Private by design

Data Ignix

Analyze CSV datasets directly in your browser.

Profile columns, spot data quality issues, and understand unfamiliar data in seconds. Your CSV stays on your device, with no accounts, uploads, or AI calls.

Drag and drop your CSV here

Your file stays in this browser tab and is never uploaded.

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See it in action

Demo dataset analysis

Explore a small synthetic CSV example to see how DataIgnix profiles columns, detects data quality issues, and summarizes a dataset.

Report panels

Dataset overview

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File: products-demo.csv
Size: 4.4 KB
Rows: 60
Columns: 10
Format: CSV

Summary report

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This dataset contains 60 records and 10 fields.

Useful grouping fields include category and brand.

product_id, product_name and price have high uniqueness and may act as an identifier.

2 fields are sparse and may need special handling.

The dataset has several compact dimensions that could support filtering and discovery.

Columns

10 total

product_idproduct_namecategorybrandpricestock_quantityratingadded_datedescriptionlegacy_code

Raw preview

First 10 of 60 rows

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product_id	product_name	category	brand	price	stock_quantity	rating	added_date	description	legacy_code
PRD-001	Wireless Mouse	Electronics	Northstar	24.99	84	4.5	2026-01-08	Compact mouse with silent buttons	empty
PRD-002	Mechanical Keyboard	Electronics	Northstar	79.99	36	4.7	2026-01-12	empty	empty
PRD-003	USB-C Hub	Electronics	BluePeak	39.50	52	4.3	2026-01-18	empty	empty
PRD-004	Laptop Stand	Office	Worksmith	34.00	41	4.6	2026-01-23	empty	empty
PRD-005	Desk Lamp	Home	BrightHome	29.95	67	4.4	2026-01-29	Adjustable LED desk lamp	empty
PRD-006	Notebook Set	Stationery	PaperTrail	12.50	125	4.2	2026-02-03	empty	empty
PRD-007	Gel Pen Pack	Stationery	PaperTrail	8.99	210	4.1	2026-02-07	empty	empty
PRD-008	Water Bottle	Sports	TrailCore	18.75	93	4.6	2026-02-13	empty	empty
PRD-009	Yoga Mat	Sports	TrailCore	32.00	48	4.5	2026-02-18	empty	empty
PRD-010	Canvas Backpack	Accessories	UrbanNest	44.90	31	4.4	2026-02-24	Padded compartment for a laptop	empty

Column profiles

10 fields

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Field	Type	Nulls	Null %	Unique	Uniqueness	Examples
product_id	text	0	0%	60	100%	PRD-001 / PRD-002 / PRD-003 / PRD-004 / PRD-005
product_name	text	0	0%	60	100%	Wireless Mouse / Mechanical Keyboard / USB-C Hub / Laptop Stand / Desk Lamp
category	text	0	0%	6	10%	Electronics / Office / Home / Stationery / Sports
brand	text	0	0%	7	11.7%	Northstar / BluePeak / Worksmith / BrightHome / PaperTrail
price	number	0	0%	60	100%	24.99 / 79.99 / 39.50 / 34.00 / 29.95
stock_quantity	number	0	0%	60	100%	84 / 36 / 52 / 41 / 67
rating	number	0	0%	9	15%	4.5 / 4.7 / 4.3 / 4.6 / 4.4
added_date	date	0	0%	60	100%	2026-01-08 / 2026-01-12 / 2026-01-18 / 2026-01-23 / 2026-01-29
description	text	47	78.3%	13	100%	Compact mouse with silent buttons / Adjustable LED desk lamp / Padded compartment for a laptop / Undated planner with weekly spreads / Soft woven blanket for sofas and beds
legacy_code	text	60	100%	0	0%	-

Dataset quality

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Empty columns

Only empty values

legacy_code

Sparse columns

More than 70% empty

descriptionlegacy_code

Low cardinality

Fewer than 20 unique values

categorybrandratingdescription

Identifiers

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Possible IDs

100% of present values are unique

product_id, product_name, price, stock_quantity, added_date, description

Possible external IDs

Names suggest a reference, such as user_id, categoryId, key or ref

product_id

What is DataIgnix?

DataIgnix is a browser-based CSV dataset analyzer for builders, developers, founders, researchers, and data explorers. It turns an unfamiliar CSV into a clear overview of its structure, quality, and useful fields.

What can it analyze?

The current MVP supports CSV files. It profiles columns, infers common data types, counts missing and unique values, detects possible identifiers, and highlights sparse, empty, and low-cardinality fields.

Why browser-based?

Your file is parsed and profiled locally in your browser. No account, backend, database, upload, or AI call is required, keeping analysis quick and your dataset on your device.

Common Use Cases

Understand an Unknown Dataset

Identify columns, possible identifiers, sparse fields, and quality issues before building on the data.

Evaluate Dataset Quality

Find missing values, empty columns, and unreliable fields before investing in deeper analysis.

Explore Downloaded Datasets

Inspect CSV files from public data portals, APIs, internal exports, or dataset libraries in seconds.

Prepare Data for Products

Check whether data can support catalogs, directories, search, recommendations, or research projects.

Validate CSV Exports

Confirm that an export contains the expected fields and quality before sharing or importing it.

Discover Useful Dimensions

Find categories, dates, ratings, locations, and other fields suited to filtering and reporting.

Frequently Asked Questions

What is a CSV dataset analyzer?

It helps you understand the structure, quality, and usefulness of a CSV file without manually inspecting every row and column.

Does DataIgnix upload my data?

No. Your file remains in your browser during analysis and is not sent to a server.

What can DataIgnix detect?

It detects column types, possible identifiers, sparse and empty fields, low-cardinality fields, and summary insights.

Who is DataIgnix for?

Builders, developers, founders, researchers, and anyone working with unfamiliar CSV datasets.

Does DataIgnix require AI?

No. The MVP uses deterministic local analysis and does not require AI or external APIs.

What file formats are supported?

DataIgnix currently supports CSV files only.