textquery.app

TextQuery imports three families of files: data files (.xlsx, .xls, .csv, .tsv), structured data (.json, .jsonld), and compressed archives (.zip, .gz, .tgz, .tar). You don't need to define a schema, write code, or run commands first — the app turns the file into a table on import. If your file is in one of those formats, it should open directly; if it isn't, you'd need to convert it first.

Supported formats at a glance

What "no schema needed" actually means

Normally, loading a raw file into a database means declaring column names and types before anything can be queried. TextQuery skips that step: you point it at a file, and it infers the table structure so the data is queryable with SQL right away. That's the practical difference for the formats above — a .csv or .xlsx becomes a table you can query without a setup script.

Choosing what to import

• Already have a spreadsheet? Use .xlsx or .xls.
• Exporting from another tool? .csv and .tsv are the most portable and usually the safest choice.
• Working with API or web data? .json and .jsonld fit naturally.
• Received a bundle of files? .zip, .gz, .tgz, and .tar can be imported as archives.

Common snags

• Format not on the list. If your file is something else (for example, a proprietary binary format or a database dump), convert it to CSV or JSON first.
• Archives with mixed contents. A .zip or .tar may hold several files; expect to deal with each file's own format once extracted.
• Delimiter confusion in .csv/.tsv. If columns look wrong after import, the delimiter or quoting is the usual culprit.

Verifying the import worked

After importing, check that the row count and column names match your source file, then run a simple SELECT against the table. If the numbers line up, the inferred schema is sound and you can move on to querying or charting.

TextQuery is a desktop app for macOS and Windows, and a free version is available to evaluate before upgrading.

Desktop software is a program you install and run directly on your computer's operating system (Windows, macOS, or Linux), rather than inside a browser tab. You should choose it over a browser extension or web app when your task needs deeper system access, steadier performance, or offline capability — for example, real-time meeting transcription that captures audio from multiple apps. Browser extensions are lighter and install in seconds, but they live inside one browser and inherit its limits.

What "desktop software" actually means

A desktop app is installed on the machine and runs as its own process. It can:

• Read and write local files without a download/upload round trip
• Access system-level resources like the microphone, speakers, camera, and other running apps
• Keep working when the browser is closed or the network drops (depending on the app)
• Persist settings and data locally

This is different from a web app, which runs in a browser tab and depends on a live connection and browser permissions, and from a browser extension, which is a small add-on that modifies or extends one browser's behavior.

Desktop app vs. browser extension vs. web app

The practical takeaway: extensions and web apps are convenient and low-commitment; desktop apps trade a heavier install for capability and stability.

When a desktop app is the better choice

Pick desktop software when the task depends on one or more of these:

• Capturing audio from multiple sources at once — a meeting where you're on a call and taking notes. A desktop notetaker can tap system audio and the mic; a browser extension generally can't reach outside its tab.
• Real-time transcription and live summaries — Otter's Meeting Agent is described as supporting real-time transcription, live chat, automated summaries, insights, and action items. That kind of continuous processing benefits from a local process rather than a single browser tab.
• Long or resource-heavy sessions — recording and transcribing for an hour is more stable outside a tab you might accidentally close.
• Working across apps — if your workflow spans a video call app, a document, and a notes tool, a desktop app can sit across all of them.

Choose a browser extension instead when you only need to enhance one website (for example, adding a button to a specific web tool), want zero install friction, and don't need system-level access.

How to choose and install a desktop app safely

1. Confirm the task fits a desktop app. If you only need to tweak one website, an extension is lighter. If you need system audio, local files, or offline use, go desktop.
2. Check system requirements. Match the OS (Windows/macOS/Linux) and version. Download only from the vendor's official site — for Otter, that's otter.ai.
3. Review permissions before granting. A meeting/transcription app will ask for microphone (and possibly screen or system audio) access. Grant only what the task needs.
4. Install and sign in. Follow the vendor's installer. If the app requires an account, expect a login step.
5. Verify it works. Run a short test — record 30 seconds and confirm transcription or capture appears as expected.
6. Check update and pricing terms. Otter lists a pricing page at otter.ai/pricing; check it for current plan limits rather than assuming a free tier.

Troubleshooting common desktop software problems

• Install fails — confirm OS version meets requirements, free up disk space, and re-download from the official source (a corrupted installer is a common cause).
• App crashes on launch — update to the latest version, restart the machine, and check whether a security tool is blocking it.
• No audio captured — re-check microphone/system-audio permissions in OS settings, not just in the app.
• Sync or login issues — verify your network and credentials; if the app stores data locally, confirm it isn't blocked by a firewall.
• Extension works but desktop doesn't (or vice versa) — they use different permission models; a feature available in one may not exist in the other.

