Dark Sky to Azure Synapse

This page provides you with instructions on how to extract data from Dark Sky and load it into Azure Synapse. (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 Azure Synapse?

Azure Synapse (formerly Azure SQL Data Warehouse) is a cloud-based petabyte-scale columnar database service with controls to manage compute and storage resources independently. It offers encryption of data at rest and dynamic data masking to mask sensitive data on the fly, and it integrates with Azure Active Directory. It can replicate to read-only databases in different geographic regions for load balancing and fault tolerance.

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 Azure Synapse

Azure Synapse provides a multi-step process for loading data. After extracting the data from its source, you can move it to Azure Blob storage or Azure Data Lake Store. You can then use one of three utilities to load the data:

  • AZCopy uses the public internet.
  • Azure ExpressRoute routes the data through a dedicated private connection to Azure, bypassing the public internet by using a VPN or point-to-point Ethernet network.
  • The Azure Data Factory (ADF) cloud service has a gateway that you can install on your local server, then use to create a pipeline to move data to Azure Storage.

From Azure Storage you can load the data into Azure Synapse staging tables by using Microsoft's PolyBase technology. You can run any transformations you need while the data is in staging, then insert it into production tables. Microsoft offers documentation for the whole process.

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

Azure Synapse 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, PostgreSQL, Snowflake, or Panoply, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Panoply, and To S3.

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 Azure Synapse automatically. With just a few clicks, Stitch starts extracting your Dark Sky data via the API, structuring it in a way that's optimized for analysis, and inserting that data into your Azure Synapse data warehouse.