Tabular Data Schema
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To ingest data via or sources, you must follow the established schema so that the ingestion process is seamless.
By conforming your data to the proper schema, you will be able to bring your first-party data into your node which is the first step to start collaborating with your partners.
Prior to uploading a XLSX
CSV
or TSV
file or connecting to your Snowflake database, you must format your data using a predefined convention so your DCN can ingest it successfully. There are two column types that are processed: identifier
and trait
. Each column type will require a specific header format to be properly processed.
There are 2 classes of identifiers
: matchable and custom. For more information on what the values of each type could look like, head over to .
The identifier
column must match one of the following headers in order for your node to recognize the values.
Email address
id_e
Raw email or pre-normalized (lowercase + space trimmed) and SHA256 hashed
Phone number
id_p
Raw phone or pre-normalized (E.164) and SHA256 hashed
IPv4 address
id_i4
Four period (.) separated octets, each of which contains a number from 0 to 255
IPv6 address
id_i6
Eight colon (:) separated segments, each of which contains 4 hexadecimal digits, lowercase
Postal Code
id_z
Down-cased alphanumeric value, special characters removed. Maximum 10 characters.
Apple IDFA
id_a
Down-cased alphanumeric value, including dashes if any
Google GAID
id_g
Down-cased alphanumeric value, including dashes if any
Roku RIDA
id_r
Down-cased alphanumeric value, including dashes if any
Samsung TV TIFA
id_s
Down-cased alphanumeric value, including dashes if any
Amazon Fire AFAI
id_f
Down-cased alphanumeric value, including dashes if any
NetID
id_n
Base64 web-safe encoded value with 70 characters (TPID)
ID5
id_id5
Down-cased alphanumeric value, prefixed by "ID5-"
Utiq
id_utiq
Down-cased alphanumeric value, including dashes if any
Custom ID
id_c
,
id_c1
, id_c2
, id_c3
, id_c4
, id_c5
,
id_c6
,
id_c7
,
id_c8
,
id_c9
Free form text, case sensitive. Cannot be used for matching, for any subsequent ids, +1 at the end of the header name (from 1 to 9)
Optable VID
id_v
Alphanumeric string.
Identifier (catch-all)
id
Any type-prefixed ID. If we do not successfully detect a valid prefix (for example "e:", "i4:", "a:", etc.) for the identifier, we will attempt to auto-detect raw email addresses and phone numbers and automatically encode and type-prefix them at import time
Traits
All trait
columns are prefixed with trait_
followed by the trait key name.
For example, columns named trait_age
, trait_gender
and trait_owns_house
would map to traits age
, gender and owns_house
, respectively.
All traits are associated to the primary identifier and not the neighboring IDs.
Trait values are optional.
If header formatting conventions are followed and the DCN can properly ingest all profiles, there are a set of rules that will be applied upon ingestion:
The first ID column is treated as the primary ID in the associated ID profile
The value specified for the first ID column must be valid, otherwise the entire row is ignored
Additional ID columns (if any) are treated as neighbouring IDs
The values specified for additional ID columns are optional. If no value is provided, that specific neighbour ID will be ignored but the remaining data specified in the row will still be ingested.
In the following section, you can find examples of how your data should be structured with varying parameters:
Example 1: A simple list of emails
id1@gmail.com
id2@mail.com
In the example above, 2 distinct person clusters will be created with their respective email identifiers.
Example 2: Two profiles with multiple emails and some traits.
id1@gmail.com
id10@gmail.com
25000
yes
id2@mail.com
no
In the example above, 2 distinct person clusters will be created but only the first will have 2 email identifiers.
Example 3: Two profiles with a neighbouring ID and some traits.
id1@gmail.com
555-555-5555
yes
id2@mail.com
no
100000
In the example above, the email provided is the primary identifier and the phone number is a neighboring ID within this profile. Any trait definition is associated to the email.
Example 4: Four profiles with a mixture of ID types with multiple neighbours and traits.
i4:8.8.8.8
555-555-5555
50000
54
s:e0ef86a8-6ebf-4c9d-9127-e69407fe748d
+15145551234
25000
24
f:6e853799-ef31-4a30-8706-9742be254d38
24000
i6:2001:0db8:85a3:0000:0000:8a2e:0370:7334
+21630010123
56
In the example above, the primary identifier for a profile is the id
column which allows for any type of ID. The email column is the neighboring ID and the traits columns are associated to the primary id
.
Upon initiating an ingestion, the DCN may reject the file or view due to one or more of the following errors:
All headers of CSV
, TSV
or XSLX
files must be valid. Any invalid header will prevent the entire file from being ingested, and the last successfully ingested file (if any) will remain active.
Unrecognized header(s)
Could not find any valid ID header
No id
or id_<type>
header was found.
The file must contain at least one id column, with either the id_<type>
prefix or id
as a catch-all column with type prefixes declared for each value.
Duplicate trait header(s)
There are at least two trait_
headers with the same name.
Each trait_
header must have a unique suffix.
Empty header(s)
Trait header must start with "trait_" and must be followed by at least 1 character.
The trait header has no name - the prefix must be followed by any character to identify your trait. You must add at least 1 character after the prefix in order for your trait column to be recognized.
During file ingestion, if the file is a CSV
, TSV
or XSLX
, your DCN validates the header first and if it passes validation, a schema is generated from it. Your DCN will check individual records & rows for errors that may cause an ingestion failure, there are three possible errors:
Line # has more (or less) columns than headers.
The number of columns does not match the number of defined headers. Typically this will be because of an extra field delimiter (,
for CSV and tab for TSV).
Bare quotes found in unquoted field.
A "
inside an unquoted field. Example: ...,to"d@gmail.com,..."
Extraneous or missing quote in quoted-field.
Either a "
at the beginning or end of a field or an extra "
inside a quoted field.
Example 1: ...,"tod@gmail.com,...
Example 2: ...,tod@gmail.com",...
Example 3: ...,"to"d@gmail.com",...
Your data is rejected if the DCN is unable to recognize more than 10% of the records within your data - nothing is ingested if this threshold is passed. The data must be formatted to ensure the accepted convention is followed.
For example, if 5 records out of 10 have no identifier, the entire file will be rejected because the rejection rate is too high (50% - above the 10% threshold). The last successfully ingested file (if any) will remain active.
The same logic applies for any types of errors - the file will be rejected if more than 10% of them are invalid.
Once the data has been formatted to the accepted convention, you will be able to proceed with your setup or begin ingestion from your database. When done, you can create an audience based off your source which can be used for , or to .
The header(s) do not follow Optable's convention and could not be recognized. All headers must be recognized. The unrecognized headers must be renamed to the .
There is at least one header without any name. Headers with empty names (or only whitespace) are forbidden. You must name each column according to Optable's or discard it.