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 medicaid_beneficiaries_inpatient_admissions_and table in this repository, by referencing it like:
"health-data-ny-gov/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4:latest"."medicaid_beneficiaries_inpatient_admissions_and"or in a full query, like:
SELECT
":id", -- Socrata column ID
"all_er_visits", -- The total number of ER visits for all beneficiaries.
"dual_er_visits", -- The total number of ER visits for dual eligible beneficiaries.
"recip_admit", -- The unique number of beneficiaries with at least one inpatient admission.
"nondual_admits", -- The total number of admissions for non-dual eligible beneficiaries.
"nondual_recip_admit", -- The unique number of non-dual eligible beneficiaries with at least one inpatient admission.
"dual_admits", -- The total number of admissions for dual eligible beneficiaries.
"dual_recip_admit", -- The unique number of dual eligible beneficiaries with at least one inpatient admission.
"child_n", -- The total number of Medicaid beneficiaries between the ages of 0 and 17 years.
"nondual_n", -- The total number of Medicaid beneficiaries identified not dually eligible for both Medicaid and Medicare.
"dual_n", -- The total number of Medicaid beneficiaries identified as dually eligible for both Medicaid and Medicare
"total_n", -- The total number of Medicaid beneficiaries within the zip code during the calendar year. The number was calculated based upon the last monthly enrollment segment of the Medicaid beneficiary during the calendar year. For example, if a beneficiary moved from one zip code to another during the year, they are counted in their last (or most recent) zip code. Beneficiaries with unknown or out of state zip codes were excluded from counts.
"zip_code", -- The New York State Zip Code
"year", -- The calendar year the data represents.
"dual_er_recip", -- The unique number of dual eligible beneficiaries with at least one Emergency Room (ER) visit.
"total_admits", -- The total number of admissions for all beneficiaries.
"county", -- The county where the majority of the zip code resides, if the zip code crosses more than one county.
"nondual_er_recip", -- The unique number of non-dual eligible beneficiaries with at least one ER visit.
"adult_n", -- The total number of Medicaid beneficiaries age 18 years and older.
"nondual_er_visits", -- The total number of ER visits for non-dual eligible beneficiaries.
"all_er_recip" -- The unique number of beneficiaries with at least one ER visit.
FROM
"health-data-ny-gov/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4:latest"."medicaid_beneficiaries_inpatient_admissions_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/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4 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/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4: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/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4Checkout 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/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4:latestThis will download all the objects for the latest tag of health-data-ny-gov/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4 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/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4: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/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4: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/medicaid-beneficiaries-inpatient-admissions-and-m2wt-pje4 is just another Postgres schema.