Mohammed Farook J
TL;DR: As datasets grow, Data Grids must now address performance, complex interactions, and maintainability. This guide explores which React Data Grid libraries are designed to scale and how to choose the one that fits where your app is headed next.
When data becomes a bottleneck
If you have built a React application recently, you may have noticed that managing data is often more challenging than developing the user interface. Dashboards are becoming denser, tables continuously expand, and datasets that begin with thousands of rows often scale to sizes that can strain both browser performance and ongoing maintenance efforts.
This is where the quality of your data grid can quietly make or break success.
Some grids perform well during initial development but reveal performance limitations when tested with real users and production-level data volumes. Others continue functioning efficiently, handling large datasets, advanced interactions, and ongoing product modifications without creating long-term maintenance challenges.
Recognizing this, the guide highlights five React Data Grid libraries that developers rely on in 2026, each suited to different types of apps, scaling needs, and teams.
Why choosing the right Data Grid matters more than ever
1. Data scale is no longer optional
Modern applications don’t just display data. They sort, filter, group, edit, and export it. As datasets grow, inefficient grids can quickly become the primary performance bottleneck.
2. Early choices lock in future constraints
A grid that works well for small datasets may require significant refactoring when requirements shift toward server‑side data, advanced filtering, or exports. Picking a grid with the right foundations early can save months of rework later.
3. Developer experience impacts velocity
Well‑designed grids reduce the need for custom fixes, manual state handling, and performance tuning. Over time, this directly affects how fast teams can ship features and respond to change.
What this guide covers
Instead of ranking grids by popularity or feature count, this article looks at:
- How different React Data Grids handle performance at scale.
- The trade‑offs between headless flexibility and component‑based convenience.
- Which grids are better suited for long‑term, production‑grade apps.
- Why do some libraries thrive in enterprise environments while others shine in highly customized setups.
By the end, you’ll see which grid complements your app’s direction.
1. MUI X Data Grid: Material design excellence
MUI X Data Grid is a TypeScript-based React component for rendering tabular data in rows and columns. It provides APIs for features such as sorting, filtering, pagination, and virtualization to support large datasets. The grid integrates with Material UI and other MUI X components, and can be customized for non-Material UI design systems as needed.
Key features
- Data operations
- Sorting: Multi-column sorting with both client-side and server-side capabilities; supports custom comparator functions.
- Filtering: Menu-driven filtering with built-in operators (e.g., equals, contains) and support for custom filter logic (advanced options in pro/premium).
- Grouping and aggregation: Enables row grouping and data summarization (e.g., sum, average, min, max) in Pro/Premium tiers.
- CRUD
- Inline editing: Edit rows directly with support for editable fields and built-in validation.
- Cell editing: Edit individual cells using custom editors (e.g., text fields, dropdowns) and use event hooks for managing updates.
- Selection
- Row and cell: Supports single or multi-row, multi-cell selection via checkboxes or click interactions.
- Advanced selection: Offers configurable selection models, including disabled selection for specific rows or columns.
- UI
- Column pinning: Fix columns to the left or right to maintain visibility during horizontal scrolling.
- Row span: Allows row spanning in merged-cell layouts for grouped datasets.
- Master-detail rows: Expandable rows for displaying nested or detailed content (Pro/Premium).
- Exporting/Print
- Export: Export data to Excel or CSV with customizable formatting; supports exporting filtered or selected content (Premium).
- Print: Provides optimized print layouts with options to tailor the printed grid appearance.
- Performance
- Pagination: Supports both client-side and server-side pagination for efficient data browsing.
- Row/column virtualization: Uses virtualization to efficiently render large datasets.
- Theming
- Native Material-UI integration: Compatible with Material UI’s design system, including support for light/dark themes.
- Customizable design: Offers extensive CSS and component overrides to align with non-Material UI systems; supports custom rendering for cells and headers.
- Accessibility
- Keyboard navigation: Complete keyboard support for navigating, editing, and selecting Grid elements.
- ARIA support: Implements ARIA attributes to ensure screen reader compatibility and meet accessibility standards.
2. AG Grid
AG Grid is a React Data Grid library that provides a structured way to display data in rows and columns. It can be integrated into React projects and adapted to different design requirements.
Key features
- Data operations
- Sorting: Multi-column sorting with customizable logic for precise control over sort behavior.
- Filtering: Includes built-in filters (text, number, date, set) and supports custom filter components; offering an Excel-style filtering experience.
