How do you handle large-scale data processing and distributed computing?

Large-scale data processing requires distributed computing frameworks and optimization strategies for performance and cost efficiency.\n\n• Technologies: Apache Spark, Dask, distributed pandas processing\n• Storage: Data lakes, columnar formats (Parquet), partitioning strategies\n• Optimization: Lazy evaluation, caching, broadcast variables\n• Cloud platforms: AWS EMR, Google Dataproc, Azure HDInsight\n\nExample: Processing 100TB customer data uses Spark with optimized partitioning, columnar storage, and broadcast joins. Implements incremental processing, data quality checks, and cost optimization through spot instances and auto-scaling.