
# Introducing SQLite file uploads

Splitgraph already had excellent support for [importing CSV files](csv-to-sql-upload-to-data-delivery-network-ddn), but now we can use the same interface to upload SQLite files as well. Try it out:

<EmbeddedUpload />

# Why use Splitgraph for SQLite data?

[SQLite](https://sqlite.org/index.html) is an incredibly versatile embedded database, used by apps on mobile devices, client code running in web browsers (via WebAssembly), and native applications.
The main difference compared to more traditional relational databases such as PostgreSQL is that an SQLite database is just a file, there's no need for a dedicated database server process.
This is an enormous advantage in resource-constrained environments and makes it possible to build cost-effective data-intensive applications using [Litestream](https://litestream.io/) for example.
At the same time, SQLite's database-as-a-file approach makes concurrent multi user access -and thus collaboration- difficult.

In addition to collaboration, Splitgraph offers countless features beyond what SQLite provides, for example:
- column-based data storage for efficient aggregation queries
- immutable data images
- built-in support for [`dbt` transformations](dbt)
- joining tables from other databases using [foreign data wrappers](foreign-data-wrappers)

# What about Datasette?

[Datasette](https://datasette.io/) is a versatile open source tool for data analysis and publishing built on SQLite.
It's a popular solution for making SQLite data available to the public, but doesn't have built-in support for controlling access to published data.
For those Datasette users who need [fine grained access control](data-sharing-settings-invites-private-repositories), Splitgraph offers a solution.

Similar to Datasette, Splitgraph also makes it possible to directly query database tables [from the browser](select-from-the-browser).
Unlike Datasette, authenticated Splitgraph-backed web apps can even write to database tables.

Datasette documentation includes two demo databases, [`global-power-plants.db`](https://global-power-plants.datasettes.com/global-power-plants.db
) and [`legislators.db`](https://congress-legislators.datasettes.com/legislators.db), both of which can be loaded into Splitgraph. You can also try the SQLite version of the [Chinook](https://github.com/lerocha/chinook-database/blob/master/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite?raw=true) reference database. Try [uploading them here](/upload)!

# Key differences and limitations

SQLite [inherited](https://www.pgcon.org/2014/schedule/events/736.en.html) most of it's syntax from PostgreSQL, so the majority of SQLite queries should work on Splitgraph out of the box.
A major difference is that by default, SQLite allows any type of data to be written to any column, regardless of the column's declared type.
In fact, column type declarations are entirely optional in SQLite!

PostgreSQL is more strict when it comes to enforcing data types. Importing SQLite databases which store different types of data in a column than what was declared in the table schema may fail.
Tables containing columns without a declared type are ignored by the current implementation of SQLite import.

We'd love to hear from you! If you're excited about this new feature or if you're missing something from the current SQLite import functionality, [let us know](mailto:hello@splitgraph.com?subject=sqlite%20import)!


Importing SQLite data into Splitgraph is now as easy as drag-n-drop!

SQLite file uploads


## What is Splitgraph?

Splitgraph is building the Unified Data Stack – an integrated and modern solution
for working with data without worrying about its infrastructure.

You can try Splitgraph now! Browse
[the public data catalog](https://www.splitgraph.com/explore) to discover
40,000+ repositories to query with SQL, through the
[web IDE](https://www.splitgraph.com/query)
or [any Postgres client](https://www.splitgraph.com/connect).

## Introduction

There is a trend in data engineering to apply best practices from software engineering, for example, version control and code reviews. Splitgraph itself was originally inspired by Git and Docker, both famous developer tools.

Another DevOps tool we like is Terraform: it lets you use a declarative configuration language to define your cloud infrastructure. Terraform then takes care of interpreting this configuration and calling cloud vendor-specific APIs to create/destroy resources and align the real state of the world with the desired configuration.

We built something similar for the data stack: [`splitgraph.yml`](https://www.splitgraph.com/docs/splitgraph.yml/introduction), or Splitgraph Cloud project files.

You can think of `splitgraph.yml` as a Terraform file that lets you programmatically configure a dataset in Splitgraph, including its:

- metadata (README, tags, description)
- data source settings (databases, SaaS services, flat files)
- ingestion schedule
- dbt transformations

We use this format ourselves to declare data ingestion pipelines with Airbyte, build our own data warehouse with dbt and organize them in our Splitgraph data catalog (see a [previous blog post](https://www.splitgraph.com/blog/airbyte-dbt-splitgraph) for more details!)

