Generate Custom Sample Data with Confluent Cloud for Apache Flink

Confluent Cloud for Apache Flink® enables creating tables that generate custom sample data.

User-defined faker tables enable you to create custom data generators directly within your Flink stream-processing workflows. This capability enables you to generate a continuous stream of realistic test data without additional infrastructure or costs, streamlining development and testing processes.

With a faker table, data is not being generated continuously. Instead, when a statement is started that reads from the sample data, it is instantiated in this specific context and generates data only to be read by this specific statement. If multiple statements read from the same faker table at the same time, they get different data.

Create a faker table by using the CREATE TABLE statement with special connector options that specify how data should be generated. The following code example creates a table that generates mock product data.

-- Create a faker table that generates mock product data.
CREATE TABLE products_mock (
  `product_id` BIGINT NOT NULL,
  `name` VARCHAR(2147483647) NOT NULL,
  `brand_id` BIGINT NOT NULL,
  `vendor` VARCHAR(2147483647) NOT NULL,
  CONSTRAINT `PK_product_id` PRIMARY KEY (`product_id`) NOT ENFORCED
) WITH (
  'changelog.mode' = 'upsert',
  'connector' = 'faker',
  'fields.brand_id.expression' = '#{Number.randomNumber ''3'',''false''}',
  'fields.name.expression' = '#{Commerce.productName}',
  'fields.product_id.expression' = '#{Number.randomNumber ''4'',''false''}',
  'fields.vendor.expression' = '#{Commerce.vendor}',
  'rows-per-second' = '1'
);

-- Generate mock data from the faker table.
SELECT * FROM products_mock LIMIT 2;

Your output should resemble:

product_id name                    brand_id vendor
6805       Intelligent Paper Plate 15       Amazon
3466       Lightweight Steel Car   130      Target

For a complete list of available expressions, see the Faker table expressions section.

You can find more examples of faker tables in the following sections.

Supported faker table options

The following table shows the supported options for faker tables.

Option Required? Example Value Description
changelog.mode No ‘upsert’ Only append and upsert are supported.
connector Yes ‘faker’ Specifies using the faker connector.
fields.<column>.expression No ‘#{Name.firstName}’ The expression to use for the column.
number-of-rows No ‘100’ The number of rows to generate.
rows-per-second No ‘1’ The number of rows to generate per second.

Faker table expressions

The following sections show the expressions that you can use in faker tables.

