I have extensive experience with Hadoop and Spark, having worked on projects that involved processing large datasets. I've used Hadoop for distributed storage and processing, and Spark for in-memory data processing, which significantly improved performance. My experience includes setting up clusters, optimizing jobs, and integrating these technologies with other tools like Hive and Kafka.

Data security and privacy are critical in big data projects. I ensure compliance with regulations like GDPR by implementing encryption, access controls, and anonymization techniques. I also use tools like Apache Ranger for fine-grained access control and audit logging to monitor data access and ensure data integrity.

I optimize big data workflows by analyzing job performance, identifying bottlenecks, and tuning configurations. I use techniques like partitioning, caching, and parallel processing to improve efficiency. Additionally, I leverage profiling tools to monitor resource usage and make data-driven decisions to enhance performance.

In a project involving real-time data processing, we faced latency issues due to high data volumes. I addressed this by redesigning the data pipeline, using Kafka for stream processing, and optimizing Spark jobs. This reduced latency by 40%, enabling real-time analytics and improving decision-making.

I ensure data quality by implementing data validation checks, using tools like Apache Griffin for data profiling, and establishing data governance policies. I also perform regular data audits and clean up inconsistent or duplicate data to maintain high-quality datasets.