I Used React DataGrid to Build a Real Space Mission Explorer

You can check out the full project and explore the missions yourself.

Live Demo 👀

GitHub Repository ⭐

Setting Up React DataGrid

Once I had the project structure in place, I wanted to get the grid working before spending time on the rest of the interface. I started with the open-source package and kept the first render intentionally simple.

The installation was straightforward:

npm install react-open-source-grid

Then I imported the library’s stylesheet:

import 'react-open-source-grid/dist/lib/index.css';

For the first test, I created a small grid with only a few mission fields:

const columns: Column[] = [
  { field: 'mission', headerName: 'Mission', width: 200 },
  { field: 'agency', headerName: 'Agency', width: 120 },
  { field: 'status', headerName: 'Status', width: 140 },
  { field: 'launchDate', headerName: 'Launch Date', width: 130 },
  { field: 'destination', headerName: 'Destination', width: 150 },
];

From there, I defined typed columns for Mission, Agency, Status, Launch Date, Destination, Mission Type, and Duration and mapped the dataset to them.

At this stage, the API felt familiar, so I didn’t have to spend much time learning a completely unfamiliar grid model.

I also kept the React DataGrid GitHub repository and documentation close by while building, since this was a hands-on test.

Building the Mission Explorer

I didn’t want to build a grid with a few rows just to say I had used one. I wanted enough data and enough interactions to see whether the component could handle the kind of complexity you’d expect in a real application.

I ended up using two dataset modes: a 1,200-row client-side working set for interactive analysis, virtual scrolling, and a 100,000-row live archive that loads records on demand through server-side infinite scrolling.

The difference between the two modes was useful because it gave me two different ways to work with the same mission data instead of artificially creating a huge dataset just for benchmarking.

Mission Explorer page
 

Sorting, Filtering, and Search

I started with the interactions I’d expect from any data grid. The column headers support sorting, including multi-column sorting, so I could do things like sort missions by agency and then by launch date without writing custom sorting logic myself.

For filtering, I had both the global search bar and individual column filters. The global search makes it easy to quickly find a mission, agency, or destination, while the filters directly under the column headers give me more control when I need to narrow down a specific field.

The sidebar filters were even more useful for this dataset. Instead of forcing me to type everything into a search box, the faceted search panel lets me filter by Agency, Status, Destination, Mission Type, and Decade. Each facet also displays live counts, such as NASA (265) or Successful (839), so I can immediately see how much data each filter represents.

If I want to see only successful NASA missions to Mars from the 1990s, I can narrow the dataset from several different dimensions without building a complicated filter UI around the grid myself.

I also tested column resizing and reordering, and both were straightforward to use when adjusting the Explorer to different screen sizes and workflows.

Sorting, Filtering, and Search
 

Grouping and Pinning

Once filtering was working, I wanted to see how the grid handled more analytical interactions.

React DataGrid lets me drag columns into the grouping area above the table. That means I can group missions by something like “Agency” and then further organize them by another field, instead of treating every mission as an isolated row.

Grouping and Pinning
 

I also pinned four reference missions to the top of the grid. This is a small feature, but I found it really useful when working with a large dataset because important records remain visible while I scroll through the rest of the archive.

Pivot Table

The most interesting part of the Explorer was the built-in Pivot Table builder.

Instead of manually writing aggregation logic for every analysis I wanted to perform, I could choose a Row Group By, Pivot Column, Value Column, and Aggregation, then apply the configuration directly from the interface. I could also toggle totals rows and a grand total column.

For example, I could group missions by agency, pivot them by destination, and aggregate values such as duration or cost. That turns the grid from a place where I simply browse records into something I can use to explore the dataset.

Pivot Table
 

Custom Cells, Totals, and Export

I also used custom cell rendering to make the grid easier to scan. Mission statuses aren’t displayed as plain text; they’re represented with color-coded badges for Successful, Planned, Partial Success, Failed, Cancelled, and in progress. The working set also includes a totals footer that aggregates values such as duration and cost.

