Memorizing Complex Documentation: A Tale of Databricks Tables and Storytelling#

Documentation, especially in tech, can be dense and intricate. Memorizing such content can be a daunting task. Yet, there’s a timeless technique that can help simplify even the most complicated of subjects: storytelling. In this blog, we will demonstrate how to transform technical documentation into memorable stories, using Databricks’ managed and unmanaged tables as an example, told through the tale of “Maddy & Liam.”

The Need to Memorize Complex Content#

In today’s fast-paced digital world, professionals across industries often encounter vast and detailed documentation. For tech professionals, understanding these details is crucial. But how can one possibly remember all those details?

Let’s consider Databricks’ managed and unmanaged tables. At first glance, the distinctions between these tables can seem quite technical and abstract. Enter storytelling, our cognitive shortcut.

The Power of Storytelling#

Humans are hardwired to remember stories. Tales filled with characters, emotions, and vivid images leave an indelible mark on our minds. This isn’t just artistry; it’s neuroscience. When we read or hear stories, our brains release oxytocin, a hormone associated with empathy and emotion. This not only makes the content relatable but also makes it more memorable.

Databricks Tables: The Technicals#

Managed tables in Databricks are where the platform manages both metadata and data. Deleting the table also gets rid of the underlying data. On the flip side, unmanaged tables have Databricks managing only the metadata. Deleting an unmanaged table doesn’t touch the underlying data. There are other nuanced differences related to data locations and the renaming process.

Seems complex? Let’s break it down with a story.

Meet Maddy & Liam (MDLM)#

Imagine a vast digital library in a city filled with data. Here, we have two librarians: Maddy and Liam.

  1. Managed By Databricks (M):

  • Maddy: Like managed tables, she handles both the title (metadata) and content (data).

  • Liam: Mirroring unmanaged tables, he only notes down the title (metadata).

  1. Deletion (D):

  • Maddy: If a book needs to be removed, she eliminates its title and erases its contents, mirroring the behavior of managed tables.

  • Liam: He simply crosses off the title but leaves the content intact, much like unmanaged tables.

  1. Location (L):

  • Maddy: She organizes her books in specific rooms based on their category, similar to how managed tables have data in a defined location.

  • Liam: He places markers on books, pointing to categories located elsewhere, akin to unmanaged tables specifying a location.

  1. Move/Rename (M):

  • Maddy: If a book’s category alters, she physically relocates it to a new room. This mirrors managed tables where moving means rewriting data.

  • Liam: He simply updates his markers, leaving the book where it is, representing the flexibility of unmanaged tables.

Why this Works#

By transposing complex details onto relatable characters, the intricacies of Databricks’ managed and unmanaged tables become simple and memorable. Each time one thinks of these tables, the narrative of Maddy and Liam serves as an easy recall mechanism.

Conclusion#

Memorizing dense documentation doesn’t have to be tedious. By leveraging the age-old art of storytelling, we can transform intricate technicalities into engaging narratives. The next time you’re faced with a thick manual or a long list of specifications, think of how you can craft your own “Maddy & Liam.” Not only will it make your learning process enjoyable, but those details will also stick with you for a long time. Happy storytelling!