Installing and Configuring the Spark Connector

Multiple versions of the connector are supported; however, Snowflake strongly recommends using the most recent version of the connector. To view release information about the latest version, see the Spark Connector Release Notes (link in the sidebar).

The instructions in this topic can be used to install and configure all supported versions of the connector.

In this Topic:

Supported Versions

Snowflake supports multiple versions of the connector:

Connector versions:


Supported Spark versions:

Spark 3.0, 2.4, 2.3

Supported Scala versions:

Scala 2.12, 2.11

Data source name:

net.snowflake.spark.snowflake — v2.4.14 (or higher) of the connector allow snowflake as the data source name

Package name (for imported classes):


Package distribution:

Maven Central Repository or Spark Packages

Source code:

spark-snowflake (GitHub): . master (for latest version) , . previous_spark_version (for earlier versions)

The developer notes for the different versions are hosted with the source code.


The Snowflake Spark Connector generally supports the three most recent versions of Spark. Download a version of the connector that is specific to your Spark version.

For example, to use version 2.7.2 of the connector with the older Spark version 2.3, download the 2.7.2-spark_2.3 version of the connector.


To install and use Snowflake with Spark, you need the following:

  • A supported operating system. For a list of supported operating systems, see Operating System Support.

  • Snowflake Connector for Spark.

  • Snowflake JDBC Driver (the version compatible with the version of the connector).

  • Apache Spark environment, either self-hosted or hosted in any of the following:

  • In addition, you can use a dedicated Amazon S3 bucket or Azure Blob storage container as a staging zone between the two systems; however, this is not required with version 2.2.0 (and higher) of the connector, which uses a temporary Snowflake internal stage (by default) for all data exchange.

  • The role used in the connection needs USAGE and CREATE STAGE privileges on the schema that contains the table that you will read from or write to.


If you are using Databricks or Qubole to host Spark, you do not need to download or install the Snowflake Connector for Spark (or any of the other requirements). Both Databricks and Qubole have integrated the connector to provide native connectivity.

For more details, see:

Verifying the OCSP Connector or Driver Version

Snowflake uses OCSP to evaluate the certificate chain when making a connection to Snowflake. The driver or connector version and its configuration both determine the OCSP behavior. For more information about the driver or connector version, their configuration, and OCSP behavior, see OCSP Configuration.

Downloading and Installing the Connector

The instructions in this section pertain to version 2.x and higher of the Snowflake Connector for Spark.


Snowflake periodically releases new versions of the connector. The following installation tasks must be performed each time you install a new version. This also applies to the Snowflake JDBC driver, which is a prerequisite for the Spark connector.

Step 1: Download the Latest Version of the Snowflake Connector for Spark

Snowflake provides multiple versions of the connector. You will need to download the appropriate version, based on the following:

  • Version of the Snowflake Connector for Spark you wish to use.

  • Version of Spark you are using.

  • Version of Scala you are using.

The Snowflake Spark Connector can be downloaded from either Maven or the Spark Packages web site. The source code can be downloaded from Github.

Maven Central Repository

Separate package artifacts are provided for each supported Scala version (2.11 and 2.12). To download the latest version of the connector from Maven:

The following screenshot provides an example of the download page for the Spark connector on the Maven web site:

Snowflake Connector for Spark download page in Maven

The individual packages use the following naming convention:



  • N.N.N is the Snowflake version (e.g. 2.4.14).

  • P.P is the Spark version (e.g. 2.4).

For example:


If you want to validate the gpg key of the file, then also download the associated key file, named spark.jar.asc.

Spark Packages

To download the latest version of the connector from the Spark Packages web site, click this link.

Snowflake uses the following naming conventions for the packages:



  • N.N.N is the Snowflake version (e.g. 2.4.14).

  • C.C is the Scala version (e.g. 2.11).

  • P.P is the earlier Spark version (e.g. 2.3).

For example:



The source code for the Spark Snowflake Connector is available on GitHub. However, the compiled packages are not available on GitHub. You can download the compiled packages from Maven or the Spark Packages web site as described in the previous sections (in this topic).

Step 2: Download the Compatible Version of the Snowflake JDBC Driver

Next, you need to download the version of the Snowflake JDBC driver that is compatible with the version of the Snowflake Spark Connnector that you are using.

The Snowflake JDBC driver is provided as a standard Java package through the Maven Central Repository. You can either download the package as a .jar file or you can directly reference the package. These instructions assume you are referencing the package.

To find the supported version of the Snowflake JDBC Driver for the version of the Snowflake Spark Connector that you are using, see the Snowflake For Spark Connector Release Notes.

For more details on downloading and installing the Snowflake JDBC Driver, see Downloading / Integrating the JDBC Driver.

