Icon for Socrata external plugin

Query the Data Delivery Network

Query the DDN

The easiest way to query any data on Splitgraph is via the "Data Delivery Network" (DDN). The DDN is a single endpoint that speaks the PostgreSQL wire protocol. Any Splitgraph user can connect to it at and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.

For example, you can query the health_education_related_locations_statewide table in this repository, by referencing it like:


or in a full query, like:

    ":id", -- Socrata column ID
    "education_aun", -- Administrative Unit Number (AUN) - A 9-digit number assigned by the Pennsylvania Department of Education (PDE) to uniquely identify entities such as school districts, intermediate units, career and technical centers, charter schools, non-public and private schools, higher education institutions, state juvenile correctional institutions, dioceses, private residential rehabilitation institutions, private driver training schools, and libraries
    "directions", -- This link will open an external mapping application to help you navigate to this point of interest. 
    "latitude", -- Longitude of the Health related locations
    "licensed_personal_care_home_street_additional", -- Street Additional Information
    "education_grades", -- Grades that exist in the Educational Institution
    "phonenumber", -- Phone Number
    "layername", -- Health related locations Layer
    "edcuation_zipcodeextn", -- Education Entity Zip Code additional 4-digit
    "education_addressline2", -- Additional Information for Education entity addresses
    "address", -- Address of the Health related locations
    "city", -- Street of the Health related locations
    "licensed_personal_care_home_program_office", -- Program Office
    "the_geom", -- The Geometrical point
    "licensed_personal_care_home_total_capacity", -- Total Capacity
    "state", -- State of the Health related locations
    "licensed_personal_care_home_accuracy", -- Accuracy of the Latitude and Longitude: X is for an intersection, 5 is interpolated house on street, 4 is street centroid, 3 is Zip Code + Extension centroid, 2 is Zip + 2 centroid, and 1 is Zip centroid.
    ":@computed_region_3x3q_vpda", -- This column was automatically created in order to record in what polygon from the dataset 'US House Districts for PA 2019' (3x3q-vpda) the point in column 'child_care_provider_legal_entity_location' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    ":@computed_region_4fjn_fq7k", -- This column was automatically created in order to record in what polygon from the dataset 'PA County Boundaries Spatial Data Current Transportation' (4fjn-fq7k) the point in column 'child_care_provider_legal_entity_location' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    "longitude", -- Latitude of the Health related locations
    "licensed_personal_care_home_type_served", -- Type Served
    "education_webaddress", -- Web Address if available for the Educational Institution
    "more_info", -- This link will provide additional information.
    "zip", -- Zip Code of the Health related locations
    "sthealthcntr_fax_number", -- Fax Number of the Health related locations
    "education_school", -- Number associated with the Educational Institution
    "name", -- Name of the Health related locations
LIMIT 100;

Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query pa-gov/health-education-related-locations-statewide-da8t-93kj with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at When you querypa-gov/health-education-related-locations-statewide-da8t-93kj:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).

We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.

Query Your Local Engine

Install Splitgraph Locally
bash -c "$(curl -sL"

Read the installation docs.

Splitgraph Cloud is built around Splitgraph Core (GitHub), which includes a local Splitgraph Engine packaged as a Docker image. Splitgraph Cloud is basically a scaled-up version of that local Engine. When you query the Data Delivery Network or the REST API, we mount the relevant datasets in an Engine on our servers and execute your query on it.

It's possible to run this engine locally. You'll need a Mac, Windows or Linux system to install sgr, and a Docker installation to run the engine. You don't need to know how to actually use Docker; sgrcan manage the image, container and volume for you.

There are a few ways to ingest data into the local engine.

For external repositories (like this repository), the Splitgraph Engine can "mount" upstream data sources by using sgr mount. This feature is built around Postgres Foreign Data Wrappers (FDW). You can write custom "mount handlers" for any upstream data source. For an example, we blogged about making a custom mount handler for HackerNews stories.

For hosted datasets, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Mounting Data

This repository is an external repository. It's not hosted by Splitgraph. It is hosted by, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.

First, install Splitgraph if you haven't already.

Mount the table with sgr mount

sgr mount socrata \
  "pa-gov/health-education-related-locations-statewide-da8t-93kj" \
  --handler-options '{
    "domain": "",
    "tables": {
        "health_education_related_locations_statewide": "da8t-93kj"

That's it! Now you can query the data in the mounted table like any other Postgres table.

Query the data with your existing tools

Once you've loaded the data into your local Splitgraph engine, you can query it with any of your existing tools. As far as they're concerned, pa-gov/health-education-related-locations-statewide-da8t-93kj is just another Postgres schema.