Update your project connection

To setup your Lightdash connection you’ll need to:

1. Connect to your data warehouse (bigquery, postgres, redshift, snowflake, databricks)

2. Connect to your dbt project

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Open up your Lightdash instance to get started.

To update an existing connection, head to:

1. Settings
2. Project management
3. Settings for your project

To create a new project connection, head to:

1. Settings
2. Project management
3. Create New

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1. Connect to a warehouse

We currently support:

We always recommend giving read-only permissions to Lightdash, that way you ensure than no data can be manipulated. See each section below for warehouse specific details.

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Bigquery

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Project

This is project ID from Google Cloud Platform for the data that you want to connect Lightdash to.

To find your project ID, head to the BigQuery Cloud console.

Once you’re in there, click on the project name in the top bar.

Then, you should see all of the projects and their project IDs in your organization (that you have access to).

For the project you want to connect Lightdash to, just copy its id and pop it into the project field in the Warehouse Connection form in Lightdash.

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Data set

This is the default dataset used by dbt to compile and run your dbt project. You can find this in the dbt cloud IDE or your local profiles.yml file.

If you’re a dbt cloud user you can find this under your profile in the dbt cloud IDE:

If you work with dbt locally, then check your profiles.yml file at ~/.dbt/profiles.yml and look for a field named dataset:

my-profile:
  target: dev
  outputs:
    dev:
      type: bigquery
      method: oauth
      project: abc-123
      dataset: my_dataset # look for this one!
      ...

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Location

The data location of the dataset in BigQuery where the output of your dbt models is written to.

location may be either a multi-regional location (e.g. EU, US), or a regional location (e.g. us-west2 ). Check out the BigQuery documentation for more information on dataset locations.

You can find the location of the dataset you’re using for your dbt project in your dbt profiles.yml file, or in your BigQuery console.

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Key File

To connect to BigQuery, you’ll need to have a service account that you can use with Lightdash. You can read more about creating and managing service accounts with Google BigQuery in their docs.

The service account you use with Lightdash will need to have the following roles in your GCP project:

• roles/bigquery.dataViewer (to see data in your project)

• roles/bigquery.jobUser (to run queries in your project)

If you need to provide access to data across multiple BigQuery projects, the service account will need to be granted roles/bigquery.dataViewer on each additional BigQuery project.

Once you have a service account all ready to go, you’ll need to add its JSON key file to Lightdash in the key file section of the Warehouse Connection page.

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Timeout in seconds

BigQuery supports query timeouts. By default, the timeout is set to 300 seconds. If a query run by Lightdash takes longer than this timeout to complete, then BigQuery may cancel the query and issue the following error:

Operation did not complete within the designated timeout.

To change this timeout, use the Timeout in seconds configuration.

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Priority

The priority for the BigQuery jobs that Lightdash executes can be configured with the priority configuration in your Warehouse Connection settings. The priority field can be set to one of batch or interactive.

For more information on query priority, check out the BigQuery documentation.

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Retries

The retries configuration specifies the number of times Lightdash should retry queries that result in unhandled server errors.

For example, setting retries to 5 means that Lightdash will retry BigQuery queries 5 times with a delay. If the query does not succeed after the fifth attempt, then Lightdash will raise an error.

By default, the number of retries is set to 3.

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Maximum bytes billed

If a value for the Maximum bytes billed is set, then queries executed by Lightdash will fail if they exceed the configured maximum bytes threshhold. This configuration should be supplied as an integer number of bytes.

For example, setting this to 1000000000 means if a query would bill more than a gigabyte of data (e.g. 2Gb), then BigQuery will reject the query and you’d get an error like this:

  Query exceeded limit for bytes billed: 1000000000. 2000000000 or higher required.

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Start of week

This controls what day is the start of the week in Lightdash. Auto sets it to whatever the default is for your data warehouse. Or, you can customize it and select the day of the week from the drop-down menu. This will be taken into account when using ‘WEEK’ time interval in Lightdash.

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Execution project

Here you can specify an execution project to bill for query execution, instead of using the project where your dbt resources are materialized. If you leave this blank, all costs get applied to the project from the top of the connection details.

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Postgres

You can see more details in dbt documentation.

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Host

This is the host where the database is running.

