How do you manage large volumes of data in a deep learning pipeline implementation using PySpark, including partitioning and handling bursts of data?

In a deep learning pipeline that processes large volumes of data, PySpark is often used to parallelize data ingestion, preprocessing, and feature engineering. The key steps are: 1) Data ingestion – read data from distributed storage (HDFS, S3, Kafka) using Spark’s DataFrame API. 2) Partitioning – partition the DataFrame by a key that aligns with downstream processing (e.g., user ID, timestamp) to ensure even data distribution and reduce shuffle. 3) Caching and persistence – cache intermediate results that are reused (e.g., tokenized text, image features) to avoid recomputation. 4) Handling bursts – use Spark Structured Streaming or a back‑pressure mechanism to buffer incoming data, or scale executors dynamically with cluster manager (YARN, Kubernetes). 5) Batch‑to‑stream bridging – for sudden spikes, write data to a durable queue (Kafka) and process it in micro‑batches. 6) Resource tuning – adjust executor memory, cores, and parallelism to match data size. This approach keeps the pipeline scalable, fault‑tolerant, and able to handle both steady and bursty workloads.