Imagine the Ultimate Lightning Data Table: a Swiss Army knife of data UI components

Imagine if Salesforce provided a Lightning Web Component (LWC) Data Table that could do it all - a single grid component that handles everything you need - with almost no code.

Picture a data table that offers:

1. Inline editing on any row, with changes saved seamlessly.
2. Computed fields/columns that automatically calculate values (like totals or formulas) on the fly - no need to create formula fields in the underlying objects.
3. Validation rules that instantly warn users of invalid data entries.
4. Master-detail views, where selecting a record in a list shows related records in the same view.
5. Client-side filtering and sorting without extra queries or round trips.
6. Grouping and summarization of rows, just like a report, within the table.
7. One-click export of the data to Excel/CSV/PDF.
8. and NO CODE OR LITTLE CODE REQUIRED

All of this would work together out-of-the-box in one powerful LWC component, configured with just a few declarative settings. No juggling multiple Apex classes, JavaScript controllers, and screen flows - just one component to define what you want, and it handles how to do it.

This isn’t the reality today, but hold that thought. It sets the stage for a comparison to an incredible technology from the past that did strive to deliver this kind of all-in-one power: the PowerBuilder DataWindow.

NOTE: If you have used SKUID (now Nintex) on Salesforce, you have had a tiny taste of what PowerBuilder’s DataWindow offered decades ago: high-productivity, low-code UI development that offered a higher degree of integration of data, logic, and layout.

Meet the PowerBuilder DataWindow - A 90s Era Miracle in Minimal Code

PowerBuilder DataWindow was an amazingly versatile data-handling UI component from the early 1990s client-server era. It was the centerpiece of PowerBuilder (a popular Rapid Application Development tool), and for good reason: the DataWindow encapsulated data access, business logic, and user interface all in one object.

Back then, in the pre-web, client-server world, PowerBuilder was a go-to for quick database apps. The DataWindow was its powerhouse, combining data retrieval, display, and manipulation into one.

Developers loved its versatility for business apps, reporting, and data entry. You could connect it to a database, retrieve data, and even update changes back to the database - with only a few lines of script.

The DataWindow control was (and still is) the heart of PowerBuilder. It could display data in a variety of layouts: from freeform forms to tabular grids, group reports, charts, etc.

Once bound to a SQL SELECT, the DataWindow knew how to present the result set and even handle user edits. In an era before web apps, you would drop a DataWindow control onto a window in a desktop app, point it to an SQL query (which could be designed graphically so the person didnt even need to know SQL), and voilà: a functioning data grid or form was ready to go.

A Ton of Functionality With Minimal Coding

You could define display formats, such as currencies or dates, and the DataWindow would handle the presentation. You could add computed fields (formula-driven columns) that calculate values based on other fields, without writing separate code - the expressions were defined declaratively in the DataWindow’s design.

You could also set validation rules (as Boolean expressions) on any input field; if a user’s entry violated the rule, the DataWindow would automatically display an error message and refuse the invalid data. All this logic was configured in the DataWindow object visually, rather than hand-coded from scratch.

Master-detail relationships and complex UI could be easily coordinated in multiple DataWindows for a master-detail interface. For example, an Account DataWindow could have a child Opportunities DataWindow - when you select an account row, it would just call a retrieval on the Opportunities DataWindow with that account ID as a parameter. With a bit of scripting (often just one line in a row-selection event), you would have achieved a coordinated master-detail view.

The DataWindow even allowed sharing its data with another DataWindow, meaning you could retrieve data once and display it in different ways (perhaps as a form, as a grid or graphical chart view of the same data) without re-querying.

The DataWindow had built-in support for things like filtering and sorting on the client side, usually with a single line of code. The user experience of clicking column headers to sort or applying simple filters could be enabled with minimal effort.

One single line to insert, update, delete

And when it came to saving data, DataWindow truly shined. After a user created/edited/deleted some rows, it was enough to call one method: Update, and the DataWindow would generate the appropriate INSERT/UPDATE/DELETE statements for all modified rows and send them to the database. It took care of batching changes and even respecting database constraints. In other words, you didn’t have to manually write inserts/updates/deletes for each row, dramatically reducing boilerplate code.

