Data Engineer Interview: Airflow, Snowflake, AWS, Apache Kafka, and Azure Data Factory Insights

In this detailed Data Engineer interview, we dive deep into the practical use of essential tools and technologies used in data pipelines and ETL processes. Our expert discusses the role of Apache Airflow in scheduling and managing ETL workflows, leveraging Snowflake for optimization using features like micropartitioning and time travel, and implementing scalable solutions with AWS Glue and Athena. Learn about real-time data streaming with Apache Kafka, and automation with Pandas and Snowpark in Snowflake. Additionally, we cover crucial topics like managing data security and access control in AWS services such as Redshift and S3, and orchestrating ETL workflows using Azure Data Factory.
Whether you're preparing for a data engineering interview or looking to enhance your knowledge on ETL processes, cloud technologies, and real-time data streaming, this interview is packed with insights that will help you excel in your data engineering career.
Key Topics Covered:
Airflow for ETL pipeline management
Optimizing Snowflake ETL processes
AWS Glue and Athena for scalable data pipelines
Real-time data processing with Apache Kafka
Automation using Pandas and Snowpark
Data security management in AWS (Redshift & S3)
ETL orchestration with Azure Data Factory
Keywords: data engineer interview, airflow ETL, snowflake optimization, AWS Glue, Athena, Apache Kafka, Pandas automation, Snowpark, Azure Data Factory, data pipeline, cloud technologies, data engineering, ETL processes, real-time data streaming, data security, AWS IAM, Redshift, S3

[@abhijitvernekar593]

can you provide a detailed end to end data engineering project using airflow, snowflake

[@rausimple]

Noted 👍