Exploring AG Grid: An Enterprise Solution with React TanStack Table

Anton Ioffe - March 11th 2024 - 10 minutes read

In the dynamic landscape of modern web development, the management of complex data structures within React applications has evolved from a cumbersome challenge into an art, thanks to powerful libraries like AG Grid and React TanStack Table. This article embarks on a deep dive into both, unraveling how AG Grid’s enterprise-level robustness pairs with the unparalleled customizability of React TanStack Table to revolutionize data management strategies. Through a journey from their historical roots to a groundbreaking hybrid implementation, we explore the nuances of each library and how to leverage their combined strengths. With an emphasis on best practices and overcoming common pitfalls, we invite senior-level developers to reimagine the potential of their data grid components, setting a new benchmark for efficiency and user experience in advanced web applications.

The Evolution of Data Grids in React Applications

The evolution of data grids within React applications marks a significant milestone in the development of web applications. Initially, developers tasked with implementing data display functionality had to rely on manually constructing tables using <table> HTML elements. This approach was not only labor-intensive but also limited in terms of scalability and interactivity. As web applications began to handle increasingly complex datasets, the need for more dynamic and efficient ways to render and manage data became apparent.

Enter the world of data grid libraries, which offered a solution to the limitations of manual table construction. These libraries, designed to render large datasets efficiently, have become an integral part of React's ecosystem. With the ability to handle data sorting, filtering, and pagination out of the box, data grid libraries significantly reduced development time and complexity. Moreover, they provided a smoother user experience by dynamically updating the UI without the need for constant page reloads.

AG Grid and React TanStack Table represent two of the most popular and sophisticated data grid libraries available to React developers. AG Grid is renowned for its enterprise-level features, offering a comprehensive suite of capabilities designed to meet the demands of large-scale applications. On the other hand, React TanStack Table, with its headless UI approach, allows for greater customization, enabling developers to tailor the look and feel of their data grids precisely to their application's requirements.

The shift towards these sophisticated data grid libraries underscores a broader trend in web development towards components that offer out-of-the-box functionality while allowing for extensive customization. By abstracting away the complexities associated with rendering and managing large datasets, these libraries enable developers to focus on creating richer, more interactive web applications.

Moreover, the integration of data grids into React applications reflects the increasing importance of handling complex data structures efficiently. As applications grow in scale and complexity, the ability to render and manipulate large datasets in real-time becomes crucial. Data grid libraries like AG Grid and React TanStack Table have become pivotal in meeting this challenge, facilitating the development of highly interactive and data-driven web applications.

AG Grid: An Enterprise-Level Behemoth

AG Grid stands out in the realm of enterprise web applications for its robust, feature-rich capabilities tailored to manage massive datasets effortlessly. Its architecture is designed from the ground up to support the complex needs of modern web applications, integrating seamlessly with popular JavaScript frameworks including React. One of its distinguishing features is virtual scrolling, which efficiently handles large amounts of data by only rendering rows visible to the user, thus optimizing performance and resource consumption. This is particularly beneficial for enterprise applications where rendering thousands, or even millions, of rows is a common requirement.

Integrated charting in AG Grid brings data visualization directly into the grid, allowing developers to easily transform data into interactive charts without the need for external libraries. This feature enhances the data analysis capabilities of web applications, providing end-users with powerful tools to sort, filter, and visualize data sets in real-time. Furthermore, AG Grid's support for live data updates enables the display of real-time data feeds within the grid, a critical requirement for applications in finance, monitoring, and other sectors where timely information is paramount.

However, the extensive functionality of AG Grid comes with its complexities. The comprehensive feature set and flexibility may present a steeper learning curve compared to simpler data grid solutions. Nevertheless, for enterprise applications requiring high levels of customization and performance, the trade-off is often worthwhile. The complexity is mitigated by AG Grid's well-documented APIs and dedicated support, ensuring that developers can leverage its full potential efficiently.

