The perfect data pipeline doesn’t exist: Databricks

<p>This is a multi-part article series where I dive into the ideal stack you&rsquo;d use for a data engineering pipeline given constraints around what software providers to use. I aim to provide some indications of cost, ease of use, and functional limitations / cool features.</p> <p>Original article:&nbsp;<a href="https://medium.com/@hugolu87/the-perfect-data-pipeline-doesnt-exist-azure-ec2be63f61b5" rel="noopener">Azure</a></p> <p>This article focus:&nbsp;<a href="https://www.google.com/search?q=databricks&amp;oq=databricks&amp;aqs=chrome..69i57l2j69i59l2j69i61j69i65j69i60.960j0j7&amp;sourceid=chrome&amp;ie=UTF-8" rel="noopener ugc nofollow" target="_blank">Databricks</a></p> <h1>The Databricks edition</h1> <p>It is really, really worthwhile looking into a platform like Databricks, and that&rsquo;s because you can do pretty much anything in it. Personally, although I work on&nbsp;<a href="https://getorchestra.io/" rel="noopener ugc nofollow" target="_blank">Orchestra&nbsp;</a>which you wouldn&rsquo;t need if you use an &ldquo;all in one&rdquo; platform, I think it makes complete sense using an &ldquo;all in one&rdquo; platform. Nothing even comes close to Databricks apart from possibly Microsoft Fabric. So let&rsquo;s dive in and see why it&rsquo;s so sick.</p> <p><a href="https://medium.com/@hugolu87/the-perfect-data-pipeline-doesnt-exist-databricks-ee0629fa2f6c"><strong>Visit Now</strong></a></p>
Tags: Data Pipeline