To illustrate how little code was needed, consider a typical use case in a PowerBuilder app: you want to show a list of employees from a database table and allow edits. You would:

1. design a DataWindow object (using a visual painter) with a SQL query like SELECT Name, Title, Salary FROM Employees (using a visual query builder)
2. place a DataWindow control on a window and assign that DataWindow object to it,
3. and write maybe three lines of PowerScript code: one to set the database connection, one to call Retrieve() on the DataWindow, and later one to call Update() to save changes

That’s it - the DataWindow handled the data fetching, display, editing, validation, and SQL updates. It was a one-stop shop.

DataWindow vs. Today’s Salesforce Stack: One vs. Many

Fast forward to today: we’re in the world of Salesforce, cloud platforms, and web-based apps. How do we achieve the kind of functionality that the DataWindow provided, but on the Salesforce Lightning platform?

The short answer: by combining a lot of different pieces.

Modern Salesforce developers and architects often split that all-in-one functionality across Apex (server-side logic), LWC (client-side UI and JavaScript), SOQL queries, Lightning Data Service, and a plethora of base components and configs. This means more code to link them all.

You import modules, delegate tasks to separate components, and write scripts for data retrieval via Apex or Lightning Data Service. Its powerful but feels scattered compared to DataWindows all-in-one approach.

For example, achieving the same data editing and display in LWC might need multiple components and extra lines of code, while DataWindow handled it with a few clicks in its painter tool.

Let’s break down a simple scenario - say we want to build a custom data table in a Lightning app that shows a list of records (e.g. Opportunities), allows inline edits, calculates a total, validates inputs, and can be filtered and exported.

Achieving this involves multiple layers:

Data Retrieval

In Salesforce, data queries (SOQL) and UI components (LWC) are intentionally separated into different layers. The client-side UI cannot execute database queries directly; instead, developers must create a separate Apex method containing the SOQL query. As a result, retrieving and displaying data always involves at least two distinct artifacts: an Apex class for data retrieval and an LWC component for presenting the data.

Lightning Data Service (LDS) somewhat alleviates this by allowing LWCs to access Salesforce object data directly without Apex - but this is currently limited to simple scenarios involving single record or straightforward multi-record queries.

In contrast, the DataWindow had the SQL packaged in its definition and could pull data directly from the DB. On Salesforce, for security and architecture reasons, the query happens on the server side. So there’s already two artifacts for a simple list: an Apex class and the LWC component.

By contrast, PowerBuilder's DataWindow encapsulated both SQL queries and the UI layout within a single object. This design allowed us to configure everything in a DataWindow, thus reducing complexity, artifacts, and the amount of code required.

The UnofficialSF's DataTable for flows approximates the feature of specifying a query with no code via a flow Get Records element.

Pertinent articles: Get Record Data and Wire Apex Methods to Lightning Web Components from the Lightning Web Components Developer Guide

Data Display (UI)

Salesforce provides the <lightning-datatable> LWC component for tabular display. It’s fairly powerful for basic needs – it will render columns based on field types (dates, phone, emails with mailto:, etc.), and it supports things like sortable columns and even inline edit for certain fields.

But to use it, you must configure it in your LWC’s HTML/JS. You pass in the data (from Apex) and a columns configuration. If you want a computed column (say, Total Price = Quantity * Unit Price), you typically have to compute that yourself (either in the Apex query via a formula field or in your LWC JS after receiving data).

There’s no simple declarative way in the component to declare a formula for a new column on the fly - unlike DataWindow, which let you define a computed field expression as part of its object.

Pertinent Stack Exchange question: How to populate Lightning Data Table with custom columns and data?

Inline Editing & Data Updates

Lightning Data Table does support inline editing, but saving those edits is a manual process. When a user edits a cell, the table stores the changes as draft values. The developer must write an event handler in the LWC to take those draft values and call an Apex method (or use Lightning Data Service’s updateRecord) to actually persist the changes.