In this post, we'll talk about how you can use `splitgraph.yml` to define your own data pipeline and show an end-to-end example that ingests some [sample data](https://data.cityofnewyork.us/Transportation/For-Hire-Vehicles-FHV-Active/8wbx-tsch) from Socrata and transforms it using dbt.

## Example `splitgraph.yml` file

Here is a basic example that builds two Splitgraph repositories:

  - [splitgraph-demo/for-hire-vehicles](https://www.splitgraph.com/splitgraph-demo/for-hire-vehicles): List of active for-hire vehicles in New York City ([source](https://data.cityofnewyork.us/Transportation/For-Hire-Vehicles-FHV-Active/8wbx-tsch))
  - [splitgraph-demo/for-hire-vehicles-summary](https://www.splitgraph.com/splitgraph-demo/for-hire-vehicles-summary): dbt transformation that summarizes the data in the first repository

```yaml
repositories:
- namespace: splitgraph-demo
  repository: for-hire-vehicles
  # Catalog-specific metadata for the repository.
  metadata:
    readme:
      text: For more information, see https://www.splitgraph.com/cityofnewyork-us/for-hire-vehicles-fhv-active-8wbx-tsch
    description: New York Active For Hire Vehicles
    topics:
    - socrata
    - new york
  # Data source settings
  external:
    plugin: csv
    params:
      connection:
        connection_type: "http"
        url: ""  # Will be overridden by the individual table
    tables:
      vehicles:
        options:
          url: "https://data.cityofnewyork.us/api/views/8wbx-tsch/rows.csv?accessType=DOWNLOAD"
        # Schema of the table (set to [] to infer)
        schema: []
    # Enable live querying for the plugin (creates a "live" tag in the
    # repository proxying to the data source).
    is_live: true

# dbt model that processes the previous repository
- namespace: splitgraph-demo
  repository: for-hire-vehicles-summary
  external:
    plugin: dbt
    credential: dbt-sample
    params:
      sources:
      # Map dbt source names to Splitgraph repositories
      - dbt_source_name: for_hire_vehicles
        namespace: splitgraph-demo
        repository: for-hire-vehicles
        hash_or_tag: latest
      git_branch: $THIS_SHA
    is_live: false
    tables: {}
  metadata:
    description: Sample dbt model
    readme:
      text: "## Sample dbt model\n\nThis is a model referencing data\
        \ from:\n\n  * [/splitgraph-demo/for-hire-vehicles](/splitgraph-demo/for-hire-vehicles)"
```

The full project that uses this file is located on [our GitHub](https://github.com/splitgraph/splitgraph-yml-example). You can also query the resultant dataset on Splitgraph:

<EmbeddedQuery
  query={`SELECT
  "Base Name",
  "Base Type",
  "Count"
FROM "splitgraph-demo/for-hire-vehicles-summary:latest"."vehicles_summary"
ORDER BY "Count" DESC;`} />

## Writing `splitgraph.yml` files

### Quick method: generating a Splitgraph Cloud project from a seed

The simplest way to get started with `splitgraph.yml` is by using our new "project seeds" functionality. This will generate a GitHub repository for you with a `splitgraph.yml` file, a dbt model and a GitHub Action file that you can use as a starting point to define your own pipeline.

We've done some magic to make the process smooth for you, so you can select data sources, copy a repo on GitHub, and paste a seed value into a GitHub action, to set up all your boilerplate.

Click the button to get started:

<ButtonLink href="/connect/splitgraph-yml">Generate splitgraph.yml and GitHub Repository</ButtonLink>
<br/>

Splitgraph supports over 100 sources. These include SaaS services (powered by the open Airbyte standard) as well as "live queries" to the most popular RDBMSs and data warehouses. The latter, powered by PostgreSQL Foreign Data Wrappers, lets you query data through Splitgraph without having to load it.

![Using the wizard to generate a splitgraph.yml file](/static-v2/images/blog/splitgraph-yml-terraform-data-pipeline/splitgraph-yml-wizard-selected.png)

The wizard will guide you through using our Splitgraph Cloud [template project](https://github.com/splitgraph/splitgraph-cloud-template) to generate a project from a "seed" (a representation of the data sources you chose).