Step 3 (Optional): Verify the Snowflake Connector for Spark Package Signature (Linux Only)


The macOS and Windows operating systems can verify the installer signature automatically, so GPG signature verification is not needed.

To optionally verify the Snowflake Connector for Spark package signature for Linux:

  1. Download and import the latest Snowflake GPG public key from the public keyserver:

    $ gpg --keyserver hkp:// --recv-keys <GPG_KEY_ID>

    For <GPG_KEY_ID>, specify one of the following key IDs:

    Snowflake Spark Connector Version

    GPG Key ID

    2.8.2 and higher


    2.4.13 through 2.8.1


    Up to 2.4.12


  2. If you have not already downloaded the key file, named spark.jar.asc, from Maven, then download it.

  3. Download the GPG signature along with the bash installer and verify the signature:

    $ gpg --verify spark.jar.asc spark-snowflake_2.12-2.8.0-spark_3.0.jar
    gpg: Signature made Wed 22 Feb 2017 04:31:58 PM UTC using RSA key ID <gpg_key_id>
    gpg: Good signature from "Snowflake Computing <snowflake_gpg\>"
  4. Your local environment can contain multiple GPG keys; however, for security reasons, Snowflake periodically rotates the public GPG key. As a best practice, we recommend deleting the existing public key after confirming that the latest key works with the latest signed package. For example:

    $ gpg --delete-key "Snowflake Computing"

Step 4: Configure the Local Spark Cluster or Amazon EMR-hosted Spark Environment

If you have a local Spark installation, or a Spark installation in Amazon EMR, you need to configure the spark-shell program to include both the Snowflake JDBC driver and the Spark Connector:

  • To include the Snowflake JDBC driver, use the --package option to reference the JDBC package hosted in the Maven Central Repository, providing the exact version of the driver you wish to use (e.g. net.snowflake:snowflake-jdbc:3.0.12).

  • To include the Spark Connector, use the --package option to reference the appropriate package ( Scala 2.11 or Scala 2.12 ) hosted in the Maven Central Repository, providing the exact version of the driver you want to use (e.g. net.snowflake:spark-snowflake_2.12:2.4.14).

For example:

spark-shell --packages net.snowflake:snowflake-jdbc:3.8.0,net.snowflake:spark-snowflake_2.12:2.4.14

Installing Additional Packages (If Needed)

Depending on your Spark installation, some packages required by the connector may be missing. You can add missing packages to your installation by using the appropriate flag for spark-shell:

  • --packages

  • --jars (if the packages were downloaded as .jar files)

The required packages are listed below, with the syntax (including version number) for using the --packages flag to reference the packages:

  • org.apache.hadoop:hadoop-aws:2.7.1

  • org.apache.httpcomponents:httpclient:4.3.6

  • org.apache.httpcomponents:httpcore:4.3.3

  • com.amazonaws:aws-java-sdk-core:1.10.27

  • com.amazonaws:aws-java-sdk-s3:1.10.27

  • com.amazonaws:aws-java-sdk-sts:1.10.27

For example, if the Apache packages are missing, to add the packages by reference:

spark-shell --packages org.apache.hadoop:hadoop-aws:2.7.1,org.apache.httpcomponents:httpclient:4.3.6,org.apache.httpcomponents:httpcore:4.3.3

Preparing an External Location For Files

You might need to prepare an external location for files that you want to transfer between Snowflake and Spark.

This task is required if either of the following situations is true:

  • You will run jobs that take longer than 36 hours, which is the maximum duration for the token used by the connector to access the internal stage for data exchange.

  • The Snowflake Connector for Spark version is 2.1.x or lower (even if your jobs require less than 36 hours).


    If you are not currently using v2.2.0 (or higher) of the connector, Snowflake strongly recommends upgrading to the latest version.

Preparing an AWS External S3 Bucket

Prepare an external S3 bucket that the connector can use to exchange data between Snowflake and Spark. You then provide the location information, together with the necessary AWS credentials for the location, to the connector. For more details, see Authenticating S3 for Data Exchange in the next topic.


If you use an external S3 bucket, the connector does not automatically remove any intermediate/temporary data from this location. As a result, it’s best to use a specific bucket or path (prefix) and set a lifecycle policy on the bucket/path to clean up older files automatically. For more details on configuring a lifecycle policy, see the Amazon S3 documentation.

Preparing an Azure Blob Storage Container

Prepare an external Azure Blob storage container that the connector can use to exchange data between Snowflake and Spark. You then provide the location information, together with the necessary Azure credentials for the location, to the connector. For more details, see Authenticating Azure for Data Exchange in the next topic.