String fields

Domain Raw expression In SQL (quoted)
Address #{Address.city} / #{Address.streetAddress} ‘#{Address.city}’
Address #{Address.countryCode} ‘#{Address.countryCode}’
Address #{Address.streetAddress} / #{Address.secondaryAddress} ‘#{Address.streetAddress}’
Address #{Address.zipCode} ‘#{Address.zipCode}’
Codes/IDs #{Code.isbn10} / #{Code.isbn13} / #{Code.imei} ‘#{Code.isbn13}’
Commerce #{Commerce.productName} / #{Commerce.department} / #{Commerce.material} ‘#{Commerce.productName}’
Company #{Company.name} / #{Company.industry} / #{Company.buzzword} ‘#{Company.name}’
Currency #{Currency.code} / #{Currency.name} ‘#{Currency.code}’
Email #{Internet.emailAddress} ‘#{Internet.emailAddress}’
Email #{Name.firstName}.#{Name.lastName}@example.com '#{Name.firstName}.#{Name.lastName}@example.com'
Enum pick #{Options.option ‘A’,’B’,’C’} ‘#{Options.option ‘’A’’,’’B’’,’’C’’}’
Finance #{Finance.iban} / #{Finance.bic} ‘#{Finance.iban}’
Geo #{Address.latitude} / #{Address.longitude} ‘#{Address.latitude}’
IDs #{IdNumber.valid} / #{Internet.uuid} ‘#{IdNumber.valid}’
Internet #{Internet.password} ‘#{Internet.password}’
Internet #{Internet.domainName} / #{Internet.url} ‘#{Internet.domainName}’
Internet #{Internet.publicIpV4Address} / #{Internet.ipV6Address} ‘#{Internet.publicIpV4Address}’
Internet #{Internet.macAddress} / #{Internet.userAgent} ‘#{Internet.userAgent}’
Job #{Job.title} / #{Job.field} ‘#{Job.title}’
Lorem #{Lorem.word} / #{Lorem.sentence} / #{Lorem.paragraph} ‘#{Lorem.sentence}’
Lorem #{Lorem.words ‘3’} ‘#{Lorem.words ‘’3’’}’
Lorem #{Lorem.sentences ‘2’} ‘#{Lorem.sentences ‘’2’’}’
Lorem #{Lorem.paragraphs ‘2’} ‘#{Lorem.paragraphs ‘’2’’}’
Lorem #{Lorem.characters ‘12’} ‘#{Lorem.characters ‘’12’’}’
Payment #{business.creditCardNumber} / #{business.creditCardType} ‘#{business.creditCardNumber}’
Person #{Name.firstName} ‘#{Name.firstName}’
Person #{Name.lastName} ‘#{Name.lastName}’
Person #{Name.fullName} ‘#{Name.fullName}’
Person #{Name.nameWithMiddle} ‘#{Name.nameWithMiddle}’
Person #{Name.prefix} / #{Name.suffix} ‘#{Name.prefix}’
Person #{Name.userName} ‘#{Name.userName}’
Phone #{PhoneNumber.phoneNumber} / #{PhoneNumber.cellPhone} ‘#{PhoneNumber.phoneNumber}’
Templating #{numerify ‘####-####’} ‘#{numerify ‘’####-####’’}’
Templating #{letterify ‘????’} ‘#{letterify ‘’????’’}’
Templating #{bothify ‘??##-##??’} ‘#{bothify ‘’??##-##??’’}’
Templating #{regexify ‘[A-Z0-9]{8}’} ‘#{regexify ‘’[A-Z0-9]{8}’’}’
Timezone #{Address.timeZone} ‘#{Address.timeZone}’
Vehicle #{Vehicle.vin} / #{Vehicle.manufacturer} / #{Vehicle.model} ‘#{Vehicle.vin}’
Weighted #{Options.option ‘gold’,’gold’,’silver’,’bronze’} ‘#{Options.option ‘’gold’’,’’gold’’,’’silver’’,’’bronze’’}’

Integer / Long fields

Data type Purpose Raw expression In SQL (quoted)
Integer/Long Age #{Number.numberBetween ‘18’,’65’} ‘#{Number.numberBetween ‘’18’’,’’65’’}’
Integer/Long Fixed digits #{Number.randomNumber ‘5’,’true’} ‘#{Number.randomNumber ‘’5’’,’’true’’}’
Integer/Long Quantity #{Number.numberBetween ‘1’,’10000’} ‘#{Number.numberBetween ‘’1’’,’’10000’’}’
Integer/Long Range #{Number.numberBetween ‘0’,’100000’} ‘#{Number.numberBetween ‘’0’’,’’100000’’}’

Decimal / Double / Money fields

Data type Purpose Raw expression In SQL (quoted)
Decimal/Double Bounded w/ scale #{Number.randomDouble ‘2’,’0’,’9999’} ‘#{Number.randomDouble ‘’2’’,’’0’’,’’9999’’}’
String (money) Price string #{Commerce.price} ‘#{Commerce.price}’

Boolean fields

Data type Purpose Raw expression In SQL (quoted)
Boolean Biased (~60/40) #{Options.option ‘true’,’true’,’true’,’false’,’false’} ‘#{Options.option ‘’true’’,’’true’’,’’true’’,’’false’’,’’false’’}’

Date / Time / Timestamp fields

Data type Purpose Raw expression In SQL (quoted)
Date Birthday 18–65 #{Date.birthday ‘18’,’65’} ‘#{Date.birthday ‘’18’’,’’65’’}’
Date/Time Between dates #{Date.between ‘2022-01-01’,’2022-12-31’} ‘#{Date.between ‘’2022-01-01’’,’’2022-12-31’’}’
Date/Time Future N days #{Date.future ‘7’,’DAYS’} ‘#{Date.future ‘’7’’,’’DAYS’’}’
Date/Time Past hours #{Date.past ‘12’,’HOURS’} ‘#{Date.past ‘’12’’,’’HOURS’’}’
Date/Time Past N days #{Date.past ‘30’,’DAYS’} ‘#{Date.past ‘’30’’,’’DAYS’’}’

File / Media / Miscellaneous fields

Data type Domain Raw expression In SQL (quoted)
String File #{File.fileName} ‘#{File.fileName}’
String File #{File.extension} / #{File.mimeType} ‘#{File.mimeType}’
String Image #{Placeholdit.image ‘200’,’300’} ‘#{Placeholdit.image ‘’200’’,’’300’’}’
String Locale #{Nation.language} / #{Nation.nationality} ‘#{Nation.language}’

Work with faker tables

Default faker table

The following code example creates a faker table that generates mock data with the default settings.

