Pipeline Developer interview questions

A data pipeline is a series of data processing steps where data is ingested from different sources, transformed, and then delivered to the user or a database.

Common tools include Apache Airflow, Kafka, Spark, AWS Data Pipeline, Google Cloud Dataflow, and ETL tools like Talend and Informatica.

Error handling can be achieved through data validation checks, logging error messages, retry mechanisms, and setting up alerts for failures.

Key considerations include choosing the right tools for data volume, ensuring fault tolerance, designing for minimal latency, considering data consistency, and implementing robust error-handling mechanisms.

Ensuring data quality involves implementing validation rules, regular data audits, monitoring changes in data, and using data profiling techniques to identify anomalies.

ETL stands for Extract, Transform, Load. It is a type of data pipeline process that involves extracting data from various sources, transforming it into a suitable format, and loading it into a destination database.

Batch processing deals with large volumes of data at once at scheduled intervals, while stream processing handles data in real-time, processing each record or chunk of data as it arrives.

Performance can be optimized by using parallel processing, optimizing data structures, ensuring efficient data storage, using appropriate hardware resources, and reducing bottlenecks in the pipeline.

Data ingestion is the process of importing data for immediate use or storage. It is crucial because it directly affects the efficiency and effectiveness of the entire pipeline, determining what data is available and when.

Data security and privacy can be maintained by implementing encryption, access controls, data anonymization, audit logs, and compliance with data protection regulations like GDPR or HIPAA.