![Using the Splitgraph Cloud template project to generate a splitgraph.yml file](/static-v2/images/blog/splitgraph-yml-terraform-data-pipeline/splitgraph-yml-project-seed.png)

After you go through the process, you will end up with a repository with Splitgraph Cloud project files:

  - A `splitgraph.yml` file that defines your chosen data
  - `build.moveme.yml` in the root with a GitHub Action definition used to ingest the data on a schedule or on-demand
  - Sample dbt model that references data from all the datasets you added

We created the [example project](https://github.com/splitgraph/splitgraph-yml-example) using this method.

You will need to perform some extra steps to finish the setup (see the README in the generated repository for more information):

  - Set up your Splitgraph API keys (we call them ["SQL credentials"](https://www.splitgraph.com/settings/sql-credentials), as you can also use them to connect to Splitgraph's PostgreSQL-compatible endpoint) as GitHub Secrets
  - Add credentials to `splitgraph.credentials.yml` (not required for our sample project, as the Socrata endpoint we're using to get the data is public)
  - Edit `splitgraph.yml` to set up the data source parameters
  - Set up GitHub Actions (copy `build.moveme.yml` to the `.github` directory)

### Advanced method: using `splitgraph.yml` with the `sgr` CLI

The example GitHub Action above calls the `sgr` CLI to interact with Splitgraph Cloud and manage datasets and pipelines.

You can run the same commands from your development machine or from another workflow orchestrator, like GitLab CI. Let's look at these commands in detail.

#### `sgr cloud stub`: generate a `splitgraph.yml` file

This command will generate a basic `splitgraph.yml` file for a specific plugin. You can use this file as a starting point [(documentation)](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-stub).

```bash
$ sgr cloud stub csv splitgraph-demo/csv
credentials:
  csv:  # This is the name of this credential that "external" sections can reference.
    plugin: csv
    # Credential-specific data matching the plugin's credential schema
    data:
      s3_access_key: ''  # AWS Access Key
      s3_secret_key: '' # AWS Secret Access Key
repositories:
- namespace: splitgraph-demo
  repository: csv
  # Catalog-specific metadata for the repository. Optional.
  metadata:
    readme:
      text: Readme
    description: Description of the repository
    topics:
    - sample_topic
  # Data source settings for the repository. Optional.
  external:
    # Name of the credential that the plugin uses. This can also be a credential_id if the
    # credential is already registered on Splitgraph.
    credential: csv
    plugin: csv
    # Plugin-specific parameters matching the plugin's parameters schema
    params:
      connection:  # Connection. Choose one of:
      - connection_type: http  # REQUIRED. Connection type. Constant
        url: '' # REQUIRED. URL. HTTP URL to the CSV file
      - connection_type: s3  # REQUIRED. Connection type. Constant
        s3_endpoint: '' # REQUIRED. S3 endpoint. S3 endpoint (including port if required)
        s3_bucket: '' # REQUIRED. Bucket name. Bucket the object is in
        s3_region: '' # S3 region. Region of the S3 bucket
        s3_secure: false # Secure. Whether to use HTTPS for S3 access
        s3_object: '' # S3 Object name. Limit the import to a single object
        s3_object_prefix: '' # S3 Object prefix. Prefix for object in S3 bucket
...
```

#### `sgr cloud load`: set up the catalog metadata

This command [(documentation)](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-load) uploads the README, tags, description as well as external data source settings and credentials to Splitgraph Cloud.

#### `sgr cloud sync`: run a data load

This command [(documentation)](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-sync) starts an ingestion job. If you previously ran `sgr cloud load`, you can use the existing data source settings uploaded to Splitgraph Cloud. You can also make this command use an existing `splitgraph.yml` file, which will run ingestion without Splitgraph Cloud storing your data source credentials.

#### Miscellaneous `sgr cloud` commands

There are other useful `sgr cloud` commands. See our [documentation](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-plugins) for a full list:

- [`sgr cloud upload`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-upload): upload a CSV file to Splitgraph Cloud
- [`sgr cloud download`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-download): export a SQL query result as a CSV file
- [`sgr cloud plugins`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-plugins): search for data source plugins
- [`sgr cloud search`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-search): search for repositories
- [`sgr cloud sql`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-sql): run SQL queries
- [`sgr cloud status`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-status): check the status of ingestion jobs

### `splitgraph.yml` reference

An in-depth reference for the `splitgraph.yml` format is available [here](https://www.splitgraph.com/docs/splitgraph.yml/introduction).