If your goal is AI meeting transcription with live summaries and action items, a desktop app is usually the more capable choice; if you just need a small tweak inside one website, a browser extension is the faster path.

TextQuery is a desktop app for macOS and Windows that lets you import raw data files, query and modify them with SQL, and turn the results into charts — all locally on your machine. It fits you best if you already know some SQL and want to analyze local files (spreadsheets, CSVs, JSON, archives) without setting up a database, defining a schema, or writing import code. If you don't know SQL at all, the SQL editor will be the main thing to learn before the app is useful.

What TextQuery actually does

The app covers four steps in one place: import, query, modify, and visualize.

• Import — Load data files in a few clicks. TextQuery says you don't need to define a schema, write code, or run commands.
• Query — Use a built-in SQL editor to explore and transform the imported tables.
• Modify — Edit values inline and commit the changes, or filter rows without writing queries by hand.
• Visualize — Build charts inside the app and export or copy them.

Supported file formats

Because archives are supported, you can point TextQuery at a compressed file and let it handle the contents rather than unpacking everything manually first.

The SQL editor

The editor is positioned as the core of the app. Its listed features:

• SQL autocomplete
• Query history and favorites
• Query formatter
• Multiple selections

If you already write SQL, the site claims there's "essentially no learning curve" — one reviewer, Greg Williams, describes it that way and calls the chart feature "fantastic." That's a user's impression, not a guarantee, but it matches the app's stated design goal of skipping setup work for people who know SQL.

Charts and visualization

You can create charts directly in the app and customize them:

• Chart types: line, bar, area, scatter, pie
• Customization: title, description, colors
• Output: export to a file or share from the clipboard

This means a typical workflow — import a CSV, run a query, chart the result — stays inside one window instead of moving between a database tool and a separate charting app.

Features that speed up everyday work

• Inline editor — Edit values directly, collect your changes, and commit them in one go.
• Filters — Narrow down table rows without writing SQL manually.
• Tabs — Work on multiple queries and tables at the same time.
• Data export — Export as CSV, JSON, Excel, or SQL, or create a new table from your results.
• Keyboard shortcuts — Frequently used functions are reachable by shortcut.

Privacy and local analysis

TextQuery states that it neither records nor sends anything related to your usage, and it's built as a local desktop app. That matters if you're working with sensitive data you don't want uploaded to a third-party service. The trade-off of a desktop app versus a browser extension is exactly this: local processing and file access, at the cost of installing software and updating it yourself.

Pricing and platform

• Platforms: macOS and Windows.
• Pricing model: A perpetual license — "Pay Once, Use Forever" — with free updates, rather than a subscription.
• Free version: There's a free version with "certain limitations" you can evaluate before upgrading to Pro.

The site links to a pricing page but doesn't list specific prices here, so check that page for current numbers and what the free tier limits.

When TextQuery is a good fit

Choose it if:

• You're comfortable with SQL and want to query local files quickly.
• Your data lives in spreadsheets, CSVs, JSON, or archives rather than a database.
• You want charts without exporting to another tool.
• You care about keeping data on your own machine.

Look elsewhere if:

• You don't want to learn SQL — the app is SQL-centric.
• You need a hosted, collaborative, or browser-based tool.
• You need to connect to live database servers rather than import files (the site describes file import, not server connections).

To try it, download the free version for macOS or Windows, import one of your own files, and run a simple query to see whether the workflow matches how you already work.

TextQuery is a one-time purchase with a perpetual license, not a subscription. According to the site, the license includes free updates, and the company explicitly frames this as an alternative to "needless subscriptions." On privacy, TextQuery states that it neither records nor sends anything related to your usage, and it is positioned as a local desktop app for analyzing data — so sensitive data can be queried without it leaving your machine. A free version is also available with "certain limitations," which you can use to evaluate the app before upgrading to Pro.

Licensing: What You Actually Get

The practical implication: there is no recurring billing described on the site. You pay once and keep using the version you licensed, with updates included. If you want to test it first, the free version lets you do that before committing to Pro.

One thing the site does not state in the provided material: the actual price, the specific limitations of the free version, or whether the free version is time-limited. If those details matter to your decision, check the pricing page directly rather than assuming.

Privacy: What the Site Claims

TextQuery's privacy position rests on two stated points:

• Local analysis. It is described as a desktop app to "import, query, modify, and visualize your raw data with SQL" — the workflow happens on your machine, not in a browser session tied to a remote service.
• No usage recording or transmission. The site states plainly: "TextQuery neither records nor sends anything related to your usage."