While Salesforce does provide other components, such as <lightning-record-edit-form>, that handle both inline editing and saving seamlessly without manual code, these components typically support only single-record editing or simple use cases. More complex data-grid scenarios—like editing multiple records simultaneously within a table—still require manual coding to handle data persistence, validation, and error handling.

Pertinent article: Display Data in a Table with Inline Editing

Validation Rules

In PowerBuilder, validation logic could live in the DataWindow object (e.g., Salary >= 0 as a rule on the Salary column). It could also be associated with the underlying table schema so that any DataWindow that used that table would import the validation logic.

In Salesforce, validation can be handled at multiple levels. Declarative Validation Rules defined at the object level (via Setup) ensure data integrity but trigger only server-side upon saving - without instant user feedback in the UI.

For immediate feedback, you would implement client-side checks in your LWC JavaScript (for example, disabling the Save if values are out of range, or showing an error message on the field).

To provide immediate, client-side validation feedback, you would implement client-side checks in your LWC JavaScript (for example, disabling the Save if values are out of range, or showing an error message on the field). This means duplicated validation logic: once on the server (Salesforce Validation Rules) and again on the client (LWC JavaScript).

Components like <lightning-record-edit-form> help mitigate this duplication by automatically enforcing object-level validations directly in the UI; however, their applicability remains limited primarily to single-record editing or simpler use cases.

By contrast, the DataWindow’s validation approach was simpler: define the validation rule once in a single place, and it was enforced consistently and instantly on the client.

Pertinent article: Infallible Techie's Validation in lightning-datatable in Salesforce LWC

Master-Detail relationships on UI

In Salesforce Lightning, creating a master-detail view in a single screen often means using multiple components - for example, a list of records in a datatable for the master, and another datatable or form for the detail.

You then need to wire them together: perhaps using a parent LWC to hold both and manage the selection event (when a user selects a master record, you trigger a query for the detail records via Apex). There are standard components for related lists, but if you want a custom combined view, you have to do the plumbing yourself.

By contrast, in PowerBuilder you could throw two DataWindows on a window and a tiny script to link them. The concept of having everything in one component isn’t how modern web frameworks typically work - we favor smaller, reusable components. That’s good for modularity, but it means more pieces to manage for a composite feature.

Pertinent article: Trailhead - Communicate Between Lightning Web Components

Filtering and Sorting

In a DataWindow, filtering and sorting were methods on the object that simply re-arranged or restricted the in-memory dataset. In an LWC datatable, if you want to allow the user to filter the data (say by a search text or dropdown), you have to implement that logic.

One approach is to use the Lightning Data Service again to re-query with a different SOQL WHERE clause (server-side filter). Or you could fetch all data and then filter in JavaScript (not feasible if the dataset is large due to memory and performance). Either way, it’s extra code or configuration.

Sorting on a lightning-datatable can be enabled by setting sortable=true on columns, but handling the sort (especially if data is supposed to be fetched sorted from server) requires writing an onsort handler to call Apex or sort the array in Javascript. There’s no single line Sort() that works magically on all data like DataWindow had.

Essentially, you have to implement what DataWindow gave you out-of-the-box.

Pertinent articles: Apex Hours Lightning Datatable Sorting in Lightning Web Components and KeenAgile Search the data loaded into the Datatable in Lightning Web Component (LWC)

Grouping and Summaries

Perhaps one of the starkest differences is in grouping and summarizing data. The PowerBuilder DataWindow could be designed with grouping - you could designate a Group By level in the DataWindow painter, and it would handle displaying group headers, footers with subtotals, etc. It blurred the line between a data-entry grid and a report generator.

In Salesforce, if you want grouped data with aggregates, you typically use a Salesforce Report (which is a separate, non-LWC tool) or build a custom solution. The standard lightning-datatable does not support row grouping by default. Only report charts (not full tabular reports) can be embedded in a record page.