In terms of performance, AG Grid excels in handling large data sets with minimal impact on browser performance. Its efficient data management and rendering mechanisms, such as row virtualization and batch updates, ensure smooth interactions even with complex, data-heavy interfaces. This performance efficiency is critical in enterprise settings where application responsiveness directly influences user satisfaction and productivity.

Integrating AG Grid with React further enhances its appeal for enterprise applications. React's component-based architecture complements AG Grid's modular design, allowing for easy integration and reuse of custom grid components. This synergy enables developers to build highly interactive and performant data-driven applications with relative ease. The combination of AG Grid's extensive feature set with React's declarative UI logic forms a powerful toolset for developing sophisticated data management solutions in enterprise-level web applications.

React TanStack Table: Embracing Flexibility and Customizability

The React TanStack Table adopts a headless UI model, diverging significantly from more traditional, component-based libraries like AG Grid. This approach leaves the UI design and interaction entirely in the hands of developers, giving them full control over how the table behaves and looks. Unlike AG Grid, which comes with pre-defined components that dictate the grid's appearance and functionality, TanStack Table provides only the functionality, sidestepping any assumptions about how data should be displayed. This paradigm shift towards a headless architecture accentuates flexibility and customizability, enabling developers to sculpt the user interface according to specific project requirements or design guidelines.

In practice, utilizing React TanStack Table involves creating your table components from scratch, leveraging the library's hooks for operations like sorting, pagination, and filtering. For instance, a basic implementation of a sortable table column might look like this:

import { useSortBy, useTable } from '@tanstack/react-table';

function MyTable({ columns, data }) {
  const { getTableProps, getTableBodyProps, headerGroups, rows, prepareRow } = useTable({
  }, useSortBy);

  return (
    <table {...getTableProps()}>
        {headerGroups.map(headerGroup => (
          <tr {...headerGroup.getHeaderGroupProps()}>
            {headerGroup.headers.map(column => (
              <th {...column.getHeaderProps(column.getSortByToggleProps())}>
                  {column.isSorted ? (column.isSortedDesc ? ' 🔽' : ' 🔼') : ''}
      <tbody {...getTableBodyProps()}>
        {rows.map(row => {
          return (
            <tr {...row.getRowProps()}>
              {row.cells.map(cell => (
                <td {...cell.getCellProps()}>

This level of control unleashes unparalleled opportunities for customization, allowing for intricate designs, layouts, and functionalities that align perfectly with the app's overall aesthetics. Developers can integrate complex interactions within the table, such as drag-and-drop ordering or inline editing, without being constrained by the grid library's predefined components.

However, this flexibility comes at the cost of added complexity and development overhead. Unlike AG Grid, where features like sorting or pagination are readily available out-of-the-box, implementing these features with TanStack Table requires additional coding. This mandates a deeper understanding of both the library's API and the overarching principles of React state management, potentially increasing the project's initial development phase.

Despite the overhead, the adaptability of React TanStack Table offers compelling advantages, particularly for projects requiring distinct customization or those aiming to maintain a lightweight bundle size. This approach can lead to a more tailored user experience and potentially lower performance overhead, as unnecessary features and markup can be omitted entirely. It prompts developers to ponder: is the added flexibility and control over the UI worth the extra effort in implementation, or would a more opinionated, out-of-the-box solution suffice for the project's needs?

Combining Forces: AG Grid with React TanStack Table

Leveraging AG Grid alongside React TanStack Table offers an innovative approach to solving complex data management and presentation challenges. AG Grid stands out for its robust data handling capabilities and extensive feature set, making it a preferred choice for backend data processes. On the other hand, React TanStack Table shines with its flexible and customizable front-end presentation layer, empowering developers to tailor the UI to their specific needs. This hybrid model combines the strengths of both libraries, achieving unparalleled data grid performance and user experience.

To integrate these two powerful solutions, developers start by setting up AG Grid to manage and process the data. This involves utilizing AG Grid's capabilities for sorting, filtering, and managing large datasets efficiently, thanks to its performance optimizations like virtual DOM rendering. Next, the focus shifts to leveraging the headless UI of React TanStack Table. By tapping into TanStack Table's flexible API, developers can create a custom presentation layer that precisely fits the application's design requirements.

