Coiled Resources


Spark and Dask are both scalable computing libraries. Eric describes Spark as distributed DataFrames in Python on the JVM on a CPU and Dask as distributed DataFrames in Python on the CPU. Their core designs are vastly different — Spark follows the map-reduce paradigm while Dask follows a task-graph approach. They also have their own ecosystems — Spark supports many Apache projects, while Dask interoperates with the Python data science stack. You can learn more about the differences in Dask’s Comparison to Spark.

DataFrames are a data structure that represent a multidimensional array, with labeled axes and useful metadata about the stored values.

In enterprise data science, there are five key elements: data storage, development environment, scalable computing tool, workflow manager, and dashboarding libraries. Data is the foundation of all analyses and can be stored in a variety of places (Amazon S3, BlazingSQL, etc.) and in different forms (CSV, SQL database, etc.).  Development environments (Jupyter Notebooks, VSCode, etc.) include code editors and code management systems that enable data scientists to write and share code. Scalable computing tools (Dask, Spark, etc.) help scale the analyses to larger datasets and models. Workflow managers allow you to automate building, scheduling, and managing data pipelines. Finally, dashboarding and visualization tools help explore the data, find patterns, and present results to stakeholders.

Dask Delayed is a low-level Dask API that allows you to parallelize any general or custom Python code. It is especially useful in cases where you need finer control over the parallel architecture.

Dask APIs are the interfaces that you use to write Dask code. There are two classes of APIs: high and low level. High-level APIs like Dask DataFame and Dask Array allows you to parallelize common data science libraries like pandas and NumPy. The low-level APIs like Dask Delayed allow you to parallelize any general Python code.

There are three main ways to parallelize your workflow with Dask:

  • Using the high-level Dask APIs: Dask provides parallel alternatives for common PyData libraries like NumPy, pandas, and scikit-learn, which have familiar syntax you can use directly.
  • Using the low-level Dask APIs: Dask also allows you to write custom parallel and distributed code with low-level APIs that access the Dask Engine. 
  • Using the tools built on Dask: There are numerous libraries that are built using Dask like Prefect, PyTorch, RAPIDS, and more, that you can use for specialized use-cases.

There are many benefits to parallelization, which include:

– faster computations,

– processing larger-than-memory data,

– utilizing all available system resources.

Parallel computing can provide significant speed-up compared to single-core workflows, allowing you to do more analysis and work with more data.

Parallel computing, also known as multi-core processing, is the use of two or more processors working simultaneously on a single task.

One of the main benefits of cloud computing services is that it allows you to access large amounts of computing and storage resources from anywhere.

Supercharging with Dask and Coiled can help scale your data work to larger data sets and larger ML models, which allows you to solve complex problems. You can take advantage of the parallel computing capabilities of your machine and quickly leverage cloud resources when needed.

Coiled is a “cluster as a service” product that offers on-demand Dask clusters on the cloud. It helps data professionals solve a wide range of large and complex problems, everything from data manipulation to machine learning, from the comfort of their own laptops. Coiled is tailored for teams and enterprises. It includes features for managing teams, creating and sharing software environments, dynamically scaling resources, analyzing costs, and much more.