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User

This is the database user name.

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Password

This is the database user password.

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DB name

This is the database name.

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Schema

This is the default schema used by dbt to compile and run your dbt project. You can find this in the dbt cloud IDE or your local profiles.yml file.

If you’re a dbt cloud user you can find this under your profile in the dbt cloud IDE:

If you work with dbt locally, then check your profiles.yml file at ~/.dbt/profiles.yml and look for a field named schema:

company-name:
  target: dev
  outputs:
    dev:
      type: postgres
      host: [hostname]
      user: [username]
      password: [password]
      port: [port]
      dbname: [database name]
      schema: [dbt schema] # look for this one!

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Port

This is the port where the database is running.

​

Keep alive idle (seconds)

This specifies the amount of seconds with no network activity after which the operating system should send a TCP keepalive message to the client. You can see more details in postgresqlco documentation.

​

Search path

This controls the Postgres “search path”. You can see more details in dbt documentation.

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SSL mode

This controls how dbt connects to Postgres databases using SSL. You can see more details in dbt documentation.

​

SSL certificate

The client certificate used to authenticate your connection to the database. This is only required if you’re using SSL mode verify-full.

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SSL private key

The private key used to authenticate your connection to the database. This is only required if you’re using SSL mode verify-full.

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SSL root certificate

The trusted certificate authority (CA) certificate used to verify the database server’s identity. This is only required if you’re using SSL mode verify-ca or verify-full.

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Start of week

This controls what day is the start of the week in Lightdash. Auto sets it to whatever the default is for your data warehouse. Or, you can customize it and select the day of the week from the drop-down menu. This will be taken into account when using ‘WEEK’ time interval in Lightdash.

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Use SSH tunnel

Enable to input your SSH Remote Host, SSH Remote Port, SSH Username, and to generate a public SSH key.

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Redshift

You can see more details in dbt documentation.

​

Host

This is the host where the database is running.

​

User

This is the database user name.

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Password

This is the database user password.

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DB name

This is the database name.

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Schema

This is the default schema used by dbt to compile and run your dbt project. You can find this in the dbt cloud IDE or your local profiles.yml file.

If you’re a dbt cloud user you can find this under your profile in the dbt cloud IDE:

If you work with dbt locally, then check your profiles.yml file at ~/.dbt/profiles.yml and look for a field named schema:

company-name:
  target: dev
  outputs:
    dev:
      type: redshift
      host: hostname.region.redshift.amazonaws.com
      user: username
      password: password1
      port: 5439
      dbname: analytics
      schema: analytics # look for this one!

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Port

This is the port where the database is running.

​

Keep alive idle (seconds)

This specifies the amount of seconds with no network activity after which the operating system should send a TCP keepalive message to the client.

If the database closes its connection while Lightdash is waiting for data, you may see the error SSL SYSCALL error: EOF detected. Lowering the keepalives_idle value may prevent this, because the server will send a ping to keep the connection active more frequently.

By default, this value is set to 240 seconds, but can be configured lower (perhaps 120 or 60), at the cost of a chattier network connection.

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SSL mode

This controls how dbt connects to Postgres databases using SSL.

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RA3 Node

Allow dbt to use cross-database-resources

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Start of week

This controls what day is the start of the week in Lightdash. Auto sets it to whatever the default is for your data warehouse. Or, you can customize it and select the day of the week from the drop-down menu. This will be taken into account when using ‘WEEK’ time interval in Lightdash.

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Use SSH tunnel

Enable to input your SSH Remote Host, SSH Remote Port, SSH Username, and to generate a public SSH key.

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Snowflake

You can see more details in dbt documentation.

​

Account

This is your Snowflake account identifer.

The easiest way to find this is to login to snowflake in your browser and your account identifier is whatever comes before snowflakecomputing.com.

For example in the image below, the user logs in via https://aaa99827.snowflakecomputing.com/console/login#/ so the account identifier is aaa99827.

If you don’t have access via the browser, you can use the following format <organization_name>-<account_name> whereorganization_name and account_name can be found by following any of the methods listed inManaging accounts in your organization.

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User

This is the login name for your Snowflake user. This is usually the same username you use to login to Snowflake.

Alternatively, you can create a new user through the snowflake consolewith a username and password specifically for Lightdash to use.

