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 retail_and_bulk_energy_storage_incentive_programs table in this repository, by referencing it like:
"ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy:latest"."retail_and_bulk_energy_storage_incentive_programs"or in a full query, like:
SELECT
":id", -- Socrata column ID
"project_name", -- Description of project
"city", -- Name of city for project location
"address", -- Name of street for project location. Blank cells represent data that were not required or are not currently available
"nyiso_zone", -- Name of New York Independent System Operator load zone for project location
"reporting_period", -- The time period covered by the data set
"state", -- New York State
"electric_utility", -- Name of electric utility service provider for project location
"date_project_approved", -- Date project application was approved by the program. Dates before April 2019 reflect the original approval date for already approved NY-Sun projects that subsequently added energy storage during or after April 2019
"storage_technology_type", -- Defines the type of energy storage system. For battery storage projects, the battery chemistry type is shown
"project_status", -- Either Completed or Approved. Complete indicates projects that are interconnected and operational, and closed out the project application. Approved indicates projects in the pipeline with an active application that are not yet completed. Pipeline projects are subject to change
"application_number", -- Unique identifier for project
"paired_with_solar", -- Indicates whether the energy storage system is interconnected with and charged by a paired solar electric PV system; either Yes or No
"contractor", -- Name of entity responsible for installation of the project
"battery_cell_manufacturer", -- The manufacturer of the battery cells. For thermal or other forms of energy storage, the manufacturer of the hardware. Blank cells represent data that were not required or are not currently available
"date_completed", -- Date NYSERDA recognized the project as interconnected and operational, and closed out the project application. Blank cells represent pipeline data for projects not yet completed
"energy_storage_power_capacity", -- Rating of the electric power that can be delivered from an energy storage project in alternating current (AC) power
"nyserda_funding_amount", -- Amount of project incentives approved or paid by NYSERDA in USD
"installed_energy_storage", -- Usable installed energy storage capacity measured in alternating current (AC) power. It is equal to the total capacity measured during a complete discharge from a 100% usable state of charge, performed in accordance with the storage manufacturer’s specifications, on the commercial operation date
"metering_method", -- How the energy storage system was interconnected; either Community DG, Remote net metering, Bulk, or On-site metering. On-site metering is with customer load, and Community DG, Remote net metering, and Bulk are not. Bulk projects provide wholesale market services
"program_type", -- Name of program type; either Retail or Bulk
"zip_code", -- ZIP code for project location. Blank cells represent data that were not required or are not currently available
"county" -- Name of county for project location
FROM
"ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy:latest"."retail_and_bulk_energy_storage_incentive_programs"
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 ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy 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 ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy: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 ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpyCheckout 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 ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy:latestThis will download all the objects for the latest tag of ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy 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 ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy: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 ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy: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, ny-gov/retail-and-bulk-energy-storage-incentive-programs-ugya-enpy is just another Postgres schema.