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_archived_05312023_parcel_universe table in this repository, by referencing it like:
"datacatalog-cookcountyil-gov/assessor-archived-05312023-parcel-universe-tx2p-k2g9:latest"."assessor_archived_05312023_parcel_universe"or in a full query, like:
SELECT
":id", -- Socrata column ID
"nearest_golf_course_id", -- Nearest golf course ID
"nearest_golf_course_dist_ft", -- Nearest golf course distance (feet)
"nearest_golf_course_data_year", -- Nearest golf course data year
"num_pin_in_half_mile", -- Number of PINs in half mile
"num_foreclosure_per_1000_pin_past_5_years", -- Number of foreclosures per 1000 PINs (past 5 years), sourced from Illinois Public Records, reported on a long lag
"num_foreclosure_data_year", -- Number of foreclosures data year
"nearest_railroad_id", -- Nearest railroad ID
"nearest_park_osm_id", -- Nearest park OSM ID
"nearest_neighbor_1_pin10", -- Nearest neighbor 1 (PIN10)
"nearest_neighbor_1_dist_ft", -- Nearest neighbor 1 distance (feet)
"nearest_metra_stop_data_year", -- Nearest Metra stop data year
"nearest_metra_route_data", -- Nearest Metra route data year
"nearest_major_road_data_year", -- Nearest major road data year
"nearest_hospital_gnis_code", -- Nearest hospital GNIS code
"nearest_hospital_data_year", -- Nearest hospital data year
"nearest_cta_stop_id", -- Nearest CTA stop ID
"nearest_cta_route_dist_ft", -- Nearest CTA route distance (feet)
"nearest_cta_route_data_year", -- Nearest CTA route data year
"nearest_cemetery_name", -- Nearest cemetery name
"nearest_bike_trail_dist_ft", -- Nearest bike trail distance (feet)
"lake_michigan_dist_ft", -- Lake Michigan distance (feet)
"lake_michigan_data_year", -- Lake Michigan data year
"nearest_neighbor_3_dist_ft", -- Nearest neighbor 3 distance (feet)
"nearest_neighbor_3_pin10", -- Nearest neighbor 3 (PIN10)
"nearest_neighbor_2_dist_ft", -- Nearest neighbor 2 distance (feet)
"nearest_neighbor_2_pin10", -- Nearest neighbor 2 (PIN10)
"nearest_water_data_year", -- Nearest water data year
"nearest_water_dist_ft", -- Nearest water distance (feet)
"nearest_water_name", -- Nearest water name
"nearest_water_id", -- Nearest water ID
"nearest_railroad_data_year", -- Nearest railroad data year
"nearest_railroad_dist_ft", -- Nearest railroad distance (feet)
"nearest_railroad_name", -- Nearest railroad name
"nearest_park_data_year", -- Nearest park data year
"nearest_park_dist_ft", -- Nearest park distance (feet)
"nearest_park_name", -- Nearest park name
"nearest_metra_stop_dist_ft", -- Nearest Metra stop distance (feet)
"nearest_metra_stop_name", -- Nearest Metra stop name
"nearest_metra_stop_id", -- Nearest Metra stop ID
"nearest_metra_route_dist_ft", -- Nearest Metra route distance (feet)
"nearest_metra_route_name", -- Nearest Metra route name
"nearest_metra_route_id", -- Nearest Metra route ID
"nearest_major_road_dist_ft", -- Nearest major road distance (feet)
"nearest_major_road_name", -- Nearest major road name
"nearest_major_road_osm_id", -- Nearest major road OSM ID
"nearest_hospital_dist_ft", -- Nearest hospital distance (feet)
"nearest_hospital_name", -- Nearest hospital name
"nearest_cta_stop_data_year", -- Nearest CTA stop data year
"nearest_cta_stop_dist_ft", -- Nearest CTA stop distance (feet)
"nearest_cta_stop_name", -- Nearest CTA stop name
"nearest_cta_route_name", -- Nearest CTA route name
"nearest_cta_route_id", -- Nearest CTA route ID
"nearest_cemetery_data_year", -- Nearest cemetery data year
"nearest_cemetery_dist_ft", -- Nearest cemetery distance (feet)
"nearest_cemetery_gnis_code", -- Nearest cemetery GNIS code
"nearest_bike_trail_data_year", -- Nearest bike trail data year
"nearest_bike_trail_name", -- Nearest bike trail name
"nearest_bike_trail_id", -- Nearest bike trail ID
"num_school_data_year", -- Number of schools data year
"num_school_in_half_mile", -- Number of schools in half mile, sourced from GreatSchools
"num_foreclosure_in_half_mile_past_5_years", -- Number of foreclosures in half mile (past 5 years), sourced from Illinois Public Records, reported on a long lag
"num_bus_stop_data_year", -- Number of bus stops data year
"num_bus_stop_in_half_mile", -- Number of bus stops in half mile, including CTA and PACE
"misc_subdivision_data_year", -- Subdivision data year
"misc_subdivision_id", -- Subdivision ID
"access_cmap_walk_data_year", -- CMAP walkability data year
"access_cmap_walk_total_score", -- CMAP walkability total score. From CMAP's ON TO 2050 spatial data files
"access_cmap_walk_nta_score", -- CMAP walkability score (no transit). From CMAP's ON TO 2050 spatial data files
"access_cmap_walk_id", -- CMAP walkability grid ID. From CMAP's ON TO 2050 spatial data files
"tax_tif_district_data_year", -- TIF district data year
"tax_tif_district_name", -- TIF district name
"tax_tif_district_num", -- TIF district number
"tax_special_service_area_data_year", -- Special Service Area data year
"tax_special_service_area_name", -- Special Service Area name
"tax_special_service_area_num", -- Special Service Area number
"tax_sanitation_district_data_year", -- Sanitation district data year
"tax_sanitation_district_name", -- Sanitation district name
"tax_sanitation_district_num", -- Sanitation district number
"tax_park_district_data_year", -- Park district data year
"tax_park_district_name", -- Park district name
"tax_park_district_num", -- Park district number
"tax_library_district_data_year", -- Library district data year
"tax_library_district_name", -- Library district name
"tax_library_district_num", -- Library district number
"tax_fire_protection_district_data_year", -- Fire protection district data year
"tax_fire_protection_district_name", -- Fire protection district name
"tax_fire_protection_district_num", -- Fire protection district number
"tax_community_college_district_data_year", -- Community College district data year
"tax_community_college_district_name", -- Community college district name
"tax_community_college_district", -- Community college district number
"school_data_year", -- School data year
"school_school_year", -- School year
"school_unified_district_name", -- School district (unified) name. Chicago Public Schools are associated with attendance areas where suburban schools are associated with districts.
"school_unified_district_geoid", -- School district (unified) GEOID. Chicago Public Schools are associated with attendance areas where suburban schools are associated with districts.
"school_secondary_district_name", -- School district (secondary) name. Chicago Public Schools are associated with attendance areas where suburban schools are associated with districts.
"school_secondary_district_geoid", -- School district (secondary) GEOID. Chicago Public Schools are associated with attendance areas where suburban schools are associated with districts.
"school_elementary_district_name", -- School district (elementary) name. Chicago Public Schools are associated with attendance areas where suburban schools are associated with districts.
"school_elementary_district_geoid", -- School district (elementary) GEOID. Chicago Public Schools are associated with attendance areas where suburban schools are associated with districts.
"env_airport_noise_data_year", -- Airport continuous noise surface estimated data year
"env_airport_noise_dnl", -- Airport continuous noise surface estimated DNL
"env_ohare_noise_contour_data_year", -- O'Hare noise contour data year. The "omp" value corresponds to the projected noise contour upon completion of the O'Hare Modernization Project
"env_ohare_noise_contour_half_mile_buffer_bool", -- O'Hare noise contour indicator (1/2 mile buffer). Indicates whether or not a parcel's centroid is within O'Hare's 65 DNL noise contour, buffered by 1/2 mile
"env_ohare_noise_contour_no_buffer_bool", -- O'Hare noise contour indicator (no buffer). Indicates whether or not a parcel's centroid is within O'Hare's 65 DNL noise contour
"env_flood_fs_data_year", -- First Street data year
"env_flood_fs_risk_direction", -- First Street flood risk direction
"env_flood_fs_factor", -- First Street Flood Factor
"env_flood_fema_data_year", -- FEMA Special Flood Hazard Area (SFHA) data year
"env_flood_fema_sfha", -- FEMA Special Flood Hazard Area (SFHA) indicator
"econ_qualified_opportunity_zone_data_year", -- Qualified Opportunity Zone data year
"econ_qualified_opportunity_zone_num", -- Qualified Opportunity Zone number
"econ_industrial_growth_zone_data_year", -- Industrial Growth Zone data year
"econ_industrial_growth_zone_num", -- Industrial Growth Zone number
"econ_enterprise_zone_data_year", -- Enterprise Zone data year
"econ_enterprise_zone_num", -- Enterprise Zone number
"econ_coordinated_care_area_data_year", -- Coordinated Care Area data year
"econ_coordinated_care_area_num", -- Coordinated Care Area number
"chicago_police_district_data_year", -- Chicago police district data year
"chicago_police_district_num", -- Chicago police district number
"chicago_industrial_corridor_data_year", -- Chicago industrial corridor data year
"chicago_industrial_corridor_name", -- Chicago industrial corridor name
"chicago_industrial_corridor_num", -- Chicago industrial corridor number
"chicago_community_area_data_year", -- Chicago community area data year
"chicago_community_area_name", -- Chicago community area name
"chicago_community_area_num", -- Chicago community area number
"ward_data_year", -- Ward data year
"ward_num", -- Ward number
"cook_municipality_data_year", -- Municipality data year
"cook_municipality_name", -- Municipality name
"cook_municipality_num", -- Municipality number
"cook_judicial_district_data_year", -- Judicial district data year
"cook_judicial_district_num", -- Judicial district number
"cook_commissioner_district_data_year", -- Commissioner district data year
