An Excel worksheet holds 1,048,576 rows. Long before that, a workbook full of lookups slows to a crawl, the file balloons, and the morning report becomes a copy-paste ritual. There are good ways past that limit, and they differ in what you give up: the grid, the formulas, the desktop, or control over where the data goes. This guide compares seven options — two that keep you in Microsoft's world, two that swap the spreadsheet for code, two cloud spreadsheets built for big data, and one desktop workspace that keeps the grid. A note on fairness: ATX Institute makes ATX AnalyticsTable, one of the seven. The others are described from their own published documentation as of October 2026, including where they are the better choice. Prices and features change over time, so parts of this comparison may no longer be up to date — always check the current prices and features on each vendor's official website before you decide.
Excel's grid is a fixed size — 1,048,576 rows by 16,384 columns per worksheet — and a workbook stores every value it shows, so the file grows with every row loaded. Formulas read cells, which means the rows have to be in the sheet before anything can be summed. Past a few hundred thousand rows, recalculation, filtering and saving all slow down, and anything repeated every day (refresh, recalculate, export, e-mail) depends on a person or a macro.
The alternatives below solve different parts of that: some lift the row limit, some move the work into a database engine, and some also automate the repeat.
What it is: a desktop analytics workspace with a familiar spreadsheet grid on top of DuckDB. Tables of 100 million rows and more stay in the engine and are queried in place; only the result lands in the grid. Formulas such as =SUM_SQL() and =COUNT_SQL() push the calculation down to the database instead of pulling rows into the sheet.
Where it runs: on your own machine — Windows today, Linux in release testing — or as a server on your own Linux infrastructure. Nothing is uploaded to a vendor cloud.
Cost: free for six months with nothing disabled, then a permanent Free edition for private or commercial use (3 sheets of up to 1,000 rows per project); an Individual licence of 100 a year, or 65 a year on a three-year term; Corporate plans per user per month.
Strengths: the 100-million-row benchmark aggregates a column in about a third of a second on an ordinary four-thread laptop; above roughly 20,000 rows a project keeps the query, not the rows, so it stays small and saves in seconds; connects to PostgreSQL, SQL Server, Oracle, BigQuery, MySQL / MariaDB, DuckDB and SQLite (Snowflake, Databricks SQL and Amazon Redshift in preview); pipelines, scheduling, PDF and Excel export, e-mail delivery, PII masking, row- and column-level security and key-driver, cluster and forecast analysis are built in.
Limits: it is a new tool to install rather than a feature of Excel; macOS is on the roadmap rather than released; and people who share workbooks back and forth will still exchange .xlsx files, which ATX AnalyticsTable reads and writes.
Best for: analysts and finance teams who want to keep working in a grid but on tens or hundreds of millions of rows, with the daily report running itself.
What it is: Excel's own route to larger data. Power Query loads and reshapes data; the Data Model behind Power Pivot stores it in compressed columns and serves PivotTables and DAX measures.
Where it runs: inside desktop Excel for Windows.
Cost: included with Microsoft 365 and recent Excel versions.
Strengths: no new tool to adopt; Microsoft documents a maximum of 1,999,999,997 rows per Data Model table; PivotTables over millions of rows remain responsive on good hardware.
Limits: Data Model rows never reach the grid, so ordinary cell formulas cannot use them; size is bounded by memory and by the 250 MB limit for workbooks opened in Excel for the web; refresh and distribution are still manual unless you add Power Automate or Power BI.
Best for: Excel-native teams whose data has outgrown the sheet but still fits on one machine.
What it is: the standard data-analysis library for Python, usually used in a notebook.
Where it runs: anywhere Python runs, locally or on a server.
Cost: free and open source.
Strengths: unlimited flexibility, a vast ecosystem for statistics and machine learning, and scripts that can be scheduled and version-controlled.
Limits: it requires programming; a standard pandas DataFrame lives in memory, so very large files need a bigger machine, chunking or another engine; and the result is code, not something a business user can open and adjust.
Best for: analysts who code, and one-off data science work.
