2024
Facebook Twitter LinkedIn Pinterest RSS
News PouroverAI

The latest news and updates about the AI Technology and Latest Tech Updates around the world... PouroverAI keeps you in the loop.

CATEGORIES

  • AI Technology
  • Automation
  • Blockchain
  • Business
  • Cloud & Programming
  • Data Science & ML
  • Digital Marketing
  • Front-Tech
  • Uncategorized

SITEMAP

  • Disclaimer
  • Privacy Policy
  • DMCA
  • Cookie Privacy Policy
  • Terms and Conditions
  • Contact us

Copyright © 2023 PouroverAI News.
PouroverAI News

No Result
View All Result
  • Home
  • AI Tech
  • Business
  • Blockchain
  • Data Science & ML
  • Cloud & Programming
  • Automation
  • Front-Tech
  • Marketing

Copyright © 2023 PouroverAI News.
PouroverAI News

Welcome Back!

Login to your account below

Forgotten Password? Sign Up

Create New Account!

Fill the forms bellow to register

All fields are required. Log In

Retrieve your password

Please enter your username or email address to reset your password.

Log In