JSON has become the preferred data format across industries due to its human-readable structure and system independence, enabling applications to store complex nested datasets efficiently. While developers appreciate JSON's flexibility, many healthcare analysts and business users need to work with JSON files without extensive programming knowledge. Healthcare organizations working with large JSON datasets - especially from the Centers for Medicare & Medicaid Services (CMS) Transparency in Coverage Rule - need reliable solutions that don't require extensive technical expertise.
Healthcare analysts and business intelligence professionals often encounter significant challenges when working with large JSON datasets from claims systems, EHRs, or price transparency files. These machine-readable files (MRFs) are often massive, messy, and far from transparent, making it difficult to extract actionable insights. This article will demonstrate how to open and analyze these large, complex JSON files without writing a single line of code.
The Challenge of Large JSON Files in Healthcare
Traditionally, healthcare analysts have faced a difficult choice when confronted with large JSON files. Standard tools like Microsoft Excel, which has a hard limit of 1,048,576 rows, are simply not equipped to handle the massive, nested data structures common in healthcare datasets. Even if the file can be opened, the nested nature of JSON does not translate well to Excel's flat, row-and-column format.
For those with programming skills, writing a Python script is a common workaround. However, this approach presents its own set of challenges. Learning a programming language simply to analyze a file is a significant time investment, and building and maintaining custom scripts can be complex and error-prone. This reality often leaves analysts feeling pressured to be "unicorn analysts" who can code, manage databases, and perform data science tasks, when their real value lies in their analytical expertise.
Why Traditional Tools Fail
Tool
Limitation
Impact on Healthcare Data Analysis
Microsoft Excel
1,048,576 row limit; poor handling of nested data
Unable to open or effectively analyze multi-gigabyte price transparency MRFs or large claims datasets.
Text Editors
Performance degradation with large files
Extremely slow to open, search, or manipulate large JSON files, often crashing the application.
Web-Based Viewers
Browser memory and performance limitations
Cannot handle the gigabyte-scale files common in healthcare, leading to browser crashes and lost work.
Python/Custom Scripts
Requires programming expertise; time-consuming to develop and maintain
High barrier to entry for non-programmers; scripts may not be robust or easily adaptable to new data formats.
A Practical Example: Analyzing Hospital Price Transparency Data
Let's consider a real-world example: analyzing a large JSON file containing hospital price transparency data. This dataset, which is nearly 2GB in size and contains over 8 million records, is too large to open in Excel or a standard text editor. Each record has a complex, nested structure, with variations in the data fields.
Our goal is to answer a simple business question: which provider network appears most frequently in this payer's data?
With Gigasheet, a no-code, big data analytics platform, this task becomes straightforward. You can upload the large JSON file (even in a zipped format to save time) directly into Gigasheet. The platform automatically flattens the nested JSON structure into a familiar row-and-column format, ready for analysis.
Once the data is loaded, you can see the distinct sender and recipient addresses, which in this context represent different provider networks. To analyze the domains, we can use Gigasheet's built-in tools to split the email columns at the "@" symbol, creating new columns with just the domain names.
By combining these new columns, we can create a single, unified list of all provider network domains. A simple "Group" operation on this new column instantly reveals the frequency of each domain, answering our original question in a matter of minutes, not hours or days.
From Raw Data to Actionable Intelligence with Gigasheet
This example demonstrates how Gigasheet transforms the challenge of working with large JSON files into a simple, intuitive process. But Gigasheet is more than just a large file viewer. It is a powerful analytics workbench that transforms complex payer and hospital price transparency data into actionable insights.
Gigasheet cleans, structures, and enriches massive machine-readable files (MRFs), making analysis and comparison easy. With access to clean rates from any payer and more than 5,500 hospitals nationwide, healthcare organizations can:
•For Payers: Streamline network development, accelerate recruitment, and negotiate stronger contracts.
•For Providers: Benchmark rates against competitors, strengthen negotiation power, and uncover new revenue opportunities.
•For Market Access & Reimbursement: Support sales and identify comparable reference rates.
