Dask

1. Dask Overview

   Core ideas: parallel Python with lazy task graphs.

2. Dask DataFrame

   Partitioned pandas-like DataFrames, partitions and shuffles.

3. Dask Array

   Chunked NumPy-like arrays and chunk sizing.

4. Dask Bag

   Parallel processing of unstructured Python collections.

5. Delayed

   Parallelizing custom Python functions with lazy task graphs.

6. Futures

   Real-time asynchronous task submission with the distributed client.

7. Task Graphs and Optimization

   Graph specification, optimization and visualization.

8. Schedulers

   Single-machine and distributed schedulers and their trade-offs.

9. Best Practices

   Chunk sizing, persist/compute and avoiding common pitfalls.

10. Deploying Dask

    Cluster deployment on Kubernetes, cloud, HPC and local clusters.

11. Diagnostics and Dashboard

    Distributed dashboard, performance profiling and debugging.

Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.