- Grouping and aggregation: Enables row grouping, pivoting, and aggregation (e.g., sum, average, min, max) to summarize large datasets.
- CRUD
- Inline editing: Supports full-row and cell-level editing with integration of custom editors.
- Cell editing: Fine-grained control over editable cells, including validation and hooks for managing data updates.
- Selection
- Row and cell: Allows single or multiple rows, range, and checkbox-based selection.
- Advanced selection: Compatible with both client-side and server-side selection models, ideal for large-scale data handling.
- UI
- Column pinning: Fix columns to the left or right to maintain visibility during horizontal scrolling.
- Row span: Supports merged cell layouts using row spanning.
- Master-detail rows: Expandable rows for displaying nested grids or detailed sub-views.
- Exporting/Print
- Export: Export data to Excel, CSV, or the clipboard with options for custom formatting.
- Print: Provides optimized layouts for printing grid content.
- Performance
- Pagination: Offers both client-side and server-side pagination for flexible data navigation.
- Row/column virtualization: Efficiently renders large datasets using viewport-based virtualization.
- Server-side row model: Enterprise feature that supports lazy loading and efficient memory usage for massive datasets.
- Theming
- Native integration: Comes with built-in themes (e.g., Alpine, Balham, Material) and supports full CSS/SCSS customization.
- Customizable design: Adapts to any design system with support for custom cell renderers and editors.
- Accessibility
- Keyboard navigation: Full keyboard support for navigating, editing, and selecting grid elements.
- ARIA support: Implements ARIA attributes to ensure compatibility with screen readers and meet accessibility standards.
3. TanStack Table (React Table v8): The flexible solution
TanStack Table (formerly React Table) is a headless UI library for React. It provides the core logic and utility functions for building customizable data tables. It is ideal for developers who want full control over the table’s structure, styling, and behavior.
Key features
- Data operations
- Sorting: Built-in support for multi-column sorting with customizable sort functions.
- Filtering: Provides a flexible filtering API with global and column-specific filters, supporting custom logic such as fuzzy matching.
- Grouping and aggregation: Enables row grouping and custom aggregation logic (e.g., sum, count) through plugins or developer-defined functions.
- CRUD
- Inline editing: Not provided by default. It can be implemented using custom cell renderers and external state management.
- Cell editing: Allows for customizable cell-level editing using hooks to manage input state and update logic.
- Selection
- Row and cell: Supports both single and multiple row selection, as well as cell-level selection, with full control over behavior.
- UI
- Column pinning: Not natively included, but achievable through custom rendering and CSS techniques.
- Custom UI: As a headless solution, all UI elements such as headers, rows, and cells are fully developer-defined.
- Performance
- Pagination, row/column virtualization, and infinite scrolling: Supports efficient rendering and navigation for large datasets using built-in hooks and patterns.
- Theming
- Headless design: Does not impose any styling, allowing seamless integration with any design system (e.g., Tailwind CSS, Material UI).
- Customizable styling: Developers have complete control over table element styling via CSS or inline styles.
- Accessibility
- Keyboard navigation: Not built-in but can be implemented using custom event handlers for keyboard interactions.
- ARIA support: Developers are responsible for adding ARIA attributes to ensure accessibility, leveraging the library’s flexibility.
4. Syncfusion® React Data Grid: Enterprise performance
The Syncfusion React Data Grid is a high-performance UI component. It displays and manages data in a structured, table-based format within React apps.
It provides a spreadsheet-like experience with flexible data binding, in-grid editing, Excel-style filtering, custom sorting, row aggregation, and advanced selection. The grid also supports exporting data to Excel, CSV, and PDF formats, making it easy to share and reuse.
Designed to handle complex data scenarios, the Syncfusion React Data Grid fits well in dashboards, admin panels, and data-intensive apps where performance, control, and consistency are critical.
Key features
- Performance and large-data handling (Top-priority)
- Row virtualization: Renders only the rows visible in the viewport and reuses DOM elements as you scroll, allowing the grid to handle very large datasets.
- Column virtualization: Renders only the columns currently visible in the viewport and virtualizes off-screen columns during horizontal scrolling.
- Infinite scrolling: Retrieves data from the data source incrementally as the user scrolls, allowing continuous browsing without loading all records at once.
- Paging: Splits data into fixed-size pages and renders only the records for the active page, improving performance and usability for large datasets.