The site connects this directly to sensitive data: "You can query sensitive data with complete peace of mind." That is the vendor's claim, not an independently verified audit — treat it as the company's stated design intent rather than a certification.

Why this matters for your use case

If you handle data you would not upload to a cloud BI tool — customer records, internal financials, health-adjacent datasets — a local desktop app removes the upload step entirely. That is the core argument for TextQuery over browser-based query tools. The trade-off is that you manage the file locally and there is no built-in collaboration or cloud sync described in the source material.

Who This Fits

TextQuery is a reasonable fit if you:

• Already know SQL and want a low-learning-curve tool (one user review notes "essentially no learning curve if you already know SQL")
• Prefer paying once over an ongoing subscription
• Need to query data you would rather not send to a third-party server
• Work with files like .xlsx, .csv, .tsv, .json, .zip, and similar formats

It may not fit if you:

• Need real-time multi-user collaboration or cloud-hosted dashboards (not described in the source)
• Want a browser extension rather than a desktop install
• Need to confirm exact pricing or free-version limits before deciding — those specifics are not in the material provided

How to Evaluate It Yourself

1. Download the free version for macOS or Windows from the site.
2. Import a representative file — ideally one with sensitive or messy data — and confirm the import works without defining a schema.
3. Run a query you would actually use, and test the SQL editor features (autocomplete, query history, formatter).
4. Check the privacy behavior in your own environment if that is a hard requirement for you.
5. Decide on Pro only after you hit a limitation that matters to your workflow.

The site reports it is "trusted by 1500+ individuals" and shows a 5-star review from a user named Greg Williams, who highlights the SQL familiarity and chart creation. Those are vendor-presented signals, not independent benchmarks — useful as context, not as proof.

Bottom Line

If your decision hinges on avoiding a subscription and keeping data local, TextQuery's stated model matches both: perpetual license with free updates, and no usage data recorded or sent. The free version lets you verify the fit before paying. The gaps to close on your own are the exact price and the specific free-version limitations, neither of which is specified in the material available here.

TextQuery's SQL editor is a desktop SQL workspace for imported files: you write queries with autocomplete, formatting, history, and favorites; edit table values inline and commit changes together; narrow rows with filters instead of writing WHERE clauses by hand; and keep multiple queries and tables open in tabs. It fits people who already know SQL and want to query local files without setting up a database server. It is a desktop app for macOS and Windows, not a browser tool.

What you can do in the editor

The editor is built around writing SQL against tables that TextQuery creates from your imported files.

• SQL autocomplete — suggests table and column names as you type, so you don't have to memorize the schema.
• Query history and favorites — previously run queries are kept, and you can save the ones you reuse.
• Query formatter — reformats a query for readability.
• Multiple selections — edit several parts of a query at once.
• Tabs — work on multiple queries and tables simultaneously instead of closing one to open another.

Because import doesn't require you to define a schema, writing code, or commands, the editor is the first place you interact with the data after loading a file.

Editing data without leaving the editor

Two features cover the common "I just need to fix a few values" case:

Inline editor

You edit values directly in the table, collect your changes, and commit them in one go. This matters when you want to review a batch of edits before they take effect rather than changing rows one at a time.

Filters

Filters narrow down a table's rows without writing queries manually. Use them when you want to inspect or work on a subset and don't need a reusable query.

A concrete workflow

For example, you have a CSV of orders and want to check totals for one region:

1. Import the file — TextQuery reads it into a table without schema definition.
2. Open a tab and write a query against that table; autocomplete fills in column names.
3. Run it, then save it to favorites if you'll rerun it.
4. If you only need to eyeball a subset, apply a filter instead of editing the query.
5. Fix any bad values with the inline editor, collect the changes, and commit them together.
6. Export the result as CSV, JSON, Excel, or SQL, or create a new table from the query results.

Where it fits and where it doesn't

TextQuery states it neither records nor sends anything related to your usage, which is the basis for querying sensitive data locally. It is available for macOS and Windows, and there is a free version with certain limitations plus a Pro upgrade; the site describes a perpetual license with free updates rather than a subscription. Check the pricing page for current terms before deciding.

Common snags

• Assuming browser access — this is a desktop app; there is no browser extension version described.
• Expecting schema setup — import is designed to skip schema definition, so don't look for a table-creation wizard before you can query.
• Editing without committing — inline edits are collected and committed together, so changes aren't live until you commit.
• Reaching for SQL when a filter is faster — for quick row narrowing, filters avoid writing a query at all.