Great question, Suheb! Working with large datasets in Databricks requires both efficient data handling and optimization of Spark operations to avoid memory issues and maintain performance. Here are some best practices:1. Optimize Data Storage & Forma...
Building ETL pipelines on Databricks is powerful, but there are some real-world challenges that teams commonly face. One of the biggest is scalability and performance tuning — especially when dealing with large datasets where choosing the right clust...
Leveraging Databricks Marketplace and APIs can significantly speed up data-driven app development. The Marketplace gives quick access to validated datasets, ML models, and connectors, reducing time spent on sourcing and infrastructure setup. For inte...
