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Learn MorePros of Dask
Pros of React
Pros of Dask
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Pros of React
- Components837
- Virtual dom673
- Performance578
- Simplicity509
- Composable442
- Data flow186
- Declarative166
- Isn't an mvc framework128
- Reactive updates120
- Explicit app state115
- JSX50
- Learn once, write everywhere29
- Easy to Use22
- Uni-directional data flow21
- Works great with Flux Architecture17
- Great perfomance11
- Javascript10
- Built by Facebook9
- TypeScript support8
- Speed6
- Server Side Rendering6
- Scalable6
- Easy to start5
- Feels like the 90s5
- Awesome5
- Props5
- Cross-platform5
- Closer to standard JavaScript and HTML than others5
- Easy as Lego5
- Functional5
- Excellent Documentation5
- Hooks5
- Scales super well4
- Allows creating single page applications4
- Sdfsdfsdf4
- Start simple4
- Strong Community4
- Super easy4
- Server side views4
- Fancy third party tools4
- Rich ecosystem3
- Has arrow functions3
- Very gentle learning curve3
- Beautiful and Neat Component Management3
- Just the View of MVC3
- Simple, easy to reason about and makes you productive3
- Fast evolving3
- SSR3
- Great migration pathway for older systems3
- Simple3
- Has functional components3
- Every decision architecture wise makes sense3
- Sharable2
- Permissively-licensed2
- HTML-like2
- Image upload2
- Recharts2
- Fragments2
- Split your UI into components with one true state2
- React hooks1
- Datatables1
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Cons of Dask
Cons of React
Cons of Dask
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Cons of React
- Requires discipline to keep architecture organized41
- No predefined way to structure your app30
- Need to be familiar with lots of third party packages29
- JSX13
- Not enterprise friendly10
- One-way binding only6
- State consistency with backend neglected3
- Bad Documentation3
- Error boundary is needed2
- Paradigms change too fast2
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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.
What is React?
Lots of people use React as the V in MVC. Since React makes no assumptions about the rest of your technology stack, it's easy to try it out on a small feature in an existing project.
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What are some alternatives to Dask and React?
Apache Spark
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.
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
PySpark
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
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.
Airflow
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.