-- Create a faker table that generates mock data with the default settings.
CREATE TABLE faker_default (
  id BIGINT,
  name STRING,
  email STRING,
  city STRING
) WITH (
  'connector' = 'faker'
);

-- Generate mock data from the faker table.
SELECT * FROM faker_default LIMIT 2;

Your output should resemble:

id     name               email                     city
658137 hfucj              zufpk                     ltuot
71500  timdz              pyons                     bdczt

Modify a faker table

The following code example shows how to use the ALTER TABLE statement to modify an existing faker table.

-- Create a faker table that generates mock data with the default settings.
CREATE TABLE faker_basic (
  id BIGINT,
  name STRING,
  email STRING,
  city STRING
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'append',
  'fields.id.expression' = '#{Number.numberBetween ''1'',''1000000''}',
  'fields.name.expression' = '#{Name.name}',
  'fields.email.expression' = '#{Internet.emailAddress}',
  'fields.city.expression' = '#{Address.city}',
  'rows-per-second' = '5'
);

-- Generate mock data from the faker_like table.
SELECT * FROM faker_basic LIMIT 2;

Your output should resemble:

id     name               email                     city
583421 Reena Ortiz        jeff.auer@yahoo.com       Koelpintown
446894 Janel Howe         ruth.shields@hotmail.com  Lake Kirstinmouth

Run the following command to alter the name field in the faker_basic table to generate full names.

ALTER TABLE faker_basic
SET ('fields.name.expression' = '#{Name.fullName}');

-- Generate mock data from the faker_basic table.
SELECT * FROM faker_basic LIMIT 2;

Your output should resemble:

id     name                  email                        city
617469 Dr. Clemente Kassulke johanne.emard@gmail.com      South Arieborough
773314 Walker VonRueden III  paris.stoltenberg@gmail.com  Ferryborough

Use the LIKE clause to copy a faker table’s schema

You can use the LIKE clause to copy a faker table’s schema. Use the EXCLUDING merging strategy to exclude unnecessary options.

The following code example creates a faker table that generates mock data with the same schema as the faker_src table.

-- Create the source faker table.
CREATE TABLE faker_src (
  id BIGINT,
  name STRING,
  email STRING,
  city STRING
) WITH (
  'connector' = 'faker'
);

-- Create a faker table that generates mock data with the same schema
-- as the faker_src table.
CREATE TABLE faker_like LIKE faker_src;

-- Generate mock data from the faker_like table.
SELECT * FROM faker_like LIMIT 2;

Your output should resemble:

id     name               email                     city
658137 hfucj              zufpk                     ltuot
71500  timdz              pyons                     bdczt

Generate NULL data with a NULL rate

The following code example creates a faker table that generates mock data with a NULL rate of 50 percent.

CREATE TABLE faker_nulls (
  name STRING
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'append',
  'fields.name.expression' = '#{Name.name}',
  'fields.name.null-rate' = '0.5',
  'rows-per-second' = '5'
);

-- Generate mock data from the faker table.
SELECT * FROM faker_nulls LIMIT 10;

Your output should resemble:

name
Hunter Stoltenberg IV
Glen Kuhlman
NULL
NULL
NULL
Marco Kerluke
NULL
NULL
Abe Corkery
Homer Pfeffer

Generate one-of columns

The following code example creates a faker table that picks a random value from a list of options by using an Options.option expression.

