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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 occupational_wages_2018_labor_and_industry table in this repository, by referencing it like:


or in a full query, like:

    ":id", -- Socrata column ID
    "state_fips", -- These are the first 2 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the State association. Each State has its own 2-digit number and each County within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county. For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.). Information gathered from census data -
    "average_annual_wage", -- Average wage in this publication refers to the mean wage. Wage data in the OES program are collected and grouped in 12 intervals. The number of employees in an occupation that are paid at each wage interval is multiplied by the mid-point of the interval. These products are then summed and the sum is divided by total employment for the occupation to obtain a mean hourly wage for the occupation. Hourly wages are converted to annual wages by multiplying by 2,080 hours.
    "entry_annual_wage", -- The mean of the lower-third of the wages for an occupation. This calculation is provided as a proxy for an entry-level wage.
    "county_code", -- Two-digit county code includes the leading zeroes. There are 67 counties in Pennsylvania.
    "area_name", -- The English name for Pennsylvania state or the appropriate Pennsylvania County for that row. 
    "experienced_annual_wage", -- The mean of the upper two-thirds of the wages for an occupation. This calculation is provided as a proxy for an experienced-level wage.
    "soc_title", -- The Standard Occupational Classification (SOC) Title conveys in brief the occupations represented by the SOC code.
    "soc", -- Occupations are classified based on the revised national Standard Occupational Classification (SOC) system. The revised SOC was developed in response to a growing need for a universal occupational classification system. The system, which is designed to cover all occupations in which work is performed for pay or profit, reflects the current occupational structure in the US. It is used by all federal agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. Occupations are combined to form major and minor groups requiring similar job duties, skills, education, or experience.
    "wage_area", -- Name of the area indicated in Wage Type.
    "wage_type", -- Indicates the geographical level for which the occupational wage is provided. When county wages are not available, the wage for the smallest available geographic area that includes the county is substituted. Wage Types are County (CTY), Workforce Development Area (WDA), Metropolitan Statistical Area (MSA) or Pennsylvania (PA).
    "county_fips", -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code (FIPS 6-4) which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. This is the 3-digit part of the 5-digit county FIPS code specifically standing for the county.
    "wage_period" -- Identifies the most recent period of data included in the wage calculation. Each semi-annual panel represents one-sixth of the sample for the full three-year sample. Utilizing three years of data significantly reduces sampling error, but requires the adjustment of the earlier two years of wage data to the current time period using the national Employment Cost Index or ECI.
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/occupational-wages-2018-labor-and-industry-mdvn-squs 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/occupational-wages-2018-labor-and-industry-mdvn-squs: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/occupational-wages-2018-labor-and-industry-mdvn-squs" \
  --handler-options '{
    "domain": "",
    "tables": {
        "occupational_wages_2018_labor_and_industry": "mdvn-squs"

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/occupational-wages-2018-labor-and-industry-mdvn-squs is just another Postgres schema.