Around the grid, I added the controls I would actually expect to use in a production data table: a column picker, CSV and Excel export, a layout reset control, and four density options ranging from Ultra Compact to Comfortable.

Custom Cells and Totals
 

CSV and Excel export
 

What I liked most was being able to combine faceted filtering, grouping, pivoting, pinned rows, custom renderers, and export in the same interface without the page turning into a collection of disconnected controls.

Building Mission Analytics

Once the Mission Explorer was working, I wanted to use the same dataset for something beyond browsing individual records. That’s why I made the second page, Mission Analytics.

This page is deliberately chart-first. Instead of displaying another table, I used the same mission data and aggregation logic from the Explorer to create a set of visualizations that answer higher-level questions about mission history, agencies, destinations, and costs.

At the top, I added four summary cards:

• 1,200 missions in scope
• 79.6% average success rate
• $669.12B in program cost
• 1,543 days average mission duration

Under those cards, the page contains five different visualizations: launch cadence by decade, agency reliability, destination distribution, success profile by mission type, and cost versus mission duration.

Mission Analytics page
 

Launch Cadence and Agency Reliability

The first chart looks at Launch Cadence by Decade, showing how the number of missions and successful missions changed across the different decades in the dataset.

This gives the archive a historical dimension that isn’t obvious when you’re looking at individual rows in the Explorer. Instead of asking which missions launched, I can start asking how mission activity changed over time.

Next, the Agency Reliability chart compares launch volume with average success rates across agencies such as NASA, SpaceX, Roscosmos, ESA, CNSA, ISRO, JAXA, and Blue Origin.

This is where the aggregation capabilities became useful. The chart isn’t based on a separate dataset created just for the dashboard. It’s derived from the same mission records I was already filtering, grouping, and analyzing in the Explorer.

Launch Cadence and Agency Reliability
 

Destination, Mission Type, and Cost

The other three visualizations look at different dimensions of the same data.

The Destination Distribution chart shows where missions in the archive are going, with destinations including Earth Orbit, the Moon, Mars, the Asteroid Belt, Deep Space, and Jupiter.

The Success Profile by Mission Type takes another angle by comparing success rates across mission types such as rovers, orbiters, flybys, robotic landers, space telescopes, and sample-return missions.

Finally, the Cost vs. Mission Duration scatter plot lets me look at whether expensive missions also tend to have longer durations. Each point represents an individual mission, making it easier to spot unusually expensive or long-running programs.

What I found interesting here is that I didn’t need to put another grid on this page for React DataGrid to remain useful. The grid’s underlying dataset and aggregation logic are still doing the work; I’m simply presenting the results in a form that’s easier to interpret visually.

Destination, Mission Type, and Cost

Building the Mission Details Page

After working with the full archive, I wanted the third page to do the opposite: take me from 100,000 missions down to one specific mission.

For this example, I used MX-000006, Apollo VIII. The page brings the mission’s main information together, including its launch date, duration, program cost, and crew, without forcing everything into another large table.

Mission Details Page
 

The most interesting part of this page, though, is the Mission Timeline.

Using Tree Data for a Real Mission Timeline

A mission isn’t just a collection of unrelated fields. It naturally has an ordered structure: launch happens before orbital insertion, which happens before the mission’s main operations, and so on.

That made the timeline a good place to use Tree Data.

For Apollo VIII, I structured the mission into 5 phases:

Apollo VIII
├── Launch
├── Orbit Insertion
├── Payload Commissioning
├── Operations
└── Deorbit

Each phase has its own COMPLETE status badge and a T+ day offset, so the timeline gives me both the hierarchy and the chronological context of the mission.

Tree Data for a Real Mission
 

This is one of the features that made more sense once I had a real application to build. I could have created a custom nested component for the timeline, but Tree Data already maps naturally to this kind of structured information.

