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 assessor_commercial_valuation_data table in this repository, by referencing it like:
"datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws:latest"."assessor_commercial_valuation_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"revpar", -- Revenue per available room (hotel specific)
"salecompmarketvalue_sf", -- Value per square foot derived from comparables approach
"year", -- Year
"tot_units", -- Total number of units (can include hotel rooms, nursing home rooms, residential apartments, commercial units etc.)
"carwash", -- Car wash at location (gas station/convenience store specific) - A = "Yes", B = "No"
"cost_day_bed", -- Cost per day per bed (nursing home specific)
"covidadjvacancy", -- Covid adjusted vacancy
"ebitda", -- Earnings before interest taxes depreciation and amortization %
"f_r", -- Fast food operation / rental space (gas station/convenience store specific)
"investmentrating", -- Invesment rating
"oiltankvalue_atypicaloby", -- Oil tank value / atypical outbuilding
"parking", -- Number of parking spaces
"pctownerinterest", -- Percent owner interest (condominium specific)
"roomrev", -- Percentage of revenue from room rentals (hotel specific)
"sap", -- In affordable housing special assessment program
"sapdeduction", -- Reduction from affordable housing special assessment program
"saptier", -- Tier of affordable housing special assessment program
"totalexp", -- Total expenses as a value
"excesslandval", -- Value of excess land area
"idphlicense", -- Illinois department of health license number
"parkingsf", -- Parking square footage
"totalrev", -- Total revenue
"sheet", -- Sheet from associated excel workbook that contains row on assessor's website
"ceilingheight", -- Ceiling height
"total2020revreported", -- Total 2020 revenue reported
"costapproach_sf", -- Cost approach value per square foot
"taxdist", -- Taxing district
"stories", -- Number of stories
"land_bldg", -- Land square footage to building square footage ratio for key PIN and all associated PINs
"nbhd", -- Assessor neighborhood number
"property_name_description", -- Property name/description
"owner", -- Owner name
"noi", -- Net operating income
"incomemarketvalue_sf", -- Value per square foot derived from income approach
"netrentablesf", -- Net rentable square footage
"pins", -- All PINs associated with primary PIN that should be valued collectively
"_2brunits", -- Number of 2 bedroom apartments
"incomemarketvalue", -- Value derived from income approach
"egi", -- Effective gross income
"_3brunits", -- Number of 3 bedroom apartments
"bldgsf", -- Building square footage for key PIN and all associated PINs
"_4brunits", -- Number of 4 bedroom apartments
"address", -- Address for keypin
"adj_rent_sf", -- Adjusted rent per square foot
"aprx_comm_sf", -- Approximate commercial square footage for key PIN and all associated PINs
"avgdailyrate", -- Average daily rate (hotel specific)
"apt", -- Total number of apartments
"_1brunits", -- Number of 1 bedroom apartments
"caprate", -- Capitalization rate
"studiounits", -- Number of studio apartments
"class_es", -- Class of key PIN or classes of key PIN and some or all associated PINs
"category", -- Hotel category (hotel specific)
"township", -- Township name
"excesslandarea", -- Excess land area (land area above 4:1 land to building)
"exp", -- Expenses
"finalmarketvalue", -- Final market value
"finalmarketvalue_bed", -- Final market value per bed (nursing home specific)
"landsf", -- Land square footage for key PIN and all associated PINs
"pgi", -- Potential gross income
"property_type_use", -- Property type/use
"reportedoccupancy", -- Reported occupancy
"revenuebed_day", -- Revenue per bed per day (nursing home specific)
"taxpayer", -- Owner
"finalmarketvalue_key", -- Final market value per key (529 class specific)
"finalmarketvalue_sf", -- Final market value per square foot
"keypin", -- Parcel identification number (PIN). Primary PIN used for valuing properties associated with multiple PINs.
"_2023permit_partial_demovalue", -- Partial value due to uncompleted new construction or demolition
"yearbuilt", -- Year built
"finalmarketvalue_unit", -- Final market value per unit (multifamily specific)
"vacancy", -- Vacancy
"total2019revreported" -- Total 2019 revenue reported
FROM
"datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws:latest"."assessor_commercial_valuation_data"
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 datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws 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 datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws: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 datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bswsCheckout 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 datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws:latestThis will download all the objects for the latest tag of datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws 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 datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws: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 datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws: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, datacatalog-cookcountyil-gov/assessor-commercial-valuation-data-csik-bsws is just another Postgres schema.