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 maternal_sepsis_by_select_risk_factors_sparcs table in this repository, by referencing it like:
"health-data-ny-gov/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y:latest"."maternal_sepsis_by_select_risk_factors_sparcs"or in a full query, like:
SELECT
":id", -- Socrata column ID
"any_sepsis_incidence_per", -- The rate of maternal sepsis events (including septicemia as well as severe sepsis/septic shock) identified via diagnosis coding during the specified maternal window and risk factor strata per 100,000 eligible live births.
"risk_factor_strata", -- The strata of the risk factor characteristic represented by the data within the row.
"live_births_n", -- The number of eligible live births events identified within the specified risk factorstrata.
"severe_sepsis_incidence_n", -- The number of maternal severe sepsis/septic shock events identified via diagnosis coding during the specified maternal window among eligible live births within the specified risk factor strata.
"any_sepsis_p_value", -- The p-value from the logistic regression model predicting any sepsis (including septicemia as well assevere sepsis/septic shock) during the specified maternal window for the specified risk factor strata. Values below .05 indicate a statistically significant association.
"maternal_window", -- The period in which sepsis was identified: pregnancy, delivery, or postpartum (within 42 days after delivery).
"any_sepsis_incidence_n", -- The number of maternal sepsis events (including septicemia as well as severe sepsis/septic shock) identified via diagnosis coding during the specified maternal window among eligible live births within the specified risk factorstrata.
"data_source", -- The data source used to identify the risk factor represented by the data within the row: SPARCS (defined using ICD10 diagnoses) and/or birth certificate
"severe_sepsis_crude_odds", -- The unadjusted odds ratio from a logistic regression model predicting severe sepsis/septic shock during the specified maternal window for the specified risk factor strata relative to the reference group, followed by the 95% confidence interval (lower bound-upper bound) for the odds ratio. The reference group for each risk factoris noted as “ref”.
"any_sepsis_crude_odds_ratio", -- The unadjusted odds ratio from a logistic regression model predicting any sepsis (including septicemia as well as severe sepsis/septic shock) during the specified maternal window for the specified risk factor strata relative to the reference group, followed by the 95% confidence interval (lower bound-upper bound) for the odds ratio. The reference group for each risk factor is noted as “ref”.
"live_births", -- The percentage of eligible live births events within the specified risk factor strata.
"severe_sepsis_incidence_per", -- The rate of maternal severe sepsis/septic shock events identified via diagnosis coding at any point within the specified maternal window and risk factor strata per 100,000 eligible live births.
"severe_sepsis_p_value", -- The p-value from the logistic regression model predicting severe sepsis/septic shock during the specified maternal window for the specified risk factor strata. Values below .05 indicate a statistically significant association.
"risk_factor", -- The specific risk factor characteristic represented by the data within the row.
"year_s_of_live_birth", -- The years during which the live births represented in the data occurred.
"risk_factor_type" -- The general type of risk factor represented by the data within the row and the coding definition used, where applicable (e.g., Bateman1 comorbidities, Elixhauser2 comorbidities).
FROM
"health-data-ny-gov/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y:latest"."maternal_sepsis_by_select_risk_factors_sparcs"
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/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y 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/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y: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/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62yCheckout 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/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y:latestThis will download all the objects for the latest tag of health-data-ny-gov/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y 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/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y: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/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y: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/maternal-sepsis-by-select-risk-factors-sparcs-p9ay-x62y is just another Postgres schema.