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 marine_zooplankton_detailed_records table in this repository, by referencing it like:


or in a full query, like:

    ":id", -- Socrata column ID
    "datasource", -- Who the data were analyzed by
    "profile_id", -- Unique identifier to link samples collected on the same day, at the same place, but at different depths
    "sample_id", -- Unique identifier to link samples collected from the same net
    "steward_note", -- Text entered by secondary data reviewers
    "biomass_mg_c_m3_", -- Biomass of the taxon and its life history stage
    "abundance_ind_m_3", -- Abundance of the taxon and its life history stage
    "species", -- Species level of taxon
    "genus", -- Genus level of taxon
    "class", -- Class level of taxon
    "phylum", -- Phylum level of taxon
    "functional_group", -- High-level taxonomic group
    "life_stage", -- Life history stage of the taxon
    "taxon", -- Identified taxon (could be at the genus level, family level, etc.)
    "volume_sampled_m_3", -- Total volume of sample collected
    "sensor_depth_m", -- Maximum depth of tow based on sensor (only for oblique tows, in meters)
    "station_depth_m", -- Depth of station in meters
    "collectdatetime", -- Date and time samples were collected
    "order", -- Order level of taxon
    "ssmsp_taxa_group", -- Salish Sea Marine Survival Project taxa group
    "sex", -- For some adult, male and females are distinguished
    "basin", -- Puget Sound Basin where samples were collected
    "uw_sample_code", -- Unique sample identifier used by the University of Washington Keister Lab to track samples
    "mesh_size_um", -- Size of net mesh
    "locator", -- Coordinate information available at:
    "agency", -- Group who collected the zooplankton samples
    "family", -- Family level of taxon
    "kingdom", -- Kingdom level of taxon
    "rank", -- Taxonomic rank identified to
    "site_name", -- Description of sample collection location
    "tow_depth_m_estimated", -- Maximum depth tow was deployed to (estimated in meters)
    "quality", -- Code indicating overall quality of the data (see DataReadMeFile_WQ.docx for definitions) 
    "tow_type" -- Sample collection method identifying type of zooplankton tow 
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 kingcounty-gov/marine-zooplankton-detailed-records-fp47-d4q8 with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at When you querykingcounty-gov/marine-zooplankton-detailed-records-fp47-d4q8: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 \
  "kingcounty-gov/marine-zooplankton-detailed-records-fp47-d4q8" \
  --handler-options '{
    "domain": "",
    "tables": {
        "marine_zooplankton_detailed_records": "fp47-d4q8"

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, kingcounty-gov/marine-zooplankton-detailed-records-fp47-d4q8 is just another Postgres schema.