- Lazy load grouping: Loads grouped records only when a group is expanded, preventing unnecessary data processing and keeping group views performant.
- Data operations (Search, sort, group, aggregate)
- Sorting: Sorts data in ascending or descending order for single or multiple columns, with support for custom sorting logic.
- Filtering: Provides Excel-style filtering with menus, checkbox filters, wildcard and Like operators, and diacritics-aware filtering.
- Searching: Filters grid data by a search keyword, enabling quick identification of matching records across visible columns.
- Grouping: Groups records by one or more columns using drag-and-drop, with expandable and collapsible group rows.
- Aggregation: Calculates summary values such as sum, minimum, maximum, average, and count, displayed in group footers, captions, or summary rows.
- CRUD editing and data synchronization
- Inline editing: Edits values directly within grid rows for fast, in-place updates.
- Batch editing: Allows multiple cell edits and commits all changes in a single save action.
- Dialog editing: Edits records using structured pop-up forms for complex or validated data entry.
- Remote data support: Works with in-memory collections and remote data services (such as REST APIs), synchronizing changes automatically through DataManager.
- Command columns: Provides built-in add, edit, save, and delete actions for consistent editing workflows.
- Selection and user interaction
- Row and cell selection: Supports single- and multi-selection of rows, cells, or both.
- Checkbox selection: Enables multi-record selection using row-level checkboxes, suitable for bulk actions.
- Drag selection: Selects ranges of rows or cells using mouse or touch drag gestures.
- Keyboard navigation: Allows navigation and interaction using keyboard keys and shortcuts.
- Layout control and column management
- Column pinning and row pinning: Frozen rows remain visible at the top, while frozen columns can be stuck to the left, right, or a fixed position. This is especially useful when comparing values across wide grids.
- Column chooser and column menu: Shows or hides columns dynamically and provides column-specific actions.
- Column resizing and reordering: Adjusts column width and rearranges column order using drag-and-drop.
- Stacked headers: Groups related columns under multi-level headers for better data organization.
- Row and column spanning: Merges adjacent cells vertically or horizontally to improve readability in report-style layouts.
- Excel-like productivity features
- AutoFill: Copies values across adjacent cells via drag-and-drop, like spreadsheet behavior.
- Clipboard support: Supports copying and pasting data using standard keyboard shortcuts for cells and rows.
- Row height customization: Controls row height to balance data density and readability.
- Exporting and printing
- Export formats: Exports grid data to Excel, PDF, and CSV formats.
- Template-aware export: Preserves templates, styles, hierarchy, and layout in exported files.
- Multi-sheet Excel export: Adds additional worksheets for summaries, metadata, or related datasets.
- Print support: Generates print-ready layouts directly from the grid.
- Responsive and adaptive UI
- Responsive behavior: Automatically adjusts layout when the browser window is resized, without requiring reloads or configuration.
- Adaptive UI layout: Renders optimized feature layouts for mobile and tablet devices, including simplified dialogs and menus.
- Touch support: Supports tap, swipe, scroll, and drag gestures out of the box.
- End-to-end template customization
- Template coverage: Customizes cells, headers, rows, editors, toolbars, pagers, group captions, and aggregates.
- Custom component rendering: Renders Blazor components inside grid elements based on app logic.
- Editing templates: Fully customizes inline, dialog, and cell editing experiences.
- Localization and globalization
- Multi-language support: Supports localization for multiple languages.
- RTL support: Provides right-to-left layout support.
- Locale-aware formatting: Formats dates, numbers, and currency based on culture settings.
- Theming and visual customization
- Built-in themes: Includes multiple Sass-based themes with light and dark variants.
- Theme customization: Overrides Sass variables or creates custom themes using Theme Studio.
- Consistent styling: Applies consistent styling across the grid UI and exported documents.
- Accessibility
- WAI-ARIA support: Provides accessible markup compatible with screen readers.
- Keyboard accessibility: Supports full keyboard interaction for navigation, selection, sorting, and editing.
- High-contrast visuals: Improves readability for users with low vision.
5. React Data Grid
React Data Grid is a TypeScript-based React component for displaying and editing data in a tabular, spreadsheet-style layout. It exposes APIs for common data interactions such as sorting, filtering, and inline editing. The component supports configurable styling to align with different design systems and includes features commonly used in spreadsheet-like data grids.
Key features
- Data operations
- Sorting: Multi-column sorting with customizable sort logic.
- Filtering: Built-in filtering with custom filter components; supports column-level filtering.
