Data Engineering

To build scalable data pipelines, this skill helps teams design and implement ETL workflows using Apache Spark, Airflow, and Kafka for both batch processing and real-time streaming analytics. It reduces manual coding and operational overhead.

To orchestrate a daily ETL workflow with dbt and Airflow, you can ingest raw events, apply transformations using Spark or SQL, and load results into a curated data warehouse while applying data quality checks via a dbt model.

Implementing data quality and lineage checks in an ELT pipeline involves applying validation rules and governance checks directly within your dbt models to ensure curated data warehouse assets maintain high quality and reliability.

Yes, you can use Apache Spark Streaming or Flink with Kafka to enable real-time data streams. This allows teams to process continuous data flows and build streaming analytics pipelines for immediate insights.

To ingest raw data into a data lake using Apache Spark, you can build a batch pipeline that extracts raw events, applies necessary transformations, and loads the curated results into a target data warehouse or cloud storage.

Batch processing handles scheduled daily workflows using Spark and SQL, whereas stream processing handles real-time continuous data via Kafka and Flink. Batch is not suitable for immediate analytics, while streaming requires more complex infrastructure.