Streaming & Real-Time Data Processing
Design and develop real-time streaming pipelines using Databricks Structured Streaming.
Build and maintain Kafka-based ingestion frameworks.
Handle late-arriving events using watermarks, event-time processing, and stateful streaming concepts.
Implement exactly-once processing and checkpointing mechanisms.
Monitor and optimize streaming workloads for performance and reliability.

Spark Declarative Pipelines (SDP) & Delta Live Tables (DLT):

Design and implement Spark Declarative Pipelines using Databricks.
Develop Delta Live Table (DLT) pipelines for scalable data transformations.
Implement data quality expectations, validations, retries, and failure handling within DLT pipelines.
Manage pipeline dependencies and orchestration using declarative approaches.
Understand advantages of SDP over traditional ETL pipelines, including:
Simplified pipeline development
Reduced operational overhead
Automated l...