If you’re a snowflake admin you can list all users available in the snowflake console:

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Authentication type

Choose to authenticate using either a Private Key (recommended) or a Password.

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Private Key

You can generate a Private Key for a Snowflake user following the guide here

Once generated, copy across the Private Key File that is generated into Lightdash. If you chose to encrypt your private key you will also need to supply the Private Key Passphrase.

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Password

This is the password your Snowflake user. This is usually the same password you use to login to Snowflake. Note that due to changes in Snowflake Authentication, users that require passwords may also need to enable MFA, which is not compatible with a Lightdash project connection.

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Role

This is the security role that you would like to use when running queries as the specified user. The role must have access to any warehouses, databases, schemas, and tables you want to use.

If you’re a Snowflake admin you can list all roles available in the snowflake console:

You can configure your role to allow read access to all warehouses, databases, schemas, and tables by following the guide forCreating custom read-only roles.

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Database

This is the name of your database. The specified user must be granted access to this database. You can see a list of databases available in the snowflake console:

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Warehouse

This is the name of the warehouse you would like to use for running queries. The specified user must be grantend access to use this warehouse. You can see al list of warehouses available in the snowflake console:

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Schema

This is the default schema used by dbt to compile and run your dbt project. You can find this in the dbt cloud IDE or your local profiles.yml file.

If you’re a dbt cloud user you can find this under your profile in the dbt cloud IDE:

If you work with dbt locally, then check your profiles.yml file at ~/.dbt/profiles.yml and look for a field named schema:

my-snowflake-db:
  target: dev
  outputs:
    dev:
      type: snowflake
      account: [account id]
      user: [username]
      password: [password]
      role: [user role]
      database: [database name]
      warehouse: [warehouse name]
      schema: [dbt schema] # Look for this one!

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Keep client session alive

This is intended to keep Snowflake sessions alive beyond the typical 4 hour timeout limit. You can see more details in dbt documentation.

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Query tag

A value with which to tag all queries, for later searching in QUERY_HISTORY view)

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Start of week

This controls what day is the start of the week in Lightdash. Auto sets it to whatever the default is for your data warehouse. Or, you can customize it and select the day of the week from the drop-down menu. This will be taken into account when using ‘WEEK’ time interval in Lightdash.

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Databricks

The credentials needed to connect to your cluster can be found in the ODBC options in your databricks account:

1. Go to the Compute tab in the sidebar.

2. Click the configuration tab for the cluster that you’re connecting to Lightdash.

3. Expand the Advanced options tab

4. Open the JDBC/ODBC tab

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Server hostname

Follow the instructions above to find your ODBC connection instructions.

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HTTP Path

Follow the instructions above to find your ODBC connection instructions.

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Port

Follow the instructions above to find your ODBC connection instructions.

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Personal Access Token

Your personal access token can be found in your user settings in databricks:

1. Open Settings by clicking the cog ⚙️ in the sidebar and select User settings

2. Click Generate token. You’ll be asked to enter a name and expiry.

3. Copy the token

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Database

The default database name used by dbt for this connection.

​

Start of week

This controls what day is the start of the week in Lightdash. Auto sets it to whatever the default is for your data warehouse. Or, you can customize it and select the day of the week from the drop-down menu. This will be taken into account when using ‘WEEK’ time interval in Lightdash.

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Trino

We only support LDAP authentication with Trino. You can see more details in dbt’s documentation.

​

Host

The hostname of your cluster. E.g. mycluster.mydomain.com

Don’t include the http:// or https:// prefix.

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User

The username (of the account) to log in to your cluster. When connecting to Starburst Galaxy clusters, you must include the role of the user as a suffix to the username.

Format for Starburst Enterprise or Trino: user.name or user.name@mydomain.com

Format for Starburst Galaxy:user.name@mydomain.com/role

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Password

This is the password for authentication.

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DB name

Specify the name of the database that your dbt models are built into. This is the name of a catalog in your cluster.

e.g. my_postgres_catalog

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Port

The port to connect to your cluster. By default, it’s 443 for TLS enabled clusters.

e.g. 443

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SSL mode

This controls how dbt connects to Trino database using SSL.

