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 lead_testing_in_school_drinking_water_sampling_and table in this repository, by referencing it like:
"health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9:latest"."lead_testing_in_school_drinking_water_sampling_and"or in a full query, like:
SELECT
":id", -- Socrata column ID
"location_zip",
":@computed_region_9yqb_tdyd",
"sampling_complete", -- Indicates if all sampling for the school is complete. “Yes” = Complete/ “No” = Incomplete
"sampling_completion_date", -- Date all sampling was completed including post-remediation sampling
"county_location_city",
"county_location_state",
"outlets_sampled_after", -- Number of outlets sampled after the regulation went into effect
"school_street_address", -- School street
"school_zip_code", -- School zip code
"county_location", -- Latitude/longitude decimal degree coordinates for the region covered by the indicator, for use in mapping
"type_of_organization", -- Public school or Board of Cooperative Educational Services (BOCES)
"number_of_outlets", -- The total number of outlets required to be sampled
"school_state", -- School state
"location_address",
"school_district", -- School district name
"school", -- School name
"county", -- County where the school is located
"any_lead_free_buildings", -- “No” or “This school has one or more buildings with lead-free plumbing”
"outlets_sampled_before", -- Number of outlets sampled before September 2016, consistent with the regulation (10 NYCRR Subpart 67)
"outlets_waiver_requested", -- Number of outlets sampled before September 2016, substantially in compliance with the regulation, and a formal waiver was requested
"waivers_granted", -- Number of outlets that did not require sampling for Compliance Year 2016, as a formal waiver was granted
"number_of_outlets_result_less", -- Number of outlets with lead results less than or equal to 15 parts per billion (ppb) (or 15 μg/L or 0.015 mg/L)
"number_of_outlets_result_greater", -- Number of outlets with lead result(s) greater than 15 ppb (or 15 μg/L or 0.015 mg/L)
"out_of_service", -- Indicates if all outlets with lead results greater than 15 ppb (or 15 μg/L or 0.015 mg/L) have been taken out of service. “Yes” = Outlets taken out of service/ “No” = Outlets not taken out of service or no outlets exceeded 15 ppb.
"results_complete", -- Indicates if all lead results, including post-remediation sampling results, for all samples collected have been received. “Yes” = Received/ “No” = Not received
"results_completion_date", -- Date all lead results were received, including post-remediation sampling results
"school_website", -- The website where the lead results are/were posted
"beds_code", -- Basic Education Data System (BEDS) code, the New York State Education Department identifier for NY schools
"school_city", -- School city
"date_sampling_updated", -- Date sampling survey was last updated by the school
"date_results_updated", -- Date results survey was last updated by the school
"county_location_address",
"location", -- Latitude and longitude
"county_location_zip",
"location_city",
"location_state"
FROM
"health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9:latest"."lead_testing_in_school_drinking_water_sampling_and"
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 health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9 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 health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9: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 health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9Checkout 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 health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9:latestThis will download all the objects for the latest tag of health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9 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 health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9: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 health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9: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, health-data-ny-gov/lead-testing-in-school-drinking-water-sampling-and-rkyy-fsv9 is just another Postgres schema.