# Processing data: batch and stream

> From what the two approaches are, through windows and tools, to a working pipeline you can run for a few dollars.

- [Batch vs stream processing, explained with a simulation](https://learn.chabulab.com/system-design/batch-vs-stream-explained.md): Batch collects data and processes it at once, stream handles it as it arrives. Run a live simulation to see what each costs, with a comparison table.
- [Batch or stream: which one fits your problem?](https://learn.chabulab.com/system-design/choosing-batch-or-stream.md): Pick a real problem to see whether batch, stream or both fits it, and why. With a quick rule for choosing and the Lambda and Kappa architectures.
- [Stream windows, event time and late data, explained](https://learn.chabulab.com/system-design/stream-windows-and-late-data.md): An endless flow cannot be summed once. Watch fixed, sliding and session windows in a live demo, then learn event time, watermarks and how to avoid duplicates.
- [Data processing tools: what to choose by language and cloud](https://learn.chabulab.com/system-design/data-processing-technologies.md): What Spark, Flink, Kafka, Airflow and the services on Cloudflare, AWS, Google Cloud and Azure do. Choose in three layers: industry standard, cloud, language.
- [Build a cheap data pipeline with TypeScript on Cloudflare or AWS](https://learn.chabulab.com/system-design/build-cheap-data-pipelines-typescript.md): Take orders, process small batches and total them nightly in one Worker. With a price table by tier, an AWS version and a prompt to hand the job to an AI.
- [Build batch pipelines with Spark and stream with Kafka, Flink](https://learn.chabulab.com/system-design/batch-and-stream-with-spark-and-flink.md): The steps to build a batch pipeline with PySpark and Airflow and a stream pipeline with Kafka and Flink SQL, with samples, safe re-runs and what to learn next.