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Start of week

This controls what day is the start of the week in Lightdash. Auto sets it to whatever the default is for your data warehouse. Or, you can customize it and select the day of the week from the drop-down menu. This will be taken into account when using ‘WEEK’ time interval in Lightdash.

​

2. Import a dbt project

Connecting Lightdash to a hosted dbt project means that you’ll be able to keep your Lightdash instance in sync with the changes in your dbt project.

To connect your dbt project, just head to your project connection settings in Lightdash:

Then scroll down to your dbt project connection:

Pick your repository type and follow the guide below:

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GitHub

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Personal access token

You can create a personal access token (classic), or a fine-grained access token (beta).

Personal access token (classic)

This is used to access your repo. See the instructions for creating a personal access token here.

Select repo scope when you’re creating the token.

Fine-grained access token (beta)

Fine-grained access tokens are new special tokens that can only give access to individual repositories on your github account. You can read more about it on the Github docs.

1. Go to settings > Developer access > Personal access tokens > fine-grained token

2. Add the name, expiration, description and owner (we’ll need their username later)

3. Add the repositories you want Lightdash to access. You might want to give access only to the repository where your dbt project is located.

4. On Repository permissions, select Contents —> Read and Write and Pull Requests —> Read and Write.

5. Click Generate token and copy the token.

You could also replace your old tokens with new fine-grained tokens on project settings.

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Repository

This should be in the format my-org/my-repo. e.g. lightdash/lightdash-analytics

​

Branch

This is the branch in your GitHub repo that Lightdash should sync to. e.g. main, master or dev

By default, we’ve set this to main but you can change it to whatever you’d like.

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Project directory path

This is the folder where your dbt_project.yml file is found in the GitHub repository you entered above.

• Put / if your dbt_project.yml file is in the main folder of your repo (e.g. lightdash/lightdash-analytics/dbt_project.yml)

• Include the path to the sub-folder where your dbt project is if your dbt project is in a sub-folder in your repo. For example, if my project was in lightdash/lightdash-analytics/dbt/dbt_project.yml, I’d write /dbt in this field.

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Host domain

If you’ve customized the domain for your GitHub pages, you can add the custom domain for your project in here.

By default, this is github.com

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Target name

target contains information about your dbt connection to your warehouse.

It’s the dataset/schema in your data warehouse that Lightdash will look for your dbt models. By default, we set this to be the same value as you have as the default in your profiles.yml file.

If you want to change this to be something other than the default target defined in dbt, you can enter the target of your choice here (for example dbt_khindson.)

To read more about dbt targets, check out the dbt docs here.

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dbt selector

You can filter out models in your dbt project that you don’t want to see in Lightdash. This is useful if you have a large dbt project and you want to speed up the sync process. Unlike table selection, this selector is applied to the dbt models, so it will skip the entire compilation process for the models that you don’t want to see in Lightdash.

To do this, you can add a dbt_selector to your dbt project. This is a JSON object that contains the models you want to include in Lightdash.

For example, if you only want to include the my_model and all models with the lightdash tag in Lightdash, you can add the following to your dbt project settings:

my_model tag:lightdash

We support all the dbt selectors, you can read more about them on the dbt docs.

​

Environment variables

If you’ve used environment variables in your dbt profiles.yml file, you can add these to Lightdash here.

For each environment variable, you’ll need to add the key + value pair for the item.

You’ll normally find these values in a file called .env in your dbt project directory.

For example, I might have something like:

profile:
  target: prod
  outputs:
    prod:
      type: postgres
      host: 127.0.0.1
      user: "{{ env_var('DBT_USER') }}"
      ....

Then a .env file like:

export DBT_USER="myspecialuserkey123"

So, in Lightdash, I’d add a new environment variable and put key as DBT_USER and value as myspecialuserkey123.

​

GitLab

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Personal access token

This is used to access your repo. See the instructions for creating a personal access token here.

Select read_repository scope when you’re creating the token. The token, if using a project access token, or the user, when using a personal access token, needs to have permission to download the code. Normally this would be the Reporter role.

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Repository

You can find this in the GitLab URL when you’re in your repo. This should be in the format my-org/my-repo. e.g. if my browser had https://gitlab.com/lightdash/lightdash-analytics.gitlab.io, I’d put in: lightdash/lightdash-analytics as my repository in Lightdash.