CREATE TABLE order_status_faker (
  `order_id` INT,
  `order_status` STRING
)
WITH (
  'connector' = 'faker',
  'fields.order_id.expression' = '#{Number.numberBetween ''0'',''100''}',
  'fields.order_status.expression' = '#{Options.option ''RECEIVED'',''SHIPPED'',''CANCELLED''}'
);

-- Generate mock data from the faker table.
SELECT * FROM order_status_faker LIMIT 10;

Your output should resemble:

order_id order_status
33       CANCELLED
4        RECEIVED
77       CANCELLED
99       SHIPPED
10       RECEIVED
22       CANCELLED
55       CANCELLED
66       RECEIVED
88       SHIPPED
21       SHIPPED

Generate data for collections

Generate ARRAY data

The following code example creates a faker table that generates mock array data.

CREATE TABLE faker_array (
  tags ARRAY<STRING>,
  floats ARRAY<FLOAT>
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'append',
  'fields.tags.length' = '2',
  'fields.tags.expression' = '#{Lorem.words ''3''}',
  'fields.floats.length' = '3',
  'fields.floats.expression' = '#{Number.randomDouble ''2'',''0'',''10''}',
  'rows-per-second' = '5'
);

-- Generate mock data from the faker table.
SELECT * FROM faker_array LIMIT 2;

Your output should resemble:

tags                                             floats
[[omnis, qui, animi], [qui, et, omnis]]          [9.31, 3.49, 8.7]
[[amet, omnis, ducimus], [eum, corporis, magni]] [0.77, 5.11, 2.46]

Generate MAP data

The following code example creates a faker table that generates mock MAP data.

CREATE TABLE faker_map (
  attrs MAP<STRING, INT>
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'append',
  'fields.attrs.length' = '3',
  'fields.attrs.key.expression' = '#{Lorem.words ''3''}',
  'fields.attrs.value.expression' = '#{Number.numberBetween ''1'',''10''}',
  'rows-per-second' = '5'
);

-- Generate mock data from the faker table.
SELECT * FROM faker_map LIMIT 2;

Your output should resemble:

attrs
{voluptatem=5, consectetur=3, non=1}
{hic=5, mollitia=1, atque=2}

Generate MULTISET data

The following code example creates a faker table that generates mock MULTISET data.

CREATE TABLE faker_multiset (
  id BIGINT,
  tags MULTISET<STRING>
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'append',
  'fields.id.expression' = '#{Number.randomNumber ''4'',''false''}',
  'fields.tags.length' = '4',
  'fields.tags.expression' = '#{Lorem.words ''2''}',
  'rows-per-second' = '5'
);

-- Generate mock data from the faker table.
SELECT * FROM faker_multiset LIMIT 2;

Your output should resemble:

id     tags
139    {[ullam, eveniet]=1, [accusantium, iure]=1, [in, est]=1, [ea, similique]=1}
7168   {[nisi, pariatur]=1, [tempora, ullam]=1, [atque, ea]=1, [nisi, molestias]=1}

Generate ROW data

The following code example creates a faker table that generates mock row data.

CREATE TABLE faker_row (
   name STRING,
  email STRING,
  addr ROW<city STRING, zip STRING>
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'append',
  'rows-per-second' = '5',
  'fields.name.expression' = '#{Name.name}',
  'fields.email.expression' = '#{Internet.emailAddress}',
  'fields.addr.city.expression' = '#{Address.city}',
  'fields.addr.zip.expression' = '#{Address.zipCode}'
);

Your output should resemble:

name                email                        addr
Santiago Stiedemann ethan.von@yahoo.com          (Cassinmouth, 02253)
Delores Murray      cristine.okuneva@hotmail.com (New Santana, 04034)

Example business objects

Category data

The following code example creates a faker table that generates mock category data.

-- Create a faker table that generates mock category data.
CREATE TABLE categories (
  `category_id` BIGINT NOT NULL,
  `name` STRING NOT NULL,
  CONSTRAINT `PK_category_id` PRIMARY KEY (`category_id`) NOT ENFORCED
) WITH (
  'changelog.mode' = 'upsert',
  'connector' = 'faker',
  'fields.category_id.expression' = '#{Number.randomNumber ''2'',''false''}',
  'fields.name.expression' = '#{Commerce.department}',
  'rows-per-second' = '1'
);

-- Generate mock data from the faker table.
SELECT * FROM categories LIMIT 2;

Your output should resemble:

category_id name
66       Books
86       Automotive

Customers

The following code example creates a faker table that generates mock customer data.

