What are the Advance topics in Databricks

Databricks offers a range of advanced topics that can help you deepen your understanding and enhance your skills in data engineering, data science, and machine learning. Here are some key advanced topics:

Advanced Data Engineering: Incremental Processing with Spark Structured Streaming and Delta Lake: Learn how to handle streaming data, perform aggregations, and manage stateful operations.
Data Ingestion Patterns: Explore various patterns for ingesting data efficiently into your data lakehouse.
Data Quality Enforcement Patterns: Implement strategies to ensure data quality and consistency.
Data Modeling: Design and optimize data models for efficient querying and analysis.
Performance Optimization: Fine-tune Spark and Delta Lake configurations to improve performance.
Advanced Machine Learning:
Machine Learning at Scale: Understand how to use Spark for data preparation, model training, and deployment.
Hyperparameter Tuning with Optuna: Learn advanced techniques for tuning machine learning models.
Model Lifecycle Management: Manage the entire machine learning lifecycle, including CI/CD, pipeline management, and model monitoring.
Model Rollout Strategies: Implement strategies for rolling out models and monitoring their performance.
Advanced ML Operations (MLOps): Focus on best practices for managing machine learning projects and ensuring reliability.

Advanced Data Science:

Advanced Data Transformations: Perform complex data transformations and manipulations using PySpark and SQL.
Real-Time Analytics: Implement real-time analytics solutions using Spark Structured Streaming.
Data Privacy Patterns: Learn how to store and manage data securely, including streaming data and Change Data Capture (CDC).
Automating Production Workflows: Use REST API and CLI to automate and manage production workflows.
Troubleshooting and Debugging: Develop skills to troubleshoot and debug data pipelines and Spark jobs.