"cook_commissioner_district_num", -- Commissioner district number
"cook_board_of_review_district_data_year", -- Board of Review district data year
"cook_board_of_review_district_num", -- Board of Review district number
"census_acs5_data_year", -- Census ACS5 data year
"census_acs5_tract_geoid", -- Census ACS5 tract GEOID
"census_acs5_state_senate_geoid", -- Census ACS5 state senate GEOID
"census_acs5_state_representative_geoid", -- Census ACS5 state representative GEOID
"census_acs5_school_district_unified_geoid", -- Census ACS5 school district (unified) GEOID
"census_acs5_school_district_secondary_geoid", -- Census ACS5 school district (secondary) GEOID
"census_acs5_school_district_elementary_geoid", -- Census ACS5 school district (elementary) GEOID
"census_acs5_puma_geoid", -- Census ACS5 PUMA GEOID
"census_acs5_place_geoid", -- Census ACS5 place GEOID
"census_acs5_county_subdivision_geoid", -- Census ACS5 county subdivision GEOID
"census_acs5_congressional_district_geoid", -- Census ACS5 congressional district GEOID
"census_data_year", -- Census data year
"census_zcta_geoid", -- Census ZCTA GEOID
"census_tract_geoid", -- Census tract GEOID
"census_state_senate_geoid", -- Census state senate GEOID
"census_state_representative_geoid", -- Census state representative GEOID
"census_school_district_unified_geoid", -- Census school district (unified) GEOID
"census_school_district_secondary_geoid", -- Census school district (secondary) GEOID
"census_school_district_elementary_geoid", -- Census school district (elementary) GEOID
"census_puma_geoid", -- Census PUMA GEOID
"census_place_geoid", -- Census place GEOID
"census_county_subdivision_geoid", -- Census county subdivision GEOID
"census_congressional_district_geoid", -- Census congressional district GEOID
"census_block_geoid", -- Census block GEOID
"census_block_group_geoid", -- Census block group GEOID
"mail_address_zipcode_1", -- Taxpayer mailing zip code
"mail_address_state", -- Taxpayer mailing state
"mail_address_city_name", -- Taxpayer mailing city
"mail_address_full", -- Taxpayer mailing street address
"mail_address_name", -- Taxpayer mailing name
"prop_address_zipcode_1", -- Property zip code
"prop_address_state", -- Property state
"prop_address_city_name", -- Property city
"prop_address_full", -- Property street address
"y_3435", -- Parcel centroid Y coordinate (CRS 3435)
"x_3435", -- Parcel centroid X coordinate (CRS 3435)
"lat", -- Parcel centroid latitude
"lon", -- Parcel centroid longitude
"tieback_proration_rate", -- Tieback proration rate. Prorated properties (whose value is split across multiple PINs) pay taxes on the proportion of value on their PIN. In other words, assessed value is multiplied by proration rate to determine taxable assessed value
"tieback_key_pin", -- Tieback key PIN. Prorated properties (whose value is split across multiple PINs) have a "main" or key PIN
"tax_code", -- Tax district code, as seen on individual property tax bills (Not currently up-to-date)
"nbhd_code", -- Assessor neighborhood code, first two digits are township, last three are neighborhood
"township_code", -- Township code
"township_name", -- Township name
"triad_code", -- Triad code. Reassessment of property in Cook County is done within a triennial cycle, meaning it occurs every three years. The Cook County Assessor's Office alternates reassessments between triads: the north and west suburbs, the south and west suburbs and the City of Chicago.
"triad_name", -- Triad name. Reassessment of property in Cook County is done within a triennial cycle, meaning it occurs every three years. The Cook County Assessor's Office alternates reassessments between triads: the north and west suburbs, the south and west suburbs and the City of Chicago.
"class", -- Property class
"year", -- Tax year
"pin10", -- Parcel Identification Number (10-digit)
"pin" -- Parcel Identification Number (PIN)
FROM
"datacatalog-cookcountyil-gov/assessor-archived-05312023-parcel-universe-tx2p-k2g9:latest"."assessor_archived_05312023_parcel_universe"
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-archived-05312023-parcel-universe-tx2p-k2g9 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-archived-05312023-parcel-universe-tx2p-k2g9: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-archived-05312023-parcel-universe-tx2p-k2g9Checkout 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-archived-05312023-parcel-universe-tx2p-k2g9:latestThis will download all the objects for the latest tag of datacatalog-cookcountyil-gov/assessor-archived-05312023-parcel-universe-tx2p-k2g9 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-archived-05312023-parcel-universe-tx2p-k2g9: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-archived-05312023-parcel-universe-tx2p-k2g9: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-archived-05312023-parcel-universe-tx2p-k2g9 is just another Postgres schema.