Pertinent articles: Lightning Web Component Tree Grid and Krishna Teja 's post "When you work with nested objects in LWC lightning-datatable doesnt respond well"

Exporting Data

Data export in Salesforce is often handled via Reports (users can run a report and click export) or via a custom Apex to dump to CSV. In an LWC context, you might write JavaScript to convert your data to CSV and use a client-side download technique, or call an Apex that returns a blob (for Excel, maybe use a library). In contrast, DataWindow had a simple command to SaveAs various formats (CSV, Excel, HTML, etc.)

Pertinent articles: Apex Hours Export to CSV/XLS using Lightning Web Component, Shivam Laidwar 's CSV Data Exporter - Lightning Web Component (LWC) and Salesforce Codex Export Data from Lightning Web Component to Excel Sheet

Declarative vs Code Mix

Modern Salesforce development gives a lot of declarative tools (point-and-click configuration) alongside code. For example, one might create formula fields on the object to handle a computed value (so it appears in any UI without extra coding), or use a Flow to handle some logic like mass updates or validations without writing Apex.

However, these declarative elements live in different places: a formula field is defined in the object schema, a Flow is built in Flow Builder, a component in LWC is coded in VS Code… The result can be a complex tapestry of configuration and code.

The PowerBuilder DataWindow was more of a self-contained declarative artifact: you used a visual painter to configure the query, design, and logic of the DataWindow, and that one artifact held most of the complexity inside it.

In Salesforce, you often end up leveraging multiple modules (Apex classes, field definitions, components) to achieve the same end-to-end functionality. This modularity is powerful, especially in a large-scale enterprise cloud environment, but it means the cognitive load and effort can be higher for a given feature.

Back to the Future: Can AI and Low-Code Bring DataWindow-like Productivity?

As we look at the landscape of today’s development tools, one might wonder: are we doomed to manage all these disparate pieces forever? Or could the pendulum swing back toward a more unified, higher-productivity model - essentially, a modern DataWindow for cloud apps?

Interestingly, the rise of low-code platforms and AI-assisted development suggests that we could indeed be heading full-circle to recapture some of that simplicity.

Salesforce itself has been investing heavily in declarative tooling. Salesforce Flow, for example, allows admins and devs to automate complex logic with clicks, not code. The App Builder and dynamic forms let you configure record layouts and conditional visibility without writing HTML.

And on the horizon, Salesforce is infusing AI into development - imagine being able to describe a data view in natural language (or point and click) and have the platform assemble the components for you. We’re not quite there yet, but it’s a conceivable future.

What if Salesforce (or another platform) introduced a next-generation component that works akin to DataWindow? Perhaps a Super Data Table where you could declaratively specify:

• Here’s my data source (maybe an object with fields or a SOQL),
• here’s my layout (columns, including some formulas),
• here are my validation rules, and these fields are editable.

With a single definition, the platform would generate the necessary Apex, UI, and metadata behind the scenes. Essentially, you would have a full-stack component - one that handles retrieving data, displaying it with rich interactions, validating user input, and saving changes - without a developer having to manually write each layer.

This sounds ambitious, but not far-fetched. In fact, some Salesforce features are partway there: the standard Related List component on a record page, for instance, knows how to do inline edit and save on a list of child records with zero code (though it’s not customizable to arbitrary queries). And dynamic interactions in LWC are starting to allow components to talk to each other declaratively.

Now add AI to the mix. AI code generation (like GitHub Copilot, or Salesforce’s CodeGen) could help bridge gaps by writing boilerplate Apex or LWC code based on high-level specs. But beyond writing code faster, AI could enable a different paradigm: interpreting a developer’s intent and configuring a set of meta-components automatically.

For example, you might feed an AI assistant a prompt: "Create a data table for Opportunities grouped by Stage, showing sum of Amount per stage, and allow inline editing of Next Steps with validation that Next Steps is not blank for closed deals."

Such a request touches multiple layers (query, grouping logic, UI, validation rule, edit handling). A human today would break that into tasks: make a report or aggregate query for grouping, or fetch data and group in JS, write a validation rule or conditional logic in save handler, etc.