The rest of the page contains the Crew Manifest, Payload & Equipment, and Related Dossiers sections. These keep the additional mission information accessible without turning the page into another dense data-management screen.

Crew Manifest, Payload & Equipment and Related Dossiers
 

At this point, I had all three parts of the application working together:

• Mission Explorer for searching and manipulating the archive
• Mission Analytics for understanding the data at a higher level
• Mission Details for drilling into an individual mission

This gave me a better environment for evaluating React DataGrid than a small demo table would have. I had real filtering, grouping, pivoting, hierarchical data, custom rendering, and large datasets all working in the same project.

Theming & UI Customization

Once the functionality was working, I spent some time making the grid belong inside the application. The default data grid look would have worked, but it didn’t really fit the dark, cyan-accented flight dynamics terminal style I was going for.

I used theme variables to adapt the grid’s appearance and added custom cell renderers for the mission status badges. The badges use different colors for states such as Successful, Planned, Partial Success, Failed, Cancelled, and in progress, which makes scanning the Explorer much easier.

I also used four density modes, Ultra Compact, Compact, Normal, and Comfortable, so the amount of information displayed per row can be adjusted without rebuilding the grid layout.

What I appreciated here is that customization didn’t require me to fight the component’s default styling. Most of the work was about making React DataGrid match the visual language of the project rather than trying to work around the grid itself.

Developer Experience: What Was Easy and What Took More Work

The overall developer experience was one of the biggest positives from this project. Installation and getting the first working grid on screen took less time than I expected, and the API felt familiar.

Once the basic grid was running, adding sorting, filtering, grouping, and the Pivot Table builder was straightforward and aligned with the documented examples. The same was true for the Tree Data implementation on the Mission Details page. I wasn’t constantly trying to figure out how to make the library do something it wasn’t designed to do.

Accessibility was another area I paid attention to while working with the grid. I also tested keyboard navigation and paid attention to the grid’s ARIA behavior while working through the interface. The documentation also gave me enough examples to understand the less common features I was using.

That said, not every part of the process was equally straightforward. The more specialized features required more time to understand than everyday operations such as sorting or filtering. The Pivot Table builder and Tree Data configuration were the areas where I spent more time checking examples and figuring out exactly how I wanted the data structured.

React DataGrid vs. a Basic HTML Table: What I Would Choose

If I were only displaying five or ten rows of static data, I wouldn’t reach for React DataGrid. A basic HTML table or lightweight React table would be simpler and would do the job well.

This project was a very different situation.

Once I needed 100,000 missions, faceted search, multi-column sorting, grouping, pivoting, Tree Data, custom cell renderers, and server-side infinite scrolling, a basic table would have required me to build a large part of that functionality myself.

That’s where React DataGrid made more sense. Instead of spending my time building and maintaining table infrastructure, I could focus on the actual application: how missions should be organized, what users should be able to explore, and how the analytics should work.

Is React DataGrid Worth Using?

After building a project with it, I think React DataGrid makes sense for applications where the data itself is a major part of the user experience.

I’d particularly consider it for:

• Data-intensive dashboards
• Analytics applications
• Admin interfaces
• Financial applications
• Internal business tools
• Applications with large datasets
• Projects with complex filtering or grouping
• Applications with hierarchical data
• React applications that need server-side data loading

The Space Mission Explorer was a good test because it combined several of these requirements at once. I had a 1,200-row client-side working set for interactive analysis and a 100,000-mission live archive using server-side infinite scrolling.

Final Thoughts

Building the Space Mission Explorer gave me a better perspective on React DataGrid than I could have gotten from a feature checklist alone.

I was able to take a real dataset, turn it into an interactive explorer for a 100,000-row mission archive, build analytics around it, and use Tree Data for a mission timeline without having to build the grid infrastructure myself.

For data-heavy React applications, that’s ultimately what matters:

The grid should handle the complexity of the data so you can focus on building the product around it.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah