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Data Warehouse Integration


SQL Ingest is an experimental feature.

Part of your data might live in databases requiring some level of integration with your warehouse to retrieve and inject them into Pachyderm.

Our SQL ingest tool provides a seamless connection between databases and Pachyderm, allowing you to import data from a SQL database into Pachyderm-powered pipelines. By bringing data-driven pipelines, versioning & lineage to structured data, we are allowing Data Science teams to easily combine structured and unstructured data.

Specifically, we help you connect to a remote database of your choice and pull the result of a given query at regular intervals in the form of a CSV or a JSON file.

Use SQL Ingest

Pachyderm's SQL Ingest uses jsonnet pipeline specs with the following parameters to automatically create the pipelines that access, query, and materialize the results of a SQL query to a data warehouse. The outputted results can take the form of CSV or JSON files. Check the Formatting section at the bottom of the page for specific details on formats and SQL Datatypes.

Pass in the following parameters and get your results committed to an output repo, ready for the following downstream pipeline:

pachctl update pipeline --jsonnet \
  --arg name=myingest \
  --arg url="mysql://root@mysql:3306/test_db" \
  --arg query="SELECT * FROM test_data" \
  --arg cronSpec="@every 30s" \
  --arg secretName="mysql-creds" \
  --arg format=json 

Where the parameters passed to the jsonnet pipeline spec are:

Parameter Description
name The name of output repo in which your query results will materialize.
url The connection string to the database.
query The SQL query that will be run against your database.
cronSpec How often to run the query. For example "@every 60s".
format The type of your output file containing the results of your query (either json or yaml).
secretName The kubernetes secret name that contains the password to the database.


In this example, we are leveraging Snowflake's support for queries traversing semi-structured data (here, JSON).

  • Find the documentation for the support of semi-structured data in Snowflake here.

  • The query in the following example will use the WEATHER schema in the public test database SNOWFLAKE_SAMPLE_DATA in the COMPUTE_WH warehouse. The column V of the table DAILY_14_TOTAL stores JSON files.

    Note the references to the JSON dataset elements by their hierarchical paths in the query:

    pachctl update pipeline --jsonnet  \
    --arg name=mysnowflakeingest \
    --arg url="snowflake://username@VCNYTW-MH64356/SNOWFLAKE_SAMPLE_DATA/WEATHER?warehouse=COMPUTE_WH" \
    --arg query="select T,, V:data[0].weather[0].description as morning, V:data[12].weather[0].description as pm FROM DAILY_14_TOTAL LIMIT 1" \
    --arg cronSpec="@every 30s" \
    --arg secretName="snowflakesecret" \
    --arg format=json


pachctl update pipeline will create pipelines if none exist, or update your existing pipelines otherwise.

When the command is run, the database will be queried on a schedule defined in your cronSpec parameter and a result file committed to the output repo named after name.

Database Secret

Before you create your SQL Ingest pipelines, make sure to create a generic secret containing your database password in the field PACHYDERM_SQL_PASSWORD.


apiVersion: v1
kind: Secret
  name: mysql-creds
  "PACHYDERM_SQL_PASSWORD": "cm9vdA==" # base64 encoded


  • Run the following command to generate your secret:

    kubectl create secret generic <secret-name> --from-literal=PACHYDERM_SQL_PASSWORD=<password-to-warehouse> --dry-run=client --output=json > yourwarehousesecret.json

  • Then apply it to your Pachyderm cluster:

    pachctl create secret -f yourwarehousesecret.json

  • The list returned by kubectl get secret should feature the secret name.

Database Connection URL

Pachyderm's SQL Ingest will take an URL as its connection string to the database of your choice.

The URL is structured as follows:



Parameter Description
protocol The name of the database protocol.
As of today, we support:
- postgres and postgresql : connect to Postgresql or compatible (for example Redshift).
- mysql : connect to MySQL or compatible (for example MariaDB).
- snowflake : connect to Snowflake.
username The user used to access the database.
host The hostname of your database instance.
port The port number your instance is listening on.
database The name of the database to connect to.

Snowflake users, you will need a variant of the URL above.

Pachyderm supports two connection URL patterns to query Snowflake:

  • snowflake://username@<account_identifier>/<db_name>/<schema_name>?warehouse=<warehouse_name>
  • snowflake://username@hostname:port/<db_name>/<schema_name>?account=<account_identifier>&warehouse=<warehouse_name>


  • The account_identifier takes one of the following forms for most URLs:

    In both cases, if you are used to connecting to Snowflake via an URL such as, you can use the full domain name in the url.

  • And db_name/schema_name are respectively the Database Name and the Schema (namespace) targeted.

  • Additionally, a warehouse, or “compute resource” is required for all queries. Pass your warehouse as a parameter to the url: warehouse=<warehouse_name>

Here is an example of connection string to Snowflake:



  • The password is not included in the URL. It is retrieved from a kubernetes secret or file on disk at the time of the query.
  • The additional parameters (<param1>=<value1>) are optional and specific to the driver. For example, Snowflake requires to pass the warehouse as a parameter warehouse=<your-warehouse>.

How Does This Work?

SQL Ingest's jsonnet pipeline specs sql_ingest_cron.jsonnet creates two pipelines:

  • A Cron Pipeline myingest_queries triggering at an interval set by cronSpec and outputting a file /0000 in its output repo myingest_queries. /0000 contains a timestamp and the SQL statement set in query.
  • The following pipeline myingest takes the /0000 file as input and runs the query against the database set in url. The query's result is then materialized in a file (JSON or CSV) of the same name /0000 committed to the output repo myingest.


The name of each pipeline and related input and output repos are derived from the name parameter. In the example above, we have set --arg name=myingest.

The same base image pachctf is used in both pipelines.

Check the visual representation of the SQL Ingest DAG created above in Console:

SQL Ingest DAG

In your terminal:

  • The list of the DAG's pipelines (pachctl list pipeline) looks like this:

    List pipeline

  • 3 repos are created:

    List repo

How To Inspect The Result Of A Query?

You have run a query using SQL Ingest. How do you inspect its result?

  • Check what the query looked like:

    pachctl get file myingest_queries@master:/0000
    -- 1643235475
    SELECT * FROM test_data

  • Read the file written to the output repo myingest:

    pachctl list file myingest@master
    /0000 file 52B

    pachctl get file myingest@master:/0000
    {"mycolumn":"hello world","id":1}
    {"mycolumn":"hello you","id":2}

Formats and SQL DataTypes

The following comments on formatting reflect the state of this release and are subject to change.

SQL datatypes supported

We support the following SQL datatypes. Some of those Data Types are specific to a database.

Dates/Timestamps Varchars Numerics Booleans


  • All numeric values are converted into strings in your CSV and JSON.


    • Note that infinite (Inf) and not a number (NaN) values will also be stored as strings in JSON files.
    • Use this format #.# for all decimals that you plan to egress back to a database.


    Database CSV JSON
    12345 12345 "12345"
    123.45 123.45 "123.45"
  • Date/Timestamps


    Type Database CSV JSON
    Date 2022-05-09 2022-05-09T00:00:00 "2022-05-09T00:00:00"
    Timestamp ntz 2022-05-09 16:43:00 2022-05-09T16:43:00 "2022-05-09T16:43:00"
    Timestamp tz 2022-05-09 16:43:00-05:00 2022-05-09T16:43:00-05:00 "2022-05-09T16:43:00-05:00"
  • Strings

    Keep in mind when parsing your CSVs in your user code that we escape " with "" in CSV files.


    Database CSV
    "null" null
    `""` """"""
    "" ""
    "my string" """my string"""
    "this will be enclosed in quotes because it has a ," "this will be enclosed in quotes because it has a ,"

Last update: May 16, 2022
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