### In Alpha: Web UI

Configure datasets individually via the web GUI. You can try it here, but note
it's in alpha status and some configurations are not yet fully supported:

<ButtonLink href="/connect/data">Connect Data</ButtonLink>
<br />

![Upcoming Web UI to add data to Splitgraph](/static-v2/images/blog/splitgraph-yml-terraform-data-pipeline/splitgraph-yml-web-ui.png)

You can still store your catalog in the `splitgraph.yml` format with this method: use the [`sgr cloud dump`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-dump) command to dump existing repositories to `splitgraph.yml`.

## Conclusion

In this blog post, we showcased `splitgraph.yml`: a configuration format that lets you programmatically manage your datasets on Splitgraph. You can find an example project on [our GitHub](https://github.com/splitgraph/splitgraph-yml-example).

The example project also shows how to make the data repositories that you build private. You can then use our [sharing settings and invites functionality](/blog/data-sharing-settings-invites-private-repositories) to manage who has access to your datasets and invite collaborators.

Interested in tighter security guarantees or more team features? We also provide private versions of Splitgraph Cloud, deployed to a cloud vendor and region of your choice. Feel free to [try it out](https://www.splitgraph.com/demo)!


We showcase the splitgraph.yml format, which lets you programmatically manage your datasets on Splitgraph, change their data source settings and define dbt transformations.

We showcase the `splitgraph.yml` format, which lets you programmatically manage your datasets on Splitgraph, change their data source settings and define dbt transformations.

`splitgraph.yml`: Terraform for your data stack

splitgraph.yml: Terraform for your data stack


### Create a dataset you wish to share

The easiest way to add data to Splitgraph is by uploading a CSV file. Go
to [the Upload page](/upload) or use this widget:

<EmbeddedUpload />

See
a [previous blog post](/blog/csv-to-sql-upload-to-data-delivery-network-ddn)
for more information.

You can also run a Splitgraph client locally. Check out
the [five minute demo](https://www.splitgraph.com/docs/sgr-advanced/getting-started/five-minute-demo)
that will show you how to create a dataset and publish it on Splitgraph.

In addition, we're working on a UI to add data to Splitgraph that will let
you ingest and query data from over 100 SaaS sources and databases.

### Open the Publication Status dialog

You can find the new Publication Status dialog to the right of the repository
name.

![Location of the publication status dialog](/static-v2/images/blog/data-sharing-settings-private-repositories/data-sharing-publication-button.png)

In here, you can:

- set a repository as private. This means only you (and users you add) will be
  able to read data from it or even find it.
- invite users to your repository
- see who can access your repository and manage their access settings

![Publication status dialog](/static-v2/images/blog/data-sharing-settings-private-repositories/data-sharing-publication-dialog.png)

### Invite a collaborator by username or email

If your collaborator is already registered on Splitgraph, you can add them to
the repository using their username:

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/add-user-demo.mp4" type="video/mp4" />
</video>

You can also add a contributor by their e-mail address. If they are already
registered on Splitgraph, they will be automatically added to your repository.

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/invite-user-demo.mp4" type="video/mp4" />
</video>

If they're not registered, they will receive an e-mail with an invitation to
register on Splitgraph.

![Sample invitation email](/static-v2/images/blog/data-sharing-settings-private-repositories/data-sharing-invite-email.png)

### Manage levels of access

We currently support three roles:

- Reader: Can view and query tables in a repository through the [DDN](/connect) or pull images with [`sgr`](https://www.splitgraph.com/docs/sgr-advanced/architecture/sgr-client)
- Writer: Can modify the repository's README and push data to it using `sgr`
- Admin: Has all permissions and can control sharing

If a repository is public, everyone on Splitgraph has an implicit Reader role on it.

## Next steps

- Query the dataset over the [DDN](/connect) or the [REST API](https://www.splitgraph.com/docs/query/postgrest)
- Check out the
  [`splitgraph.yml`](https://www.splitgraph.com/docs/splitgraph.yml/introduction)
  format for a sneak peek of how you will be able to add more data to Splitgraph

## Want a private Splitgraph instance?

If you want Splitgraph for your internal company data,
then [join the private beta](https://www.splitgraph.com/join-beta) for our cloud
SaaS product. We can deploy a white-labeled instance to your own infrastructure
and setup a modern data stack for you without any hassle.


