How can I build an AI system (using Databricks) where the AI model doesn’t just rely on its built-in knowledge, but also retrieves real information from a database or documents before answering?
If you are building data pipelines in Databricks (where data is Extracted, Transformed, and Loaded), what tips, methods, or best practices do you use to make those pipelines run faster, cheaper, and more efficiently?
Databricks Repos make collaborative development easy by connecting notebooks to Git. You can work on branches, track changes, and sync with your team. Plus, they integrate with CI/CD pipelines, allowing automated testing and deployment of notebooks o...
Developing ETL pipelines in Databricks comes with challenges like managing diverse data sources, optimizing Spark performance, and controlling cloud costs. Ensuring data quality, handling errors, and maintaining security and compliance add complexity...
Leveraging Databricks Marketplace and API integrations can significantly streamline app development. By using pre-built datasets, notebooks, and APIs, developers can accelerate data workflows, reduce redundant coding, and ensure seamless integration ...
Developing and debugging Spark jobs in Databricks can be challenging due to the distributed nature of Spark and the volume of data processed. To streamline your workflow:Leverage Notebooks for Iterative Development:Use Databricks notebooks to write a...