Our commitment to healthcare analytics excellence drives continuous innovation, ensuring our platform addresses the evolving data challenges facing healthcare payers, providers, and MedTech organizations. Healthcare analysts can focus on generating actionable insights rather than wrestling with technical data preparation challenges that traditionally required specialized programming skills.
This approach transforms how healthcare organizations handle complex JSON datasets, enabling analysts to derive meaningful insights from price transparency files, claims data, and other critical healthcare information sources with unprecedented ease and reliability.
Ready to see for yourself? Sign up for a free Gigasheet account and start analyzing your large JSON files today.
This is a dataset of 8,145,323 IOT records, each in its own JSON array:
And looking at the data, you see each record has a distinct combination of sub-nesting, as sometimes it has sender and sometimes recipient. And let’s say you want to answer a simple question like: which domain shows up the most in all email addresses, either as sender or recipient?
Opening this JSON in Excel won’t work. Because of the non-standard nesting, any sort of text-to-columns will fail.
Cutting and pasting this into a web-based tool won’t work. The dataset is nearly 2GB in size, and will most likely crash a browser.
So, how do we open this JSON file so we can analyze it? Let's dig in!
How To Open Large JSON Files In Python
At this point, it has likely become clear that — for our long-suffering analyst, who just wants to answer a simple question — workarounds don’t get the job done. If you want to open big JSON files (potentially running to hundreds of millions of rows) you need to take a completely different approach.
One such option is to use Python, or another similarly powerful coding or scripting language.
Python is a general-purpose programming language that, among other uses, has historically seen a lot of uptake in the scientific and mathematical communities. Its high-performance nature and built-in library of useful modules make Python an extremely powerful tool for interrogating and visualizing huge datasets.
And Python is far from the only option. Just about every programming language has ways to interrogate even the largest JSON datasets. However, they all run into the same problems: time and complexity.
As an analyst, do you have the capacity (or inclination) to learn one or more programming languages just to analyze large JSON files? Even if you do, will you ever be completely confident that your custom-written scripts and tools are watertight? Ultimately, while Python and other scripting languages are undoubtedly an option, they don’t fulfill our criteria.
Analysts don’t need to be full-on data scientists or programming experts - they need to be analysts! So many large datasets are stored in and output to JSON, and analysts need simple, powerful solutions that allow them to work with these huge JSON datasets, just as easily as they would with a smaller file, using a universal spreadsheet-like application.
How To View Very Large JSON Files and Datasets
If you’ve been waiting for the pitch, here it is.
Gigasheet is a no-code analyst workbench that allows analysts to work efficiently with even the largest datasets.
No longer will you be pressured to be a ‘unicorn analyst’ who can code, manage databases, and perform data science tasks. With Gigasheet, you can open large JSON files with millions of rows or billions of cells, and work with them just as easily as you’d work with a much smaller file in Excel or Google Sheets.
So in our IOT data example, it’s easy enough to upload our large JSON file into Gigasheet. You can even zip your file before uploading to save time. Once it’s loaded, we can open it and see the flattened JSON structure in a rows-and-columns format:
In one spot, the JSON data is loaded, flattened, and ready for analysis. Easy enough to see how the sender and recipient addresses are made distinct.
Now that we’re in a spreadsheet, analysis is straightforward. Apply a function to split the email columns at the @ symbol:
Do that on both sender and recipient, and it looks like this:
We can likewise use the combine column function in a similar way:
Which results in one column with either the sender’s or recipient’s domain.
A simple Group by that new column produces the answer to the original question, showing how many of each domain appear in the dataset:
There you have it, an easy way to view and open large JSON files! We can further break this down by how many are sender and how many are recipient by using an aggregation:
We think this is the best way out there to open large JSON files and work with nested JSON datasets. We believe we've finally created an easy solution to the age old “how to open a large JSON file" question.
Start with a free Gigasheet account. Sign up here.
We want to know exactly what problems you face so we can make sure Gigasheet is equipped to solve as many of them as possible.
The ease of a spreadsheet. The power of price transparency.