Splitgraph now supports advanced data sharing settings. Make a repository private, invite a collaborator and control their level of access, all from a simple Web UI.

Share datasets like Notion pages


## Introduction

In a [previous blog post](/blog/airbyte-dbt-splitgraph), we talked about our own data stack that uses dbt and Airbyte to build our data warehouse.

![Our main analytics dataset, built with dbt on our private instance of Splitgraph Cloud.](/static-v2/images/blog/dbt-splitgraph-cloud/dbt-data-catalog.png)
_Our main analytics dataset, built with dbt on our private instance of Splitgraph Cloud._

Today, we will show you how you can add your own dbt project to Splitgraph Cloud to build Splitgraph images with dbt models. We'll also discuss how the dbt transformation functionality works.

**Don't have much time? [Our GitHub](https://github.com/splitgraph/dbt-transform-example/) has a forkable self-contained example that uses a GitHub Action to build [a dataset of top commodity exporters in each country](https://www.splitgraph.com/splitgraph/dbt-transform-example). Feel free to check it out!**

## Using the `splitgraph.yml` file

`splitgraph.yml` is a declarative way to define the datasets to add to Splitgraph. You can think of it as a Terraform template for your data stack. See more information in the documentation [here](https://www.splitgraph.com/docs/splitgraph.yml/introduction).

We are also working on a UI to add data to Splitgraph. It will let you ingest data from over 100 SaaS sources (powered by Airbyte and Singer). It will also support data federation for popular databases and warehouses (powered by PostgreSQL Foreign Data Wrappers).

This is what the part of our `splitgraph.yml` file responsible for the dbt transformation looks like:

```yaml
credentials:
  splitgraph_dbt:
    plugin: dbt
    data:
      git_url: https://[USERNAME]:[PASSWORD]@gitlab.com/splitgraph/core/analytics.git

repositories:

  # Data transformed with dbt
  - namespace: analytics
    repository: splitgraph-com
    external:
      credential: splitgraph_dbt
      plugin: dbt
      is_live: false
      schedule:
        enabled: true
        # NOTE: we currently don't support inter-task dependencies,
        #   so we run this 10 minutes after the main raw data update
        #   starts
        schedule: 10 */6 * * *
      params:
        sources:
          - dbt_source_name: airbyte_raw
            namespace: airbyte-raw
            repository: splitgraph-com
          - dbt_source_name: stopforumspam
            namespace: analytics
            repository: stopforumspam
      # This lets us select specific dbt models to build in this image
      tables:
        daily_summary:
          options: { }
          schema: [ ]
        dim_users:
          options: { }
          schema: [ ]
        fct_export_jobs:
          options: { }
          schema: [ ]
        fct_ingestion_jobs:
          options: { }
          schema: [ ]
        # ...
    metadata:
      topics:
        - analytics
        - splitgraph-com
        - processed
        - normalization:custom
        - dbt
      sources:
        - anchor: dbt model for splitgraph-com
          href: https://gitlab.com/splitgraph/core/analytics
      description: Main data warehouse for splitgraph.com analytics
      readme:
        text: |
          # Splitgraph Analytics

          Built dbt models for splitgraph.com analytics (users, activity etc)
          using https://gitlab.com/splitgraph/core/analytics

          This dataset on Metabase: https://admin.splitgraph.com/metabase/browse/5/schema/analytics~splitgraph-com
      extra_metadata:
        dbt:
          project: https://gitlab.com/splitgraph/core/analytics
```

This snippet:

  - uses the raw data we ingest into the `airbyte-raw/splitgraph-com` repository with Airbyte. It's also in this file but has been omitted.
  - uses the data in the `analytics/stopforumspam`  repository (available [here](https://www.splitgraph.com/splitgraph/stopforumspam))
  - builds a dbt model with statistics on active users and feature usage. It does that by joining the two datasets to exclude activity from users with known spam e-mails.

### Running the file

There are two ways you can run a dbt model on Splitgraph Cloud.

The first method is using the `schedule` stanza. This lets you specify a cron-like syntax for Splitgraph to run the model on schedule. Note that currently we don't support inter-task dependencies. For us, this means we have to give the source `airbyte-raw/splitgraph-com` dataset some time to load and start the dbt model later.

