What is Dask?
It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.
Dask is a tool in the Data Science Tools category of a tech stack.
Who uses Dask?
12 companies reportedly use Dask in their tech stacks, including Oxylabs, Corca's Tech Stack, and Data Science.
75 developers on StackShare have stated that they use Dask.
Python, NumPy, Pandas, PySpark, and OpenRefine are some of the popular tools that integrate with Dask. Here's a list of all 8 tools that integrate with Dask.
Jobs that mention Dask as a desired skillset
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- Supports a variety of workloads
- Dynamic task scheduling
- Trivial to set up and run on a laptop in a single process
- Runs resiliently on clusters with 1000s of cores
Dask Alternatives & Comparisons
What are some alternatives to Dask?
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Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.