CureData Documentation

Define, automate, and enforce business-grade data validations — all in a single page app.

Getting started

Upload CSV or XLSX (most common), or JSON. We auto-detect headers and sample five rows. You can also start with a blank schema.

Sample input

Email,Amount,StartDate,EndDate,State
alice@example.com,12.50,2024-01-10,2024-01-12,CA
bob@example.com,0,2024-02-01,2024-02-01,WA
,19.99,2024-03-05,2024-03-06,TX

Tips & behavior

Header row required (first line).
Delimiter auto-detects (, default). Supports quotes and newlines in fields.
Dates: prefer ISO (YYYY-MM-DD).
Numbers: plain decimal (no currency symbols).
Saved rulesets map to headers using a strategy: EXACT / CI / FUZZY.

Pro tip: Upload first, then click “Use Detected Headers” to build a starter schema.

Schemas & columns

A schema is a named collection of columns. Each column carries 0..n rules. Order doesn’t matter — rules are evaluated in an optimized pass.

Column config

{
  name: "StartDate",
  rules: [
    { type: "type", expected: "date" },
    { type: "noFutureDate" }
  ]
}

Cross-field logic

{
  name: "EndDate",
  rules: [
    { type: "type", expected: "date" },
    { type: "dateOrder", otherField: "StartDate", op: ">=" }
  ]
}

Header matching strategy

When you apply a saved ruleset to a new upload, we need to match your file’s headers to the ruleset’s column keys. The header_match_strategy controls how strictly we match names.

Strategy	Match rule	Example	When to use
EXACT	Names must match exactly	ruleset key NPI ⇄ header NPI	Strict internal feeds with stable headers
CI	Case-insensitive match	ruleset key NPI ⇄ header npi, Npi	Vendors who vary capitalization only
FUZZY	Loose/approximate name match	ruleset key ProviderNPI ⇄ header NPI Number	External files with inconsistent naming

Tip: If your uploads already match your ruleset keys, use EXACT. If only capitalization varies, use CI. Otherwise choose FUZZY.

Rule Packs (one-click presets)

Email (basic)

Best for: Contact emails

requiredtype:stringemail

US Phone

Best for: US phone numbers

type:stringphoneUS

US Address Lite

Best for: Ship address

stateCodeUSzipUS

Money

Best for: Prices, totals

type:numberrange≥0precision:2

Walkthrough: Sample Orders

orderId → required, type:string, length 8–36, unique_in_file
email → required, type:string, email
phone → type:string, phoneUS
amount → type:number, range ≥ 0, precision 2
order_date → type:date, noFutureDate
ship_date → type:date, dateOrder: ship_date ≥ order_date
status → enum [Pending, Processing, Shipped, Delivered, Cancelled]
ifThen → If status="Cancelled" then ship_date blank
state / zip → stateCodeUS, zipUS

Rules reference

Core rules ship built-in. Add as many as needed per column. regex/enum can repeat. Note: Cross-field rules like dateOrder and ifThen reference other columns.

Rule	What it checks	Parameters	Expected value	Good example	Bad example
required Field cannot be blank or missing.	Value is present (not empty, not null).	`—`	any	"ORD-123"	(empty)
type:string Force the value to be text.	Value parses as a string.	`expected = string`	text	"hello"	42 (number)
type:number Force the value to be numeric.	Value parses as a number.	`expected = number`	number	19.99	"N/A"
type:date Force the value to be a valid date.	Value parses as a date.	`expected = date`	date	2024-05-01	"not-a-date"
type:boolean Force the value to be yes/no.	Value is boolean-ish (true/false, yes/no).	`expected = boolean`	boolean	true	"maybe"
length Restrict minimum / maximum characters.	Text length is within bounds.	`min, max`	text	orderId length 12	orderId length 2
enum Only allow these values.	Value is one of the provided list.	`values: [Pending, Processing, Shipped, Delivered, Cancelled]`	text (categorical)	"Shipped"	"Unknown"
regex Advanced pattern rule.	Value matches a regular expression.	`pattern, flags, name`	text	"ORD-202501-0042"	"ORD-Jan-42"
range Limit number to a range.	Number is ≥ min and ≤ max (inclusive by default).	`min, max, inclusive=true`	number	0 ≤ amount ≤ 100000	-4
precision Limit digits after the decimal.	Number has at most N decimals.	`scale: 2`	number	19.99	19.9999
dateOrder Ensure dates are in logical order.	This date compares correctly to another column.	`otherField, op: >, ≥, =, ≤, <`	date	ship_date ≥ order_date	ship_date 2025-01-01 < order_date 2025-02-01
noFutureDate Date must be today or earlier.	Date is ≤ today.	`—`	date	yesterday	next week
notExpired Date must be today or later.	Date is ≥ today.	`—`	date	next month	last year
ifThen Apply rules conditionally.	IF condition is true THEN target must satisfy requirement.	`if:{field, op, value} → then:{field, mustBe/op}`	varies	IF status=Cancelled THEN ship_date blank	Cancelled with ship_date present
email Must look like name@example.com.	Basic email shape.	`—`	text	"user@example.com"	"name@@example"
phoneUS Valid 10-digit US phone.	Accepts common US formats with 10 digits.	`—`	text	"(415) 555-0123"	"555-12-1234"
stateCodeUS Two-letter US state like CA, TX.	Matches official 2-letter postal code.	`—`	text	"CA"	"California"
zipUS 5-digit or ZIP+4.	##### or #####-####	`—`	text	"94107-1234"	"9410A"
Luhn Checksum validation (Luhn).	Passes Luhn checksum (often 10+ digits).	`—`	text/number	"79927398713"	"79927398710"

19 / 19 rules shown

Operators & Glossary

Comparison operators

== / = equal to
!= not equal
>, ≥, ≤, < greater/less (dates and numbers)
exists value present (not empty)
blank value must be empty

Field types

string any text; length/regex/enum apply
number decimal; range/precision apply
date ISO recommended (YYYY-MM-DD)
boolean true/false (1/0, yes/no accepted)

Validation context & phases

Phase 1 runs client-only with local rules. A lightweight ctx object provides things like now.

Context object

const ctx = { now: new Date() }

Exporting results

The demo exports a CSV of issues. Columns: rowIndex, column, code, message, value.

CSV example

rowIndex,column,code,message,value
12,Email,required,"Value is required",""
42,Amount,range,"Must be between 0 and 1000","-12"

FAQ & Troubleshooting

Can I add multiple regex/enums on a column?

Yes. regex and enum are repeatable.

Is column order important?

No. Evaluation is optimized independent of order.

Cross-row validations?

Use unique_in_file today; more dataset-level rules land via packs.

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