Query the Data Delivery Network
Query the DDNThe 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 data.splitgraph.com:5432 and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.
For example, you can query the quarterly_census_of_employment_and_wages_qcew_2019 table in this repository, by referencing it like:
"pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf:latest"."quarterly_census_of_employment_and_wages_qcew_2019"or in a full query, like:
SELECT
":id", -- Socrata column ID
"latitude", -- This is a generic latitude point for the county so that a map can be created.
"longitude", -- This is a generic longitude point for the county so that a map can be created.
"weekly_wages", -- QCEW wages represent total compensation paid during the calendar year, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging. Weekly Wages are derived by dividing total wages reported by average employment and then dividing the quotient by 52 weeks per year. When the field is blank it tells us the data is non-disclosed which has the following meaning: The non-disclosure guidelines essentially exist for one or two reasons: 1) Disclosure of the information would breach confidentiality. 2) The data lacks the statistical rigor to be valid or reliable.
"employment", -- Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers. Employment data is presented as the 12-month average of the calendar year. When the field is blank it tells us the data is non-disclosed which has the following meaning: The non-disclosure guidelines essentially exist for one or two reasons: 1) Disclosure of the information would breach confidentiality. 2) The data lacks the statistical rigor to be valid or reliable.
"establishments", -- An employer establishment represents a single economic unit such as a mine, factory or store engaged in one, or predominantly one activity. An employer represents a business entity and may consist of one or more establishments. Establishments represent the 12-month average of monthly counts.
"naics", -- The North American Industry Classification System (NAICS) is the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. The NAICS coding hierarchy shared among Canada, Mexico, and the United States ranges from aggregated 2-digit industry sectors to detailed 6-digit country-specific industries. Industries at the 2-, 3-, 4-, and 5-digit NAICS level are comparable among all three countries.
"calendar_quarter", -- Represents the period inclusive of stated Quarter.
"county_fips", -- FIPS code. The Federal Information Processing Standard (FIPS) code, used by the United States government to uniquely identify counties, is provided with each entry. FIPS codes are five-digit numbers; for Pennsylvania the codes start with 42 and are completed with the three-digit county code. The County FIPS is the last three digits of the five digit FIPS and the code 000 is for statewide.
"georeferenced_latitude_longitude", -- Georeferenced Latitude and Longitude column as generic points for each county that can be used for creating visuals such as maps.
"naics_title", -- The North American Industry Classification System (NAICS) Title conveys in brief the industries represented by the NAICS code.
"county_code", -- There are 67 counties in Pennsylvania. They're numbered 01 thru 67, and 00 identifies the statewide total.
"area_name", -- The name for the area that the row of data supports. The state of Pennsylvania and all the county names.
"state_fips", -- The Federal Information Processing Standard (FIPS) code, used by the United States government to uniquely identify states and counties. FIPS codes are five-digit numbers; for Pennsylvania the codes start with 42 and are completed with the three-digit county code. The state code is the first two digits of the five digit FIPS code.
":@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 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@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 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
FROM
"pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf:latest"."quarterly_census_of_employment_and_wages_qcew_2019"
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/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf with SQL in under 60 seconds.
Query Your Local Engine
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"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, 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 (like this repository), where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Cloning Data
Because pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf:latest is a Splitgraph Image, you can clone the data from Spltgraph Cloud to your local engine, where you can query it like any other Postgres database, using any of your existing tools.
First, install Splitgraph if you haven't already.
Clone the metadata with sgr clone
This will be quick, and does not download the actual data.
sgr clone pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xfCheckout the data
Once you've cloned the data, you need to "checkout" the tag that you want. For example, to checkout the latest tag:
sgr checkout pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf:latestThis will download all the objects for the latest tag of pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf and load them into the Splitgraph Engine. Depending on your connection speed and the size of the data, you will need to wait for the checkout to complete. Once it's complete, you will be able to query the data like you would any other Postgres database.
Alternatively, use "layered checkout" to avoid downloading all the data
The data in pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf:latest is 0 bytes. If this is too big to download all at once, or perhaps you only need to query a subset of it, you can use a layered checkout.:
sgr checkout --layered pa-gov/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf:latestThis will not download all the data, but it will create a schema comprised of foreign tables, that you can query as you would any other data. Splitgraph will lazily download the required objects as you query the data. In some cases, this might be faster or more efficient than a regular checkout.
Read the layered querying documentation to learn about when and why you might want to use layered queries.
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/quarterly-census-of-employment-and-wages-qcew-2019-bm6e-y9xf is just another Postgres schema.