To optimize data pipeline development on Databricks for large-scale workloads, focus on efficient data processing and resource management. Leverage Apache Spark's distributed computing capabilities to handle massive datasets. Use Delta Lake for relia...
Common pitfalls when migrating large on-premise ETL workflows to Databricks include data compatibility issues, lack of scalability planning, and inefficient resource management. Data transformation logic may need to be rewritten for Spark compatibili...
To build reusable data engineering components in Databricks, focus on modular design by creating reusable notebooks, libraries, and widgets. Leverage Delta Lake for data consistency and scalability, ensuring reliable data pipelines. Use MLflow for mo...
Databricks Repos enables seamless collaborative development by integrating Git repositories for version control, allowing multiple users to work on notebooks and code simultaneously. To integrate CI/CD, link your Databricks workspace with CI/CD tools...
When working with large datasets in Databricks, it's crucial to follow best practices to avoid memory issues. First, optimize data partitioning to ensure that data is evenly distributed across workers. Use efficient data formats like Parquet for bett...
