Suppose you are calling an API that returns a nested JSON response with a very large data size, updated in milliseconds. How would you handle and transform this data efficiently?

First, stream the API response instead of loading it all into memory. Use a streaming JSON parser (e.g., Jackson Streaming API, ijson in Python) to process each record as it arrives. Define a schema that flattens the nested structure: map nested objects to separate columns or tables, and use JSON path expressions to extract needed fields. Store the flattened data in a columnar format like Parquet or ORC for efficient compression and query performance. Partition the data by a logical key (e.g., timestamp, region) to enable parallel processing. For transformation, use a distributed processing framework such as Apache Spark or Flink; they can read the streaming JSON, apply UDFs to flatten and clean, and write back to a data lake or database. If updates are frequent, consider using a change‑data‑capture (CDC) approach or a time‑series database that supports upsert semantics. Finally, monitor the pipeline with metrics (latency, throughput) and set up alerts for failures. This architecture balances low latency, scalability, and maintainability for large, rapidly changing JSON data.