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 employment_first_annual_ovr_outcomes_current table in this repository, by referencing it like:
"pa-gov/employment-first-annual-ovr-outcomes-current-uimv-hpcj:latest"."employment_first_annual_ovr_outcomes_current"or in a full query, like:
SELECT
":id", -- Socrata column ID
"percent_of_individuals", -- Individuals placed in Competitive Employment divided by the total placements in all employment types.
"estimated_total_government", -- Estimated federal, state, and local taxes paid plus annualized public support dollars at closure.
"average_hourly_wage_of", -- PA average for all occupations (2019): $24.686 RSA standard: "VR consumers served by general/combined agencies who achieved competitive outcomes are earning, on the average, at least 52 cents for every dollar earned hourly by all employed individuals in the state." OVR Program Year 2019 ratio: 57 cents for every dollar earned hourly by all employed individuals in the state (calculated as $14.11/$24.68), exceeding the RSA standard by 5 cents, or 9.94.%. OVR Program Year 2019 Average Hourly wages: From 'PY2019 26 Closures for Highlight' sheet - 1 Tab, column 'K' (Average of DE359 from Q4 PY2019 RSA911 for cases closed DE354 as "6" and DE353 within PY2019 was $14.30)
"average_time_in_months_from",
"individuals_employed_in_the", -- Employment in Competitive Placement means employment at or above minimum wage in settings where most employees do not have disabilities. (1)
"cases_open_00_39_at_end_of", -- Ad hoc Case History Report *PY2019 include a period when the Order of Selection was Closed
"cases_closed_during_year", -- RSA-911 closures; RSA-911 DE354, excludes -1 to -2 and 00 to 08 closures
"individuals_engaged_with", -- FFY 2015-16: 51,267 (RPT019, Open 00-39, 10/1-9/30) + 21,208 (RSA-113 Year-end Report, Line D8) PY 2016-17: 49,636 (RPT019, Open 00-39 as of 6/30/2017) + 24,958 (All closures during the Program Year) PY 2017-18: 50,394 cases open 00-39 as of 6/30/2018 (ad hoc) + 21,940 closures during the Program Year (RSA-911; excludes -1--2 and 00-08 closures) PY 2018-19: 47,425 cases open 00-39 as of 6/30/2019 (ad hoc) + 21,996 closures during the Program Year (RSA-911, validated in ad hoc, excludes -1 to -2 and 00 to 08 closures). PY 2019-20: 36,014 cases open 00-39 as of 6/30/2020 (ad hoc) + 18,535 closures during the Program Year (RSA-911 DE354, excludes -1 to -2 and 00 to 08 closures). *PY2019 include a period when the Order of Selection was Closed
"applicants_found_eligible", -- Old Status Code 10 between 7/1 -6/30; distinct PIDS (Case History Report PY2019)
"new_applicants_for_services", -- Old Status Code 02 between 7/1 -6/30; distinct PIDS (Case History Report PY2019)
"period", -- Identifies the Federal Fiscal Year or State Program Year for the provided statistics.
"estimated_federal_state_and", -- Based on a standard deduction for the year, annual tax brackets and rates established by the IRS, and flat-rate FICA, state, and local taxes.
"average_per_person_cost_of", -- Average individual “life of case” cost for all persons having a competitive employment outcome regardless of total number of years receiving services.
"average_per_person_cost_for", -- Average individual “life of case” cost for all persons having a competitive employment outcome regardless of total number of years receiving services.
"projected_time_in_months", -- Time to recover investment in months.
"individuals_placed_into", -- Adhoc History Report (RSA-911 DE354, Type of Exit code 6=6932)
"percentage_of_applicants" -- Applicants Found Eligible divided by New Applicants
FROM
"pa-gov/employment-first-annual-ovr-outcomes-current-uimv-hpcj:latest"."employment_first_annual_ovr_outcomes_current"
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/employment-first-annual-ovr-outcomes-current-uimv-hpcj 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/employment-first-annual-ovr-outcomes-current-uimv-hpcj: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/employment-first-annual-ovr-outcomes-current-uimv-hpcjCheckout 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/employment-first-annual-ovr-outcomes-current-uimv-hpcj:latestThis will download all the objects for the latest tag of pa-gov/employment-first-annual-ovr-outcomes-current-uimv-hpcj 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/employment-first-annual-ovr-outcomes-current-uimv-hpcj: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/employment-first-annual-ovr-outcomes-current-uimv-hpcj: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/employment-first-annual-ovr-outcomes-current-uimv-hpcj is just another Postgres schema.