The integration process requires a thoughtful approach to data flow and component architecture. The data managed by AG Grid must be seamlessly passed to the React TanStack Table components. This often involves using React's Context API or a state management library to ensure that the data is accessible to the TanStack components for rendering. Additionally, developers must handle events and interactions in the TanStack Table UI, such as sorting and pagination requests, and ensure these actions are reflected in the AG Grid data processing layer.

One significant advantage of this hybrid approach is the ability to leverage AG Grid's superior data handling features, such as its integrated charting capabilities and support for live data updates, while still providing a highly customizable user interface designed with React TanStack Table. This combination allows for the creation of data-rich applications that are not only performant but also offer a tailored user experience that matches the application's branding and UI design principles closely.

However, it's crucial to consider the complexity this approach introduces. Developers must be adept at managing the interactions between AG Grid and React TanStack Table, requiring a deep understanding of both libraries. Properly architecting the application to handle the data flow between the backend data processing and the front-end presentation layer is key to building a successful implementation. When done correctly, combining AG Grid with React TanStack Table empowers developers to build data management solutions that are both powerful and personalized, offering the best of both worlds.

Best Practices and Common Pitfalls

In deploying AG Grid and React TanStack Table within React applications, understanding the importance of efficient state management is paramount. Common mistakes often stem from a mishandling of component state, leading to unnecessary re-renders that can significantly degrade performance. For AG Grid, a frequent pitfall is not leveraging the grid's built-in capability to manage data updates, which can lead to overuse of React's useState and useEffect for data that AG Grid could handle more efficiently. Conversely, with React TanStack Table, since it is headless and relies on the developer to implement UI rendering, there's a risk of mismanaging state and props that could lead to similar performance issues.

Another critical area prone to errors is neglecting accessibility in data grid implementations. Both libraries offer extensive capabilities to create accessible data grids, yet developers may overlook these features. For example, ensuring that all interactive elements in AG Grid are reachable and operable through a keyboard interface and providing appropriate ARIA roles and attributes in React TanStack Table implementations are best practices that sometimes are missed. Accessibility should not be an afterthought; it requires the same level of attention as any other feature in your data grid.

Over-complication and under-testing are other common pitfalls. Implementing features that the business does not require at the expense of application performance or developer productivity can be a critical mistake. Similarly, choosing one library over another without thorough testing based on actual project requirements can lead to regrettable decisions. For instance, incorporating AG Grid with all its enterprise-level functionalities for a simple project that a React TanStack Table could manage may not be the most efficient approach. Developing and testing small, realistic prototypes with both libraries can often illuminate the best path forward based on actual performance metrics and feature needs.

Thought-provoking Questions: Before settling on a data grid solution, consider the following: Does the proposed solution meet your project's accessibility requirements? Have you measured the performance impact of your implementation in realistic scenarios? How much of the library's functionality are you actually leveraging, and could you achieve the same outcomes with a simpler approach? Answering these questions can often help to avoid common coding mistakes and ensure that you're making the most informed decision for your specific needs.

In conclusion, while both AG Grid and React TanStack Table offer robust solutions for implementing data grids in React applications, they come with their respective challenges and pitfalls. Through careful planning, efficient state management, accessibility considerations, and rigorous testing, developers can mitigate these issues. Balancing the capabilities of the data grid library with the specific requirements of your project is crucial in deploying an effective data grid solution. Remember, the best solution is one that not only meets today’s requirements but is also maintainable and scalable for tomorrow's needs.


The article explores the integration of AG Grid and React TanStack Table in modern web development, highlighting their strengths and how they can be combined to build efficient and customizable data grid solutions. The key takeaways include the evolution of data grids in React applications, the enterprise-level features of AG Grid, and the flexibility of React TanStack Table. The challenge for the reader is to evaluate their project's requirements and consider the trade-offs between the out-of-the-box functionality of AG Grid and the customization potential of React TanStack Table, in order to make the best decision for their specific needs.

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