But an AI-driven platform could theoretically take that prompt and either generate one or multiple custom LWCs with all those features wired up, or configure a super-component’s properties to achieve it.

Essentially, AI could act as the DataWindow painter of the future, where the developer describes what they want at a high level, and the tool builds the pieces under the hood.

It’s also worth noting that outside of Salesforce, many modern web frameworks are reintroducing higher level abstractions. Consider React libraries that offer data grid components with lots of built-in functionality (sorting, editing, filtering) - developers gravitate to those to avoid reinventing the wheel.

Sometimes trends move in cycles: we went from monolithic tools (like DataWindow) to very granular frameworks where everything is custom, and now we see a hunger for more productivity again, especially as app requirements get more complex.

Low-code platforms (Outsystems, Mendix, etc.) explicitly try to give you that one-stop experience for building an app, albeit with their own trade-offs.

Salesforce has a unique opportunity here

Because it knows the data schema (metadata) and security rules, it could provide a component that automatically respects field types, picklist options, validation rules, etc., much like DataWindow used the database schema for automatic SQL generation.

Imagine a Lightning component or component builder where you simply specify an object (or SOQL) and it auto-builds a grid with all required columns, appropriate editors (picklist drop-downs, lookup pop-ups for relations), enforces the object’s validation rules in real-time, and offers built-in export.

Even further: when you create an object the platform itself would give a best guess at how the records would be used and suggest what components are needed and how to display them, then ask the admin/dev permission to proceed with their creation or make adjustments.

This could save immense time for developers and ensure consistency. We can see glimmers of this idea in things like Lightning App Builder’s “Dynamic Related List” component (you pick an object and fields, and it makes a list view for you) and in record list components. But a truly programmable, DataWindow-like component hasn’t emerged yet in full glory on the platform.

With the advent of AI, however, Salesforce and others might leapfrog incremental improvements and deliver something revolutionary. It could be an AI-driven DataWindow reincarnation - not limited to just one use-case, but as a pattern for development: describe what you need, and the platform constructs the working solution.

This doesn’t mean writing off all the benefits of modern architectures; we’d still value component reusability and clear separation under the hood. But those details could be generated or managed by the platform, rather than hand-crafted each time by the developer.

Conclusion: Past Inspiration for Future Innovation

This tale of PowerBuilder’s DataWindow vs. today’s LWC data tables is not just tech nostalgia - it’s an approach on how developer productivity tools could evolve.

The DataWindow showed simplicity and power going hand in hand: one object encapsulated what we now spread across database, server, and client layers. While modern cloud architectures required us to break apart that all-in-one model (for good reasons like scalability and security), the pendulum could be swinging back toward offering higher-level building blocks again.

This is intended as an eye-opener for Salesforce developers and architects and a reminder that our ultimate goal is to deliver business functionality with as little friction as possible.

In the 1990s, DataWindow was the apex (😁) of high productivity for client-server apps. In 2025 and beyond, we have an arsenal of specialized tools - but perhaps we could use a bit of that magic one-stop component again.

By learning from the past, Salesforce and other platform providers can innovate the next generation of low-code components that draw inspiration from DataWindow’s elegance while leveraging today’s technology (AI, cloud, metadata-driven UIs).

Imagine a future release of Salesforce where you drag on a Smart Data Table component, point it to an object (or even a multi-object set of SOQLs), and instantly get a fully functional interactive report - editable, filterable, groupable, with all the trimmings - no Apex class needed.

That vision aligns with the original spirit of DataWindow, but updated for the cloud era. It’s a future where we spend more time defining what the business needs and less time wrangling how to make the tech do it.

The journey from DataWindow to LWC data tables could come full circle in concept. By reintroducing higher-level abstractions (with the help of AI and declarative design), we just might bring back the simplicity of the past to the platforms of the future.

The next generation of Salesforce development could very well be "DataWindow-driven" - not literally using 90s technology, of course, but by embracing the core idea that made it great: maximizing power and productivity while minimizing code.