To do that, use the [`sgr cloud load`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-load) command to upload the `splitgraph.yml` file to Splitgraph Cloud.

The second method is by triggering the job from GitHub Actions. Our [GitHub example](https://github.com/splitgraph/dbt-transform-example/) runs the ingestion this way by executing [`sgr cloud sync`](https://www.splitgraph.com/docs/sgr/splitgraph-cloud/cloud-sync). This lets you control when the job starts as well as manage inter-task dependencies. You can find the [full GitHub Action code](https://github.com/splitgraph/dbt-transform-example/blob/main/.github/workflows/build.yml#L21-L22) here.

### Result

In either case, you will end up with a repository of images built from the dbt model. One benefit of using Splitgraph for this is that this repository is versioned. This means you can easily switch back to the old versions of the dataset.

![Tag selector letting you switch between dataset versions](/static-v2/images/blog/dbt-splitgraph-cloud/dbt-versioning.png)

You can also query the dataset on the [Splitgraph DDN](https://www.splitgraph.com/docs/query/ddn) with any SQL client. You can even run JOINs between different versions of the dataset to see how it evolved.

![Using DBeaver to query different versions of a dbt model](/static-v2/images/blog/dbt-splitgraph-cloud/dbt-versioning-query.png)

Interested in seeing how this works? Read on!

## How it works

At its core, Splitgraph is a tool for building and sharing versioned "data products" (we call them data images). Each image is a snapshot of a PostgreSQL schema that you can freely move around and query.

dbt itself expects to be able to work against a database with a single shared namespace. It handles multitenancy by letting you build a dataset in a schema with a custom prefix. Each dbt user uses a separate target schema. This means their models don't step on the actual production models. See the [dbt documentation](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas#why-does-dbt-concatenate-the-custom-schema-to-the-target-schema) for more details.

This presents some challenges in how we integrate Splitgraph Cloud with dbt.

We talked a bit about how the open-source Splitgraph library can work with dbt in [a previous blog post](https://www.splitgraph.com/blog/dbt). When running it in a managed fashion, we need to be able to automate all these steps.

Roughly, here's what we do:

  - When a user adds a dbt project to Splitgraph, introspect it and let the user select which models they want to build in their dataset
  - When running the dbt project:
	  - Set up a temporary Splitgraph engine
	  - Set the dbt sources to point to specific Splitgraph datasets required by it
	  - Set the dbt target to a specific schema on the engine
	  - Run dbt
	  - Run `sgr commit` to build the image and push it out

### Introspection

This is a step that isn't required when using `splitgraph.yml`. However, we are currently working on a GUI to add data sources to Splitgraph. The first part of this workflow will be a "preview" stage. When you input the connection parameters, Splitgraph will introspect it and return a list of tables it discovered in the data source.

In case of dbt, we perform introspection by compiling your dbt project and extracting its [manifest file](https://docs.getdbt.com/reference/artifacts/manifest-json). Here's the [code](https://github.com/splitgraph/sgr/blob/7677454da0666d6e57c13e4129b9cd4dcc26a209/splitgraph/ingestion/dbt/utils.py#L199).

Then, we parse the manifest file and load a list of models in it ([code](https://github.com/splitgraph/sgr/blob/7677454da0666d6e57c13e4129b9cd4dcc26a209/splitgraph/ingestion/dbt/data_source.py#L123-L129)). Each model will become a table in your Splitgraph image.

### Patching sources

This is the part of the plugin configuration that's responsible for it:

```yaml
sources:
- dbt_source_name: airbyte_raw
  namespace: airbyte-raw
  repository: splitgraph-com
- dbt_source_name: stopforumspam
  namespace: analytics
  repository: stopforumspam
```

The dbt plugin crawls the project for all source references ([code](https://github.com/splitgraph/sgr/blob/7677454da0666d6e57c13e4129b9cd4dcc26a209/splitgraph/ingestion/dbt/utils.py#L74)). It then rewrites each source's schema to point to a temporary schema. This temporary schema has the dependent Splitgraph image checked out in [layered querying](https://www.splitgraph.com/docs/sgr-advanced/large-datasets/layered-querying) mode.

