1 Why scalable computing matters


This chapter covers

  • Presenting what makes Dask a standout framework for scalable computing
  • Demonstrating how to read and interpret directed acyclic graphs (DAGs) using a pasta recipe as a tangible example
  • Discussing why DAGs are useful for distributed workloads and how Dask’s task scheduler uses DAGs to compose, control, and monitor computations
  • Introducing the companion dataset

1.1 Why Dask?

1.2 Cooking with DAGs

1.3 Scaling out, concurrency, and recovery

1.3.1 Scaling up vs. scaling out

1.3.2 Concurrency and resource management

1.3.3 Recovering from failures

1.4 Introducing a companion dataset