What it is: a fast in-process analytical database — often described as SQLite for analytics. It reads and writes CSV, Parquet and JSON directly and answers SQL over hundreds of millions of rows on a laptop.
Where it runs: embedded in another program or from its command line, on your own machine.
Cost: free, under the permissive MIT licence.
Strengths: remarkable speed, no server to run, and it queries large files where they sit without importing them.
Limits: it is a SQL engine, not an application — no grid, charts, scheduling or sharing unless you build or add them. ATX AnalyticsTable is one way to put a grid, pipelines and reporting on top of it.
Best for: engineers and SQL-literate analysts who need raw speed on local files.
What it is: a web-based, no-code spreadsheet for very large files, which describes itself as a cloud big-data analytics platform handling billions of rows.
Where it runs: in Gigasheet's cloud, in the browser.
Cost: free and paid plans; see Gigasheet's pricing page for current limits.
Strengths: open a multi-gigabyte CSV in a browser with no installation; filtering, grouping and pivots without formulas or code.
Limits: your data is uploaded to a third-party cloud, which rules it out for many regulated datasets; it is focused on exploring files rather than on connected, scheduled reporting.
Best for: occasional exploration of very large files that are not sensitive.
What it is: a cloud spreadsheet built for warehouse-scale data, with familiar spreadsheet functions and connectors to Snowflake, Databricks, Redshift, BigQuery, PostgreSQL and SQL Server.
Where it runs: in Row Zero's cloud, in the browser.
Cost: a free plan that it says supports tens of millions of rows; paid plans listed from $15 per user per month, with billion-row spreadsheets on Enterprise.
Strengths: spreadsheet formulas at large scale, and direct connections to cloud data warehouses, including writing back.
Limits: cloud-hosted, so the data leaves your infrastructure; built around cloud warehouses rather than on-premises databases.
Best for: teams whose data already lives in Snowflake, Databricks or BigQuery and who want a spreadsheet on top of it.
What it is: Google Sheets is capped at 10 million cells per spreadsheet, but Connected Sheets lets it analyse BigQuery tables with pivot tables, charts and formulas, while the data stays in BigQuery.
Where it runs: Google Workspace and Google Cloud.
Cost: Connected Sheets is available on selected Google Workspace editions; BigQuery is billed separately for storage and queries.
Strengths: billions of rows behind a familiar Sheets interface, with sharing and collaboration built in.
Limits: only works with BigQuery, needs the right Workspace edition, and every query is billed; extracting data back into an ordinary sheet hits the 10-million-cell limit.
Best for: organisations already on Google Workspace and BigQuery.
The seven options in one view:
Answer these before trying a tool:
ATX AnalyticsTable is for the case where the data has outgrown Excel but the people have not outgrown the grid: tens or hundreds of millions of rows, on your own machine or server, with the daily refresh, export and e-mail handled by a schedule. If your data is in BigQuery and your company runs on Google Workspace, Connected Sheets may be simpler; if you code, pandas or DuckDB alone may be all you need. The detailed ATX AnalyticsTable vs Excel comparison goes through 63 differences row by row.
An Excel worksheet holds 1,048,576 rows by 16,384 columns. Excel's Data Model, used by Power Pivot, can hold far more — Microsoft documents 1,999,999,997 rows per table — but those rows are not shown in the grid.
Inside Microsoft's tools, Power Pivot and Power Query. Outside them: ATX AnalyticsTable for a desktop grid on DuckDB, pandas or DuckDB if you write code, and Gigasheet, Row Zero or Google Connected Sheets if your data may be processed in a vendor's cloud.
Load it into a database engine instead of a worksheet. DuckDB reads large CSV and Parquet files directly; ATX AnalyticsTable imports them into its built-in DuckDB and shows the results in a grid; cloud tools such as Gigasheet open large files in the browser after upload.
Yes. ATX AnalyticsTable runs on your own machine or server and connects to on-premises databases directly, with no vendor cloud involved. Excel's Power Pivot also runs locally, within the limits of one machine's memory.
Yes. It imports .xlsx workbooks and exports results back to .xlsx, so colleagues who stay in Excel are not affected.
This site needs JavaScript for the full experience. Every page above is reachable without it.