Here's an example of our dbt YAML file defining sources:

```yaml
version: 2
sources:
- name: airbyte_raw
  schema: "airbyte-raw/splitgraph-com"
  tables:
  - name: _airbyte_raw_celery_events_raw
  - name: _airbyte_raw_gql_api_calls_raw
  # ...
- name: stopforumspam
  schema: "analytics/stopforumspam"
  tables:
  - name: listed_email_365_all
  - name: listed_ip_365_ipv46_all
  - name: listed_username_365_all
```

The `schema` in this case is what the plugin patches out. The default value for the schema is the standard Splitgraph format of `namespace/repository`. This is to make local development easier (more below).

### Running dbt

We make a special [profile](https://github.com/splitgraph/sgr/blob/7677454da0666d6e57c13e4129b9cd4dcc26a209/splitgraph/ingestion/dbt/utils.py#L45) to run dbt against the Splitgraph engine and get it to write the output to a temporary schema. Note that this won't capture cases where a dbt model uses a [custom schema](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas#how-do-i-use-custom-schemas).

Finally, we run `sgr commit` to build the image. We also give it a versioned tag and push it out. At this point, it becomes just another dataset in our catalog that we can query with any SQL client.

In our case, we point Metabase at this dataset to build beautiful BI dashboards.

## Local development

Developing the dbt model with this method is more complex than just running dbt against a data warehouse. We have plans to improve this.

In the meantime, here's how you can do local development.

First, you'll need to [install sgr](https://github.com/splitgraph/sgr/releases) and [set up a Splitgraph engine](https://www.splitgraph.com/docs/sgr-advanced/getting-started/installation#manual).

Then, set up a dbt profile file to work with the engine:

```yaml
$ cat ~/.dbt/profiles.yml
splitgraph:
  outputs:
    prod:
      dbname: splitgraph
      # Change the connection parameters to the ones
      # you set up for the engine
      host: 127.0.0.1
      pass: password
      port: 6432
      schema: my_target_schema
      threads: 32
      type: postgres
      user: sgr
  target: prod
```

Then, set up the source datasets. We'll be checking them out on the local engine into the `namespace/repository` PostgreSQL schemas. This is the reason we default to `schema: "namespace/repository"` in the dbt source file:

```bash
sgr clone my-source/repo:latest
sgr checkout --layered my-source/repo:latest
```

Finally, run the model:

```bash
dbt build --profile splitgraph
```

## Future plans

### Integrating the dbt docs site

Every dbt model comes with plenty of metadata about model and column meanings as well as tests. dbt uses this to generate its docs site.

Splitgraph could extend on its dbt manifest parsing and ingest that metadata as well. It would then be able to display it in the repository's overview page.

We could even integrate this with Splitgraph's lineage tracking features. This would let you click through to the source datasets for each built dbt model and query them.

### Cataloging dbt models built on other warehouses

Splitgraph also supports live querying (data federation). Instead of running the dbt model itself, it could parse the manifest for a dbt project built in another data warehouse (Snowflake, Redshift, BigQuery etc). It would then create a set of repositories pointing to the data in that warehouse.

### Autogenerating dbt models

With plenty of sources to come to Splitgraph, the next question will be how to make them immediately usable to people ingesting this data.

We could include some simple pre-generated queries for commonly used data sources. For example, when adding your data from Stripe, you could see a sample query for your monthly revenue.

We've done some research on feasibility of this. As part of that, we recently published an [Airbyte-to-Fivetran Stripe adapter](https://hub.getdbt.com/splitgraph/ab2ft_stripe/latest/) to dbt Hub. This package converts the Stripe data loaded by Airbyte into a format that's usable by [Fivetran's Stripe dbt package](https://hub.getdbt.com/fivetran/stripe/latest/).

### Writeable DDN

The local development story with running dbt models on Splitgraph is a bit lacking. Ideally, a user would be able to run dbt on the [Splitgraph DDN](https://www.splitgraph.com/docs/query/ddn) without running a local Splitgraph engine.

This will involve us building the ability to write to the DDN, including support for `CREATE TABLE AS` statements that dbt uses to build models.

## Conclusion

In this post, we talked about Splitgraph Cloud's support for dbt models and did an in-depth dive of how it works and how we use it ourselves.

On our GitHub, you can find [a similar self-contained example](https://github.com/splitgraph/dbt-transform-example/) of this feature. It uses a GitHub Action to build [a dataset of top commodity exporters in each country](https://www.splitgraph.com/splitgraph/dbt-transform-example).

