This page provides you with instructions on how to extract data from Dark Sky and load it into PostgreSQL. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Dark Sky?
Dark Sky Company specializes in weather forecasting and visualization. Its software powers the company's own Dark Sky weather app, and its API provides current, historical, and forecast data to other apps.
What is PostgreSQL?
PostgreSQL, known by most simply as Postgres, is a hugely popular object-relational database management system (ORDBMS). It labels itself as "the world's most advanced open source database," and for good reason. The platform, despite being available for free via an open source license, offers enterprise-grade features including a strong emphasis on extensibility and standards compliance.
It runs on all major operating systems, including Linux, Unix, and Windows. It is fully ACID-compliant, has full support for foreign keys, joins, views, triggers, and stored procedures (in multiple languages). Postgres is often the best tool for the job as a back-end database for web systems and software tools, and cloud-based deployments are offered by most major cloud vendors. Its syntax also forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless and makes Postgres a good "first step" for developers who may later expand into Redshift's data warehouse platform.
Getting data out of Dark Sky
Dark Sky provides an API that lets developers retrieve data stored in the platform about temperature, precipitation, wind, and other meteorological conditions, using the format https://api.darksky.net/forecast/[key]/[latitude],[longitude],[time]?parameter=value1,value2. For example, to retrieve information about Boston weather, you could call GET https://api.darksky.net/forecast/key/42.3601,-71.0589.
Sample Dark Sky data
Here's an example of the kind of response you might see with a query like the one above.
{
"latitude": 42.3601,
"longitude": -71.0589,
"timezone": "America/New_York",
"currently": {
"time": 1509993277,
"summary": "Drizzle",
"icon": "rain",
"nearestStormDistance": 0,
"precipIntensity": 0.0089,
"precipIntensityError": 0.0046,
"precipProbability": 0.9,
"precipType": "rain",
"temperature": 66.1,
"apparentTemperature": 66.31,
"dewPoint": 60.77,
"humidity": 0.83,
"pressure": 1010.34,
"windSpeed": 5.59,
"windGust": 12.03,
"windBearing": 246,
"cloudCover": 0.7,
"uvIndex": 1,
"visibility": 9.84,
"ozone": 267.44
},
"minutely": {
"summary": "Light rain stopping in 13 min., starting again 30 min. later.",
"icon": "rain",
"data": [{
"time": 1509993240,
"precipIntensity": 0.007,
"precipIntensityError": 0.004,
"precipProbability": 0.84,
"precipType": "rain"
},
...
]
},
"hourly": {
"summary": "Rain starting later this afternoon, continuing until this evening.",
"icon": "rain",
"data": [{
"time": 1509991200,
"summary": "Mostly Cloudy",
"icon": "partly-cloudy-day",
"precipIntensity": 0.0007,
"precipProbability": 0.1,
"precipType": "rain",
"temperature": 65.76,
"apparentTemperature": 66.01,
"dewPoint": 60.99,
"humidity": 0.85,
"pressure": 1010.57,
"windSpeed": 4.23,
"windGust": 9.52,
"windBearing": 230,
"cloudCover": 0.62,
"uvIndex": 1,
"visibility": 9.32,
"ozone": 268.95
},
...
]
},
"daily": {
"summary": "Mixed precipitation throughout the week, with temperatures falling to 39°F on Saturday.",
"icon": "rain",
"data": [{
"time": 1509944400,
"summary": "Rain starting in the afternoon, continuing until evening.",
"icon": "rain",
"sunriseTime": 1509967519,
"sunsetTime": 1510003982,
"moonPhase": 0.59,
"precipIntensity": 0.0088,
"precipIntensityMax": 0.0725,
"precipIntensityMaxTime": 1510002000,
"precipProbability": 0.73,
"precipType": "rain",
"temperatureHigh": 66.35,
"temperatureHighTime": 1509994800,
"temperatureLow": 41.28,
"temperatureLowTime": 1510056000,
"apparentTemperatureHigh": 66.53,
"apparentTemperatureHighTime": 1509994800,
"apparentTemperatureLow": 35.74,
"apparentTemperatureLowTime": 1510056000,
"dewPoint": 57.66,
"humidity": 0.86,
"pressure": 1012.93,
"windSpeed": 3.22,
"windGust": 26.32,
"windGustTime": 1510023600,
"windBearing": 270,
"cloudCover": 0.8,
"uvIndex": 2,
"uvIndexTime": 1509987600,
"visibility": 10,
"ozone": 269.45,
"temperatureMin": 52.08,
"temperatureMinTime": 1510027200,
"temperatureMax": 66.35,
"temperatureMaxTime": 1509994800,
"apparentTemperatureMin": 52.08,
"apparentTemperatureMinTime": 1510027200,
"apparentTemperatureMax": 66.53,
"apparentTemperatureMaxTime": 1509994800
},
...
]
},
"alerts": [
{
"title": "Flood Watch for Mason, WA",
"time": 1509993360,
"expires": 1510036680,
"description": "...FLOOD WATCH REMAINS IN EFFECT THROUGH LATE MONDAY NIGHT...\nTHE FLOOD WATCH CONTINUES FOR\n* A PORTION OF NORTHWEST WASHINGTON...INCLUDING THE FOLLOWING\nCOUNTY...MASON.\n* THROUGH LATE FRIDAY NIGHT\n* A STRONG WARM FRONT WILL BRING HEAVY RAIN TO THE OLYMPICS\nTONIGHT THROUGH THURSDAY NIGHT. THE HEAVY RAIN WILL PUSH THE\nSKOKOMISH RIVER ABOVE FLOOD STAGE TODAY...AND MAJOR FLOODING IS\nPOSSIBLE.\n* A FLOOD WARNING IS IN EFFECT FOR THE SKOKOMISH RIVER. THE FLOOD\nWATCH REMAINS IN EFFECT FOR MASON COUNTY FOR THE POSSIBILITY OF\nAREAL FLOODING ASSOCIATED WITH A MAJOR FLOOD.\n",
"uri": "http://alerts.weather.gov/cap/wwacapget.php?x=WA1255E4DB8494.FloodWatch.1255E4DCE35CWA.SEWFFASEW.38e78ec64613478bb70fc6ed9c87f6e6"
},
...
],
{
"flags": {
"units": "us",
...
}
}
Preparing Dark Sky data
If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Dark Sky's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.
Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
Loading data into Postgres
Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.
For simple, day-to-day data insertion, running INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.
For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.
The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.
Keeping Dark Sky data up to date
At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Dark Sky's API results include fields like time that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.
Other data warehouse options
PostgreSQL is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to move data from Dark Sky to PostgreSQL automatically. With just a few clicks, Stitch starts extracting your Dark Sky data, structuring it in a way that's optimized for analysis, and inserting that data into your PostgreSQL data warehouse.