My First Month Learning Databricks - Key Takeaways So Far.

[Rohan_Samariya]

Hey everyone 👋

I recently started my Databricks learning journey about a month ago, and I wanted to share what I’ve learned so far from one beginner to another.

Here are a few highlights:
1️⃣ Understanding the Lakehouse Concept - Realized how Databricks combines the best of data lakes and data warehouses in one unified platform.
2️⃣ Getting Started with Notebooks - I practiced running PySpark code directly inside notebooks, which helped me explore data and visualize results quickly.
3️⃣ Learning About Delta Tables - Discovered how Delta Lake makes data versioning and updates easy with ACID transactions.
4️⃣ Experiment Tracking with MLflow - Even though I’m still exploring it, MLflow looks powerful for keeping track of models and experiments.

Next, I plan to build a small end-to-end pipeline and start experimenting with ML models using Databricks.

If anyone has beginner-friendly project ideas or tips, I’d love to hear them! 🙌

#Databricks #LearningJourney #DataEngineering #MLflow #DeltaLake

[bianca_unifeye]

 Kudos to you for diving into Databricks so quickly and already covering so many core concepts! That’s a fantastic foundation, you’ve clearly built an understanding of both the platform and its ecosystem (Lakehouse, Delta Lake, and MLflow are key pillars).

Suggestion: a great next step would be to build a simple data pipeline using the Medallion Architecture. For example:

• Bronze: Ingest raw CSV/JSON data (like public datasets from Kaggle).

• Silver: Clean and transform it with Spark (handle nulls, deduplicate, enrich).

• Gold: Aggregate the data for insights, maybe a small dashboard or BI visualization.

Then, you can layer in MLflow to track a basic ML model (like predicting sales or ratings). It’s the perfect beginner-friendly project that connects all the concepts you’ve learned so far.

Keep going! You’re already thinking like a data engineer 
#KeepLearning #Databricks #Lakehouse #DataEngineering