We also discussed our future ideas for dbt support. If you have any ideas or use cases to share, feel free to [drop us an e-mail](mailto:support@splitgraph.com). If you're interested in a similar setup, [get in touch](https://4o99daw6ffu.typeform.com/to/zO5zBG3d?typeform-source=www.splitgraph.com) and join our private beta!


We showcase the ability to run dbt models on Splitgraph, triggering them on a schedule as well as using GitHub Actions. We also talk about how it works and share more plans for our dbt integration.

Scheduling, versioning and cataloging: introducing our dbt integration


## What is Splitgraph?

Splitgraph is building the Unified Data Stack – an integrated and modern solution
for working with data without worrying about its infrastructure.

You can try Splitgraph now! Browse
[the public data catalog](https://www.splitgraph.com/explore) to discover
40,000+ repositories to query with SQL, through the
[web IDE](https://www.splitgraph.com/query)
or [any Postgres client](https://www.splitgraph.com/connect).

Splitgraph is powered by open standards and simple abstractions, like
[data images](https://www.splitgraph.com/docs/concepts/images) – immutable tables
that you can push and pull, or query on the fly.

## Push to the Data Delivery Network (DDN)

You can use the `sgr` command line client to build images from dozens of data
sources,
from [CSV files](https://www.splitgraph.com/docs/sgr-advanced/ingesting-data/load-csv-files)
to
[custom Foreign Data Wrappers](https://www.splitgraph.com/blog/foreign-data-wrappers).
To push them to the DDN, just run `sgr push`, like you would a Git branch or
Docker image.

Splitgraph helps data engineers work more like software engineers. But what if
you're not a data engineer? What if you just want to share a CSV file with
someone else, or query it with SQL, or join it with some other data?

That's why we built this feature – **drag and drop a CSV file** to upload it to
the Data Delivery Network, where you can publish it or share it privately. You
can even upload multiple, to create a table from each! (You can join across
those tables, too.)

## Upload, Share and Query CSV File(s)

Navigate to [`splitgraph.com/upload`](https://www.splitgraph.com/upload) to get
started,
or try it right from this blog post! Just upload the CSV file(s) you want
to publish as tables (privately or with anyone you share them with).

<EmbeddedUpload />

## Videos

Impatient? Watch these ten second clips demonstrating the feature.

### Upload one or multiple CSV files

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/upload-csv-files-demo.mp4" type="video/mp4" />
</video>

### Configure parameters

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/configure-csv-demo.mp4" type="video/mp4" />
</video>

### Invite collaborators by email

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/invite-user-demo.mp4" type="video/mp4" />
</video>

### Add collaborator by username

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/add-user-demo.mp4" type="video/mp4" />
</video>


## What's Next?

We've got a lot planned, and CSV uploads are just the tip of the iceberg. We're following a general theme of augmenting powerful command line tools
with a simple and intuitive web interface. You can already do much of this
from the CLI, but from the web you'll soon be able to:

- Connect to, or ingest from, hundreds of external data sources, all configurable via the web interface
- Create a [`splitgraph.yml`](https://www.splitgraph.com/docs/splitgraph.yml/introduction)
file from the web, and clone a GitHub repo to orchestrate scheduled ingestion of your data sources

## Want a private Splitgraph instance?

If you want Splitgraph for your internal company data,
then [join the private beta](https://www.splitgraph.com/join-beta) for our cloud
SaaS product. We can deploy a white-labeled instance to your own infrastructure
and setup a modern data stack for you without any hassle.



You can now upload CSV files to Splitgraph from the web, so that you can query them with SQL by pointing your Postgres client to the Data Delivery Network (DDN). Share data publicly or with only those you invite – all discoverable and queryable from a single unified interface.

Splitgraph has been acquired by EDB! Read the blog post.

feature

SQLite file uploads

`splitgraph.yml`: Terraform for your data stack

Share datasets like Notion pages

Scheduling, versioning and cataloging: introducing our dbt integration

Drag, drop and share CSV files as queryable SQL tables

Product

Support

Company

Splitgraph

Splitgraph has been acquired by EDB! Read the blog post.

feature

SQLite file uploads

splitgraph.yml: Terraform for your data stack

Share datasets like Notion pages

Scheduling, versioning and cataloging: introducing our dbt integration

Drag, drop and share CSV files as queryable SQL tables

Product

Support

Company

Community

Splitgraph

`splitgraph.yml`: Terraform for your data stack