Heroku is a cloud platform that lets companies build, deploy, monitor, and scale apps.

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

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.

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="204.204.204.204" 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="204.204.204.204" 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 4.1.81.209 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 ...

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.

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

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.

Delta Lake on Databricks 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, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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.