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 healthy_people_2020_overview_of_health_disparities table in this repository, by referencing it like:
"cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm:latest"."healthy_people_2020_overview_of_health_disparities"or in a full query, like:
SELECT
":id", -- Socrata column ID
"population_group_with_the_1", -- Population group category name with the best rate for the corresponding population characteristic at the final data point in the disparities analysis.
"standard_error_of_best_rate_1", -- Standard error of the best population-group rate (most favorable, least adverse) for the corresponding population characteristic at the final data point in the disparities analysis. A blank cell indicates that the standard error was not available.
"best_rate_final_year_s", -- Value of the best population-group rate (most favorable, least adverse) for the corresponding population characteristic at the final data point in the disparities analysis.
"standard_error_of_rate_ratio", -- Standard error of the ratio comparing the best group rate with the average of the other group rates (if the number of population groups is three or more) for the baseline data point in the disparities analysis. If only two population groups are included for the characteristic, the standard error for the ratio of the best group rate to the rate of the other group is provided. A blank cell indicates that the standard error was not available.
"standard_error_of_best_rate", -- Standard error of the best population-group rate (most favorable, least adverse) for the corresponding population characteristic for the baseline data point in the disparities analysis. A blank cell indicates that the standard error was not available.
"topic_area", -- The HP2020 topic area corresponding to the objective included in the disparities analysis.
"objective_description",
"disparities_change_over_time", -- Status category indicating whether the disparity had a decrease, showed little or no detectable change, or had an increase between the baseline and final data point in the disparities analysis.
"number_of_population_groups", -- Number of population groups included in the disparities analysis for the corresponding population characteristic at the baseline and final data points.
"standard_error_of_average", -- Standard error of the average rate of all other groups (if the number of population groups is three or more) besides the group with the best rate for the corresponding population characteristic at the baseline data point in the disparities analysis. If only two population groups are included for the characteristic, this value is the standard error of the rate for the second group that does not have the best rate. A blank cell indicates that the standard error was not available.
"standard_error_of_average_1", -- Standard error of the average rate of all other groups (if the number of population groups is three or more) besides the group with the best rate for the corresponding population characteristic at the final data point in the disparities analysis. If only two population groups are included for the characteristic, this value is the standard error of the rate for the second group that does not have the best rate. A blank cell indicates that the standard error was not available.
"standard_error_of_rate_ratio_1", -- Standard error of the ratio comparing the best group rate with the average of the other group rates (if the number of population groups is three or more) for the final data point in the disparities analysis. If only two population groups are included for the characteristic, the standard error for the ratio of the best group rate to the rate of the other group is provided. A blank cell indicates that the standard error was not available.
"hp2020_objective", -- The analysis included 611 HP2020 objectives that met the criteria for the population characteristics in the disparities analysis (see Technical Notes).
"population_characteristic", -- Population characteristics included in the disparities analysis are sex, race and ethnicity, educational attainment, family income, disability status, and geographic location.
"best_rate_baseline_year_s", -- Value of the best population-group rate (most favorable, least adverse) for the corresponding population characteristic at the baseline data point in the disparities analysis.
"rate_ratio_baseline_year", -- Ratio of the best group rate to the average of the other group rates (if the number of population groups is three or more) for the baseline data point in the disparities analysis. If only two population groups are included for the characteristic, the ratio of the best group rate to the rate of the other group is provided. A blank cell indicates that the rate ratio could not be calculated.
"z_score_of_the_difference", -- z score corresponding to the difference in the rate ratio between the final and baseline data point in the disparities analysis. A blank cell indicates that the z score was not calculated.
"difference_in_the_rate_ratio", -- Absolute difference in the rate ratio between the final and baseline data point in the disparities analysis. A blank cell indicates that the difference in the rate ratio could not be calculated.
"rate_ratio_final_year_s", -- Ratio of the best group rate to the average of the other group rates (if the number of population groups is three or more) for the final data point in the disparities analysis. If only two population groups are included for the characteristic, the ratio of the best group rate to the rate of the other group is provided. A blank cell indicates that the rate ratio could not be calculated.
"average_of_other_rates_final", -- Average rate of all other groups (if the number of population groups is three or more) besides the group with the best rate for the corresponding population characteristic at the final data point in the disparities analysis. If only two population groups are included for the characteristic, this value is the rate for the second group that does not have the best rate.
"population_group_with_the", -- Population group with the best rate for the corresponding population characteristic at the baseline data point in the disparities analysis.
"average_of_other_rates" -- Average rate of all other groups (if the number of population groups is three or more) besides the group with the best rate for the corresponding population characteristic at the baseline data point in the disparities analysis. If only two population groups are included for the characteristic, this value is the rate for the second group that does not have the best rate.
FROM
"cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm:latest"."healthy_people_2020_overview_of_health_disparities"
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 cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm 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 cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm: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 cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddmCheckout 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 cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm:latestThis will download all the objects for the latest tag of cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm 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 cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm: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 cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm: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, cdc-gov/healthy-people-2020-overview-of-health-disparities-fdpm-fddm is just another Postgres schema.