Build a Data Lakehouse with DataBricks and the Strava API
<p>A data lakehouse is an advantageous way to store data as it utilizes modern principles of cloud architecture. This allows for decoupling of storage and compute along with the ability to handle both structured and unstructured data.</p>
<p>This blog will detail how to effectively build a lakehouse with data from the <a href="https://developers.strava.com/" rel="noopener ugc nofollow" target="_blank">Strava API</a> using <a href="https://azure.microsoft.com/en-us/products/databricks" rel="noopener ugc nofollow" target="_blank">Azure DataBricks</a> and its services. This exercise focuses on querying <em>new</em> activities logged with the Strava app.</p>
<p><strong>About DataBricks</strong>: A Data and AI company providing a data platform centered around Apache Spark and Delta Lake.<br />
This exercise uses Azure DataBricks, DataBricks File Storage (DBFS), PySpark Notebooks, and Delta Files.</p>
<p><a href="https://medium.com/@nickradich/build-a-data-lakehouse-with-databricks-and-the-strava-api-43f1e813cd5e"><strong>Read More</strong></a></p>