# CREATE INFERRED PIPELINE

Infers the schema from the input files and creates a table and pipeline based on the inferred DDL. Use this command to create the table and pipeline.

## Syntax

```sql
CREATE INFERRED PIPELINE <pipeline_name> AS
    LOAD DATA {input_configuration }
    [FORMAT [CSV | JSON | AVRO | PARQUET | ICEBERG]]
    [AS JSON];

```

## Remarks

* The `input_configuration` specifies configuration for loading files from Apache Kafka, Amazon S3, a local filesystem, Microsoft Azure, HDFS, and Google Cloud Storage. Refer to `CREATE PIPELINE` for more information on configuration specifications.
* All options supported by `CREATE PIPELINE` are supported by `CREATE INFERRED PIPELINE`.
* CSV, JSON, Avro, Parquet, and Iceberg formats are supported.&#x20;
* The default format is CSV.
* `TEXT` and `ENUM` types use `utf8mb4` charset and `utf8mb4_bin` collation by default.
* The `AS JSON` keyword is used to produce pipeline and table definitions in JSON format.
* Refer to the [Permissions Matrix](https://docs.singlestore.com/cloud/reference/sql-reference/security-management-commands/permissions-matrix.md) for the required permissions.

## Example

The following example demonstrates how to use the `CREATE INFERRED PIPELINE` command to infer the schema of a Avro-formatted file in an AWS S3 bucket.

This example uses data that conforms to the schema of the `books` table, as shown in the following.

```
{"namespace": "books.avro",
"type": "record",
"name": "Book",
"fields": [
{"name": "id", "type": "int"},
{"name": "name", "type": "string"},
{"name": "num_pages", "type": "int"},
{"name": "rating", "type": "double"},
{"name": "publish_timestamp", "type": "long",
"logicalType": "timestamp-micros"} ]}

```

Refer to [Generate an Avro File](https://docs.singlestore.com/cloud/load-data/about-singlestore-pipelines/pipeline-concepts/schema-and-pipeline-inference/#section-idm4572725773489634329890353354.md) for an example of generating an Avro file that conforms to this schema.

The following example creates a pipeline named `books_pipe` by inferring the schema from the specified file. This command also creates a table with the same name as the pipeline. The pipeline is automatically started to allow review and adjustment of the pipeline and table definitions as required.

```sql
CREATE INFERRED PIPELINE books_pipe AS LOAD DATA S3
         's3://data_folder/books.avro'
CONFIG '{"region":"<region_name>"}'
CREDENTIALS '{
    "aws_access_key_id":"<your_access_key_id>",
    "aws_secret_access_key":"<your_secret_access_key>",
    "aws_session_token":"<your_session_token>"}'
FORMAT AVRO;

```

```output

Created 'books_pipe' table and 'books_pipe' pipeline

```

Run the `SHOW CREATE PIPELINE` command to view the `CREATE PIPELINE` statement for the pipeline created by the `CREATE INFERRED PIPELINE` command.

```sql
SHOW CREATE PIPELINE books_pipe;

```

```output

Pipeline,Create Pipeline
books_pipe,"CREATE PIPELINE `books_pipe`
AS LOAD DATA S3 's3://data-folder/books.avro'
CONFIG '{\""region\"":\""us-west-2\""}'
CREDENTIALS <CREDENTIALS REDACTED>
BATCH_INTERVAL 2500
DISABLE OUT_OF_ORDER OPTIMIZATION
DISABLE OFFSETS METADATA GC
INTO TABLE `books_pipe`
FORMAT AVRO(
    `books_pipe`.`id` <- `id`,
    `books_pipe`.`name` <- `name`,
    `books_pipe`.`num_pages` <- `num_pages`,
    `books_pipe`.`rating` <- `rating`,
    `books_pipe`.`publish_date` <- `publish_date`)"

```

Run the `SHOW CREATE TABLE` command to view the `CREATE TABLE` statement for the table created by the C`REATE INFERRED PIPELINE` command.

```sql
SHOW CREATE TABLE books_pipe;

```

```output

Table,Create Table
books_pipe,"CREATE TABLE `books_pipe` (
  `id` int(11) NOT NULL,
  `name` longtext CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
  `num_pages` int(11) NOT NULL,
  `rating` double DEFAULT NULL,
  `publish_date` bigint(20) NOT NULL,
   SORT KEY `__UNORDERED` (),
   SHARD KEY ()
) AUTOSTATS_CARDINALITY_MODE=INCREMENTAL
AUTOSTATS_HISTOGRAM_MODE=CREATE
AUTOSTATS_SAMPLING=ON
SQL_MODE='STRICT_ALL_TABLES,NO_AUTO_CREATE_USER'"

```

The pipeline and table definitions can be adjusted using `CREATE OR REPLACE PIPELINE` (`CREATE PIPELINE`) and `ALTER TABLE` commands, respectively.

Once the pipeline and table definitions are configured, start the pipeline.

```sql
START PIPELINE books_pipe FOREGROUND;
```

This command starts a pipeline in the foreground and displays any errors in the client. For pipelines that run continuously, start them in the background by omitting the `FOREGROUND` keyword. Refer to `START PIPELINE` for more information.

Check if the data is loaded.

```sql
SELECT * FROM books_pipe
ORDER BY id;

```

```output

+----+--------------------+-----------+--------+------------------+
| id | name               | num_pages | rating | publish_date     |
+----+--------------------+-----------+--------+------------------+
|  1 | HappyPlace         |       400 |    4.9 | 1680721200000000 |
|  2 | Legends & Lattes   |       304 |    4.9 | 1669665600000000 |
|  3 | The Vanishing Half |       352 |    4.9 | 1591124400000000 |
+----+--------------------+-----------+--------+------------------+

```

Refer to [Schema and Pipeline Inference - Examples](https://docs.singlestore.com/cloud/load-data/about-singlestore-pipelines/pipeline-concepts/schema-and-pipeline-inference/#section-idm4656158159921634329807205399.md) for more examples.

***

Modified at: February 5, 2026

Source: [/cloud/reference/sql-reference/pipelines-commands/create-inferred-pipeline/](https://docs.singlestore.com/cloud/reference/sql-reference/pipelines-commands/create-inferred-pipeline/)

(An index of the documentation is available at /llms.txt)