diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
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--- a/docs/source/_toctree.yml
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@@ -30,6 +30,8 @@
       title: Process
     - local: stream
       title: Stream
+    - local: stream_best_practices
+      title: Streaming best practices
     - local: use_with_pytorch
       title: Use with PyTorch
     - local: use_with_tensorflow
diff --git a/docs/source/stream_best_practices.mdx b/docs/source/stream_best_practices.mdx
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+# Streaming best practices
+
+This page complements the Streaming guide with practical patterns and pitfalls for large-scale, production-grade usage.
+
+## TL;DR patterns
+
+- Prefer local streaming (to_iterable_dataset) when data is already on disk.
+- Use batched map with remove_columns to keep memory bounded.
+- Shuffle with a large buffer and set_epoch per epoch for determinism.
+- For multi-worker loaders, shard at source: to_iterable_dataset(num_shards=...)
+- Persist results incrementally: push_to_hub with num_proc or to_parquet per shard.
+
+## Local vs HTTP streaming
+
+- Local streaming avoids network flakiness and maximizes throughput. Convert hub datasets once, then do:
+
+```py
+from datasets import load_dataset
+base = load_dataset("allenai/c4", "en")
+it = base.to_iterable_dataset(num_shards=64)
+```
+
+- HTTP streaming is ideal for huge corpora you won’t store. Use resumable endpoints; retry-aware file systems (fsspec, HF Hub); and keep transformations simple.
+
+## Buffered transforms that stay memory-safe
+
+```py
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+ds = load_dataset("HuggingFaceFW/fineweb", split="train", streaming=True)
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+# Avoid keeping original text after tokenization
+batched = ds.map(
+    lambda ex: tokenizer(ex["text"], truncation=True, padding="max_length"),
+    batched=True,
+    remove_columns=["text", "timestamp", "url"],
+    batch_size=1024,
+)
+```
+
+- Use batched=True to amortize tokenizer overhead.
+- remove_columns to drop large raw fields ASAP.
+- Tune batch_size to the smallest size that saturates your CPU/GPU.
+
+## Deterministic shuffling and epoch control
+
+```py
+seed, buffer_size = 42, 10_000
+shuffled = it.shuffle(seed=seed, buffer_size=buffer_size)
+for epoch in range(num_epochs):
+    shuffled.set_epoch(epoch)
+    for example in shuffled:
+        ...
+```
+
+- Shuffle reorders shards and uses a buffer; larger buffers approximate global shuffle.
+- set_epoch(epoch) changes the RNG stream so each epoch differs but is reproducible.
+
+## Multi-worker data loading
+
+```py
+import torch
+from torch.utils.data import DataLoader
+
+it = base.to_iterable_dataset(num_shards=64).shuffle(seed=42, buffer_size=10_000)
+loader = DataLoader(it, num_workers=4)
+```
+
+- Sharding at creation lets each worker read disjoint shards without contention.
+- Prefer num_shards >> num_workers to keep workers busy.
+
+## Checkpoint and resume
+
+- Use IterableDataset.state_dict() and .load_state_dict() to save/restore progress.
+- With shuffle(), buffer contents aren’t preserved; expect slight differences after resume.
+
+## Persisting results
+
+- Save per-shard parquet to keep memory bounded and enable parallelism:
+
+```py
+num = it.num_shards
+for i in range(num):
+    shard = it.shard(num_shards=num, index=i)
+    shard.to_parquet(f"out/data-{i:05d}.parquet")
+```
+
+- Or push_to_hub("username/dataset", num_proc=8) to upload in parallel.
+
+## Common pitfalls
+
+- Trying to index by absolute position; IterableDataset only supports iteration.
+- Mapping with functions that capture large globals or return Python objects not supported by Arrow.
+- Using take/skip before shuffle, which locks shard order and prevents later shuffling.
+- Small shuffle buffers leading to poor mixing; increase buffer_size.
+
+## See also
+
+- Streaming guide: ./stream
+- Dataset vs IterableDataset: ./about_mapstyle_vs_iterable
+- Batch mapping: ./about_map_batch