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Branch

This is the branch in your GitLab repo that Lightdash should sync to. e.g. main, master or dev

By default, we’ve set this to main but you can change it to whatever you’d like.

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Project directory path

This is the folder where your dbt_project.yml file is found in the GitLab repository you entered above.

If your dbt_project.yml file is in the main folder of your repo (e.g. lightdash/lightdash-analytics/dbt_project.yml), then you don’t need to change anything in here. You can just leave the default value we’ve put in.

If your dbt project is in a sub-folder in your repo (e.g. lightdash/lightdash-analytics/dbt/dbt_project.yml), then you’ll need to include the path to the sub-folder where your dbt project is (e.g. /dbt).

​

Host domain

If you’ve customized the domain for your GitLab pages, you can add the custom domain for your project in here.

By default, this is gitlab.io.

​

Target name

target contains information about your dbt connection to your warehouse.

It’s the dataset/schema in your data warehouse that Lightdash will look for your dbt models. By default, we set this to be the same value as you have as the default in your profiles.yml file.

If you want to change this to be something other than the default target defined in dbt, you can enter the target of your choice here (for example dbt_khindson.)

To read more about dbt targets, check out the dbt docs here.

​

dbt selector

You can filter out models in your dbt project that you don’t want to connect to Lightdash. This is useful if you have a large dbt project and you want to speed up the sync process. Unlike table selection, this selector is applied to the dbt models, so it will skip the entire compilation process for the models that you don’t want to connect to Lightdash.

To do this, you can add a dbt_selector to your dbt project. This is a JSON object that contains the models you want to include in Lightdash.

For example, if you only want to include the my_model and all models with the lightdash tag in Lightdash, you can add the following to your dbt project settings:

my_model tag:lightdash

We support all the dbt selectors, you can read more about them on the dbt docs.

​

Environment variables

If you’ve used environment variables in your dbt profiles.yml file, you can add these to Lightdash here.

For each environment variable, you’ll need to add the key + value pair for the item.

You’ll normally find these values in a file called .env in your dbt project directory.

For example, I might have something like:

profile:
  target: prod
  outputs:
    prod:
      type: postgres
      host: 127.0.0.1
      user: "{{ env_var('DBT_USER') }}"
      ....

Then a .env file like:

export DBT_USER="myspecialuserkey123"

So, in Lightdash, I’d add a new environment variable and put key as DBT_USER and value as myspecialuserkey123.

​

Azure DevOps

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Personal access token

This is your secret token used to access Azure Devops. See the instructions to create a personal access tokenYou must specify at least the Repo:Read scope.

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Organization

This is the name of the organization that owns your repository

​

Project

This is the name of the project that owns your repository

​

Repository

This is the name of the repository. For many projects, this is the same as your project name above.

​

Branch

This is the branch in your repository that Lightdash should sync to. e.g. main, master or dev

By default, we’ve set this to main but you can change it to whatever you’d like.

​

Project directory path

This is the folder where your dbt_project.yml file is found in the repository you entered above.

If your dbt_project.yml file is in the main folder of your repo (e.g. lightdash/lightdash-analytics/dbt_project.yml), then you don’t need to change anything in here. You can just leave the default value we’ve put in.

If your dbt project is in a sub-folder in your repo (e.g. lightdash/lightdash-analytics/dbt/dbt_project.yml), then you’ll need to include the path to the sub-folder where your dbt project is (e.g. /dbt).

​

Target name

target contains information about your dbt connection to your warehouse.

It’s the dataset/schema in your data warehouse that Lightdash will look for your dbt models. By default, we set this to be the same value as you have as the default in your profiles.yml file.

If you want to change this to be something other than the default target defined in dbt, you can enter the target of your choice here (for example dbt_khindson.)

To read more about dbt targets, check out the dbt docs here.

​

dbt selector

You can filter out models in your dbt project that you don’t want to see in Lightdash. This is useful if you have a large dbt project and you want to speed up the sync process. Unlike table selection, this selector is applied to the dbt models, so it will skip the entire compilation process for the models that you don’t want to see in Lightdash.

To do this, you can add a dbt_selector to your dbt project. This is a JSON object that contains the models you want to include in Lightdash.