-- Create a faker table that generates mock customer data.
CREATE TABLE `customers_fake` (
  `customer_id` BIGINT NOT NULL,
  `name` VARCHAR(2147483647) NOT NULL,
  `address` VARCHAR(2147483647) NOT NULL,
  `postcode` VARCHAR(2147483647) NOT NULL,
  `city` VARCHAR(2147483647) NOT NULL,
  `email` VARCHAR(2147483647) NOT NULL,
  CONSTRAINT `PK_customer_id` PRIMARY KEY (`customer_id`) NOT ENFORCED
) WITH (
  'changelog.mode' = 'upsert',
  'connector' = 'faker',
  'fields.address.expression' = '#{Address.streetAddress}',
  'fields.city.expression' = '#{Address.city}',
  'fields.customer_id.expression' = '#{Number.randomNumber ''5'',''false''}',
  'fields.email.expression' = '#{Internet.emailAddress}',
  'fields.name.expression' = '#{Name.fullName}',
  'fields.postcode.expression' = '#{Address.postcode}',
  'rows-per-second' = '1'
);

-- Generate mock data from the faker table.
SELECT * FROM customers_fake LIMIT 2;

Your output should resemble:

customer_id name                  address                  postcode city              email
3134        Dr. Andrew Terry      45488 Eileen Walk        78690    Latoyiaberg       romaine.lynch@hotmail.com
3243        Miss Shelby Lueilwitz 199 Bernardina Brook     79991    Johnburgh         dominick.oconner@hotmail.c…

Email fields

The following code example creates a faker table that generates mock email data.

 -- Create a faker table that generates mock email data.
 CREATE TABLE faker_basic (
   id BIGINT,
   name STRING,
   email STRING,
   city STRING
) WITH (
   'connector' = 'faker',
   'changelog.mode' = 'append',
   'fields.id.expression' = '#{Number.numberBetween ''1'',''1000000''}',
   'fields.name.expression' = '#{Name.name}',
   'fields.email.expression' = '#{Internet.emailAddress}',
   'fields.city.expression' = '#{Address.city}',
   'rows-per-second' = '5'
 );

 -- Generate mock data from the faker table.
 SELECT * FROM faker_basic LIMIT 2;

Your output should resemble:

id     name               email                     city
946836 Maile Nader Jr.    bobby.paucek@gmail.com    Carmeliachester
371664 Dewitt Kemmer      shelby.harvey@yahoo.com   Renaldoton

Order data

The following code example creates a faker table that generates mock order data.

-- Create a faker table that generates mock order data.
CREATE TABLE `orders` (
  `order_id` VARCHAR(2147483647) NOT NULL,
  `customer_id` INT NOT NULL,
  `product_ids` ARRAY<BIGINT> NOT NULL,
  `price` DECIMAL(10,2) NOT NULL
) WITH (
  'changelog.mode' = 'append',
  'connector' = 'faker',
  'fields.customer_id.expression' = '#{Number.randomNumber ''5'',''false''}',
  'fields.order_id.expression' = '#{Internet.UUID}',
  'fields.price.expression' = '#{Number.randomDouble ''2'', ''10'', ''500''}',
  'fields.product_ids.expression' = '#{Number.randomNumber ''4'',''false''}',
  'fields.product_ids.length' = '5',
  'rows-per-second' = '1'
);

-- Generate mock data from the faker table.
SELECT * FROM orders LIMIT 2;

Your output should resemble:

order_id                             customer_id product_ids                    price
e6bbd9ae-a03f-43cb-8018-01a25f52c99b 3134        [5436, 796, 2373, 8492, 6236]  231.02
cbb4a3c9-0c24-4171-a4dc-729d7be34d97 3243        [4357, 5565, 2598, 5551, 5753] 48.74

Product categories

The following code example creates a faker table that generates mock product category data.

-- Create a faker table that generates mock product category data.
CREATE TABLE product_categories (
  `product_id` BIGINT NOT NULL,
  `category_id` BIGINT NOT NULL,
  `is_active` BOOLEAN NOT NULL,
  CONSTRAINT `PK_product_category_id` PRIMARY KEY (`product_id`, `category_id`) NOT ENFORCED
) WITH (
  'changelog.mode' = 'upsert',
  'connector' = 'faker',
  'fields.category_id.expression' = '#{Number.randomNumber ''2'',''false''}',
  'fields.product_id.expression' = '#{Number.randomNumber ''4'',''false''}',
  'fields.is_active.expression' = '#{Options.option ''TRUE'',''FALSE''}',
  'rows-per-second' = '1'
);

-- Generate mock data from the faker table.
SELECT * FROM product_categories LIMIT 2;

Your output should resemble:

product_id category_id is_active
6805       66          TRUE
3466       86          FALSE

Product details

The following code example creates a faker table that generates mock product details.

