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JSON Lines is the same format as NDJSON — one JSON object per line, newline-separated. The .jsonl extension is slightly more common in data science and machine learning workflows, while .ndjson is more common in streaming and logging contexts. The content is identical.
The format is popular in ML/AI pipelines because training datasets are naturally line-oriented. Each line is one training example — an input-output pair, an annotated text sample, a feature vector. Tools like Hugging Face datasets, OpenAI's fine-tuning API, and many data processing frameworks expect JSONL input.
Processing JSONL is trivially parallelisable: split the file by lines, distribute to workers, process independently. No parsing context is shared between lines. This makes JSONL ideal for MapReduce-style processing, distributed systems, and any workflow where data arrives incrementally.