You're building a feature that requires processing a large dataset (e.g., 1TB CSV file) to generate daily reports. Describe a robust and scalable approach to handle this data processing, considering potential memory constraints and performance requirements.

Employ a MECE (Mutually Exclusive, Collectively Exhaustive) approach for large dataset processing. First, data ingestion: utilize distributed file systems (HDFS) or cloud storage (S3) for the 1TB CSV. Second, data partitioning: split the CSV into smaller, manageable chunks based on a key (e.g., date, ID) to enable parallel processing and reduce memory footprint. Third, distributed processing framework: leverage Apache Spark or Hadoop MapReduce for parallel computation across a cluster, ensuring fault tolerance and scalability. Fourth, incremental processing: process data daily, appending new reports rather than reprocessing the entire dataset. Fifth, optimization: implement columnar storage (Parquet/ORC) for report generation, data compression, and efficient I/O. Finally, monitoring and alerting: set up tools to track job progress, resource utilization, and error handling.

For processing a 1TB CSV to generate daily reports, a robust and scalable approach involves a multi-stage, distributed strategy. Initially, store the 1TB CSV in a distributed file system like HDFS or cloud object storage (AWS S3, Azure Blob Storage) to ensure durability and accessibility across a cluster. Next, employ a distributed processing framework such as Apache Spark. Spark allows for efficient partitioning of the large CSV into smaller, manageable dataframes or RDDs, which can be processed in parallel across multiple nodes, mitigating memory constraints. Implement data partitioning based on a relevant key (e.g., date, customer ID) to enable targeted processing and reduce shuffle operations. For daily reports, adopt an incremental processing model: only process new data ingested since the last report, appending to existing reports rather than reprocessing the entire 1TB. Utilize columnar storage formats like Parquet or ORC for intermediate and final report data, as they offer superior compression and query performance compared to CSV. Finally, integrate robust error handling, retry mechanisms, and comprehensive monitoring to track job progress, resource utilization, and ensure data integrity and timely report generation.