-- Create a faker table that generates mock product details.
CREATE TABLE `product_details` (
  `product_id` BIGINT NOT NULL,
  `dimensions` ROW<`length` STRING, `width` STRING, `height` STRING>,
  `initial_release_date` STRING,
  CONSTRAINT `PK_product_id` PRIMARY KEY (`product_id`) NOT ENFORCED
) WITH (
  'changelog.mode' = 'upsert',
  'connector' = 'faker',
  'fields.product_id.expression' = '#{Number.randomNumber ''4'',''false''}',
  'fields.dimensions.length.expression' = '#{Number.randomDouble ''2'', ''5'', ''500''}cm',
  'fields.dimensions.width.expression' = '#{Number.randomDouble ''2'', ''5'', ''500''}cm',
  'fields.dimensions.height.expression' = '#{Number.randomDouble ''2'', ''1'', ''1000''}cm',
  'fields.initial_release_date.expression' = '#{Number.numberBetween ''0'', ''1000000000''}',
  'rows-per-second' = '1'
);

-- Generate mock data from the faker table.
SELECT * FROM product_details LIMIT 2;

Your output should resemble:

product_id dimensions                   initial_release_date
1019       (93.55cm, 65.08cm, 366.88cm) 959199498
5104       (93.55cm, 65.08cm, 366.88cm) 909599576

TIMESTAMP and DATE data

For rows of type TIMESTAMP and DATE, the corresponding expression must return a timestamp formatted as yyyy-mm-dd hh:mm:ss[.nnnnnnnnn].

The following code example creates a faker table that generates mock TIMESTAMP and DATE data.

  • timestamp1: the table generates a random timestamp that occurs at most 15 seconds in the past.
  • timestamp2: the table generates a random timestamp that occurs at most 15 seconds in the past, but at least 5 seconds.
  • timestamp3: the table generates a random timestamp that occurs at most 15 seconds in the future, but at least 5 seconds.
  • time: the table generates a random time that occurs at most 15 seconds in the future, but at least 5 seconds.
  • date: the table generates a random birthday between 1 and 100 years ago.
CREATE TABLE faker_time (
  `timestamp1` TIMESTAMP(3),
  `timestamp2` TIMESTAMP(3),
  `timestamp3` TIMESTAMP(3),
  `time`       TIME,
  `date`       DATE
) WITH (
  'connector' = 'faker',
  'fields.timestamp1.expression' = '#{date.past ''15'',''SECONDS''}',
  'fields.timestamp2.expression' = '#{date.past ''15'',''5'',''SECONDS''}',
  'fields.timestamp3.expression' = '#{date.future ''15'',''5'',''SECONDS''}',
  'fields.time.expression' = '#{date.future ''15'',''SECONDS''}',
  'fields.date.expression' = '#{date.birthday ''1'',''100''}'
);

-- Generate mock data from the faker table.
SELECT * FROM faker_time LIMIT 2;

Your output should resemble:

timestamp1               timestamp2               timestamp3               time      date
2025-10-15 14:47:54.938  2025-10-15 14:47:47.591  2025-10-15 14:48:16.789  14:48:07  2009-04-01
2025-10-15 14:47:51.761  2025-10-15 14:47:56.802  2025-10-15 14:48:11.148  14:48:06  1949-12-08

Faker table errors

Upsert without primary key error

The following code example generates an error, because an upsert table requires a PRIMARY KEY constraint.

CREATE TABLE faker_upsert_error (
  id BIGINT,
  name STRING,
  email STRING,
  city STRING
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'upsert'
);

Type-mismatch error

The following code example generates an error, because the expression for the INT field n is of type STRING.

CREATE TABLE faker_type_mismatch_error (
  n INT
) WITH (
  'connector' = 'faker',
  'changelog.mode' = 'append',
  'fields.n.expression' = '#{Internet.emailAddress}'
);

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