- Grouping and aggregation: Limited native grouping, but aggregations (e.g., sum, count) can be implemented via custom row renderers or external state management.
- CRUD
- Inline editing: Supports inline editing.
- Cell editing: Granular cell-level editing with customizable editors (e.g., text, dropdowns) and event hooks for updates.
- Selection
- Row and cell: Single/multiple row and cell selection with configurable modes.
- Advanced selection: Custom selection logic via event handlers, including keyboard-driven selection.
- UI
- Column pinning: Freeze columns for fixed visibility during scrolling (via frozen column property).
- Row span: Supports row and column spanning for merged cell displays.
- Master-detail rows: Not natively supported, but expandable rows can be implemented with custom rendering.
- Performance
- Row/column virtualization: Efficient rendering for large datasets using row virtualization; column virtualization is less emphasized.
- Accessibility
- Keyboard navigation: Supports keyboard interactions.
- ARIA support: Partial ARIA support; developers may need to add custom attributes for full accessibility compliance.
Quick guide: How to choose the right React Data Grid
Choosing the right React Data Grid depends on data size, performance expectations, and more. Use the guide below to make a fast, confident decision.
| Library | Best for | UI approach | Performance at scale | Enterprise‑ready |
| MUI X Data Grid | Material UI projects | Component‑based | High (virtualization, pagination) | Yes |
| AG Grid | Complex, data‑heavy apps | Component‑based | Very high (server‑side models) | Yes |
| TanStack Table | Full UI control | Headless | Depends on implementation | No |
| Syncfusion React Data Grid | Large‑scale enterprise apps | Component‑based | Enterprise‑grade (row + column virtualization, lazy loading) | Yes |
| React Data Grid | Editable, spreadsheet‑style tables | Component‑based | High (row virtualization) | Partial |
Frequently Asked Questions
What is the difference between a component-based and a headless React Table library?
• Component-based libraries offer ready-to-use components with pre-defined styles.
• Headless libraries provide core logic and utilities, allowing developers to create custom UI and integrate with any design system.
Why are Data Grids important in React apps?
Data Grids are crucial for managing large datasets, enabling efficient viewing, sorting, filtering, and CRUD operations in React apps.
How do I choose the right React Data Grid library?
To choose the right React Data Grid library for your project, consider features, performance, ease of integration, and developer support.
What makes Syncfusion React Data Grid stand out?
Syncfusion React Data Grid offers robust features like Excel-like filtering, custom sorting, and high performance, making it ideal for enterprise apps.
Do these React Data Grid libraries support advanced data operations like grouping, aggregation, and master-detail views?
Yes, most libraries support advanced data operations. Syncfusion React Data Grid primarily supports grouping, aggregation, master-detail views, and more.
Can Data Grids in a React app handle millions of records without slowing down?
Yes, but only with the right architecture. Syncfusion React DataGrid is designed for large-scale datasets by combining row and column virtualization, lazy loading, and server-side pagination. Instead of rendering all records at once, the grid loads and renders only the visible data, helping maintain consistent performance even with millions of records.
Other enterprise grids, like AG Grid, follow a similar approach, but Syncfusion provides these capabilities as part of a fully integrated React DataGrid component, reducing the need for custom performance tuning.
Are React Data Grid libraries accessible for all users?
Yes. Many popular React data grid libraries, such as Syncfusion, AG Grid, and MUI X, provide keyboard navigation and ARIA support to improve accessibility. Among these, the Syncfusion React DataGrid offers accessibility features out of the box, reducing the need for additional customization, though overall accessibility still depends on proper configuration and testing.
Which React Data Grid libraries are most popular in 2026?
Popular React data grid libraries include MUI X Data Grid, AG Grid, TanStack Table, React Data Grid, and Syncfusion React DataGrid. Among these, Syncfusion React DataGrid stands out for offering a broad set of built-in features and enterprise-level capabilities in a single, well-supported component.
Conclusion
Thanks for reading! Choosing the right React Data Grid in 2026 ultimately depends on your project’s requirements, whether you need the flexibility of lightweight, open-source grids or the comprehensive capabilities of an enterprise-ready solution. With a wide range of mature, well-supported libraries available, teams can confidently select a grid that aligns with their performance needs, customization goals, and licensing preferences.
For applications that manage large datasets, require advanced filtering, reliable exporting, and long-term support, Syncfusion’s React Data Grid offers a robust, production-ready solution.
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