This page provides you with instructions on how to extract data from Heroku and load it into Azure SQL Data Warehouse. (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 Heroku?
Heroku is a cloud platform that lets companies build, deploy, monitor, and scale apps.
What is Azure SQL Data Warehouse?
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 Heroku
You can extract the data you want from Heroku's servers using the Heroku API. A common use case for extracting Heroku data is retrieving server logs or other event logs. There are some API endpoints related to logs, as well as command-line tools like the logs command that let you retrieve this data.
Sample Heroku data
Here's an example set of commands and responses you might see when interacting with the
logs command-line tool.
$ heroku logs --ps router 2012-02-07T09:43:06.123456+00:00 heroku[router]: at=info method=GET path="/stylesheets/dev-center/library.css" host=devcenter.heroku.com fwd="188.8.131.52" dyno=web.5 connect=1ms service=18ms status=200 bytes=13 2012-02-07T09:43:06.123456+00:00 heroku[router]: at=info method=GET path="/articles/bundler" host=devcenter.heroku.com fwd="184.108.40.206" dyno=web.6 connect=1ms service=18ms status=200 bytes=20375 $ heroku logs --source app 2012-02-07T09:45:47.123456+00:00 app[web.1]: Rendered shared/_search.html.erb (1.0ms) 2012-02-07T09:45:47.123456+00:00 app[web.1]: Completed 200 OK in 83ms (Views: 48.7ms | ActiveRecord: 32.2ms) 2012-02-07T09:45:47.123456+00:00 app[worker.1]: [Worker(host:465cf64e-61c8-46d3-b480-362bfd4ecff9 pid:1)] 1 jobs processed at 23.0330 j/s, 0 failed ... 2012-02-07T09:46:01.123456+00:00 app[web.6]: Started GET "/articles/buildpacks" for 220.127.116.11 at 2012-02-07 09:46:01 +0000 $ heroku logs --source app --ps worker 2012-02-07T09:47:59.123456+00:00 app[worker.1]: [Worker(host:260cf64e-61c8-46d3-b480-362bfd4ecff9 pid:1)] Article#record_view_without_delay completed after 0.0221 2012-02-07T09:47:59.123456+00:00 app[worker.1]: [Worker(host:260cf64e-61c8-46d3-b480-362bfd4ecff9 pid:1)] 5 jobs processed at 31.6842 j/s, 0 failed ...
Preparing Heroku data
This part could be the trickiest: you need to map the data that comes out of each Heroku API endpoint or log extraction into a schema that can be inserted into your destination database. This means that, for each value in the response, you need to identify a predefined datatype (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them. Depending on your log files, you may also opt to break those up into raw logs and more meaningful metadata or log portions.
The Heroku API documentation can give you a good sense of what fields will be provided by each endpoint, along with their corresponding datatypes.
Loading data into Azure SQL Data Warehouse
SQL Data Warehouse 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 SQL Data Warehouse 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 Heroku 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.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Heroku.
And remember, as with any code, once you write it, you have to maintain it. If Heroku modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
Other data warehouse options
Azure SQL Data Warehouse 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 Heroku to Azure SQL Data Warehouse automatically. With just a few clicks, Stitch starts extracting your Heroku data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Azure SQL Data Warehouse data warehouse.