For example, if you only want to include the my_model and all models with the lightdash tag in Lightdash, you can add the following to your dbt project settings:

my_model tag:lightdash

We support all the dbt selectors, you can read more about them on the dbt docs.

​

Environment variables

If you’ve used environment variables in your dbt profiles.yml file, you can add these to Lightdash here.

For each environment variable, you’ll need to add the key + value pair for the item.

You’ll normally find these values in a file called .env in your dbt project directory.

For example, I might have something like:

profile:
  target: prod
  outputs:
    prod:
      type: postgres
      host: 127.0.0.1
      user: "{{ env_var('DBT_USER') }}"
      ....

Then a .env file like:

export DBT_USER="myspecialuserkey123"

So, in Lightdash, I’d add a new environment variable and put key as DBT_USER and value as myspecialuserkey123.

​

Local dbt project

To start Lightdash with the option to connect to a local dbt project, you must specify the directory of the dbt project when you start docker compose:

# Specify the absolute path to your dbt project
# e.g. export DBT_PROJECT_DIR=/Users/elonmusk/mydbtproject
export DBT_PROJECT_DIR= # Enter your path here!
docker compose start

​

Bitbucket

​

Username

This is the login name for your Bitbucket user. This is usually the same username you use to login to Bitbucket. You can find your username in Bitbucket by:

1. Going to your avatar in the bottom left and click Personal settings.

2. From the Account settings page, check the value next to the Username

Alternatively, you can create a new user through the Bitbucket consolewith a username and password specifically for Lightdash to use.

​

Http access token

Getting a token depends on whether you use Bitbucket Cloud or Bitbucket server:

• Bitbucket Cloud users should follow instructions for creating an App Password

• Bitbucket Server users should follow instructions for creating a HTTP Access Token

Select Project read and Repository read scope when you’re creating the token.

​

Repository

This should be in the format my-org/my-repo. e.g. lightdash/lightdash-analytics

​

Branch

This is the branch in your Bitbucket repo that Lightdash should sync to. e.g. main, master or dev

By default, we’ve set this to main but you can change it to whatever you’d like.

​

Project directory path

This is the folder where your dbt_project.yml file is found in the Bitbucket repository you entered above.

• Put / if your dbt_project.yml file is in the main folder of your repo (e.g. lightdash/lightdash-analytics/dbt_project.yml)

• Include the path to the sub-folder where your dbt project is if your dbt project is in a sub-folder in your repo. For example, if my project was in lightdash/lightdash-analytics/dbt/dbt_project.yml, I’d write /dbt in this field.

​

Host domain

If you’ve customized the domain for your Bitbucket server, you can add the custom domain for your project in here.

​

Target name

target contains information about your dbt connection to your warehouse.

It’s the dataset/schema in your data warehouse that Lightdash will look for your dbt models. By default, we set this to be the same value as you have as the default in your profiles.yml file.

If you want to change this to be something other than the default target defined in dbt, you can enter the target of your choice here (for example dbt_khindson.)

To read more about dbt targets, check out the dbt docs here.

​

dbt selector

You can filter out models in your dbt project that you don’t want to see in Lightdash. This is useful if you have a large dbt project and you want to speed up the sync process. Unlike table selection, this selector is applied to the dbt models, so it will skip the entire compilation process for the models that you don’t want to see in Lightdash.

To do this, you can add a dbt_selector to your dbt project. This is a JSON object that contains the models you want to include in Lightdash.

For example, if you only want to include the my_model and all models with the lightdash tag in Lightdash, you can add the following to your dbt project settings:

my_model tag:lightdash

We support all the dbt selectors, you can read more about them on the dbt docs.

​

Environment variables

If you’ve used environment variables in your dbt profiles.yml file, you can add these to Lightdash here.

For each environment variable, you’ll need to add the key + value pair for the item.

You’ll normally find these values in a file called .env in your dbt project directory.

For example, I might have something like:

profile:
  target: prod
  outputs:
    prod:
      type: postgres
      host: 127.0.0.1
      user: "{{ env_var('DBT_USER') }}"
      ....

Then a .env file like:

export DBT_USER="myspecialuserkey123"

So, in Lightdash, I’d add a new environment variable and put key as DBT_USER and value as myspecialuserkey123.