How to Build a 5-Layer Data Stack
<p>Like bean dip and <a href="https://www.youtube.com/watch?v=aJQmVZSAqlc" rel="noopener ugc nofollow" target="_blank">ogres</a>, layers are the building blocks of the modern data stack.</p>
<p>Its powerful selection of tooling components combine to create a single synchronized and extensible data platform with each layer serving a unique function of the data pipeline.</p>
<p>Unlike ogres, however, the cloud data platform isn’t a fairy tale. New tooling and integrations are created almost daily in an effort to augment and elevate it.</p>
<p>So, with infinitely expanding integrations and the opportunity to add new layers for every feature and function of your data motion, the question arises — where do you start? Or to put it a different way, how do you deliver a data platform that drives real value for stakeholders without building a platform that’s either too complex to manage or too expensive to justify?</p>
<p>For small data teams building their first cloud-native platforms and teams making the jump from on-prem for the first time, it’s essential to bias those layers that will have the most immediate impact on business outcomes.</p>
<p><a href="https://towardsdatascience.com/how-to-build-a-5-layer-data-stack-508ed09711f2"><strong>Learn More</strong></a></p>