Software Alternatives, Accelerators & Startups
Register | Login

PyPOTS VS Nimbella

Compare PyPOTS VS Nimbella and see what are their differences

TimeLake.io
Improve team performance continuously with time tracking automation. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

PyPOTS logo PyPOTS

a Python lib for data mining on PartiallyObserved TimeSeries

Nimbella logo Nimbella

Simple serverless cloud for developers
  • PyPOTS Landing page
    Landing page //
    2023-09-15
  • Nimbella Landing page
    Landing page //
    2021-08-28

PyPOTS features and specs

No features have been listed yet.

Nimbella features and specs

  • Serverless Architecture
    Nimbella provides a serverless platform that enables developers to build and deploy applications without managing server infrastructure, allowing for scalable and optimized resource usage.
  • Multi-cloud Support
    The platform supports deployment across different cloud providers, providing flexibility and reducing vendor lock-in for applications.
  • Integrated Developer Experience
    Nimbella offers tools and features that enhance the developer experience, such as built-in CLI tools, debugging, and monitoring capabilities.
  • Event-driven Model
    Supports event-driven programming paradigms, helping developers build reactive applications that can efficiently handle various triggers and events.
  • Seamless CI/CD Integration
    Facilitates continuous integration and deployment through integrations with popular CI/CD tools, streamlining application development and updates.

Possible disadvantages of Nimbella

  • Learning Curve
    New users might face a learning curve when adapting to the serverless architecture and Nimbella's specific tools and workflows.
  • Limited Customization
    Serverless solutions like Nimbella may offer less control over infrastructure and server configurations compared to traditional hosting solutions.
  • Cold Start Latency
    Like other serverless platforms, Nimbella might experience latency during the 'cold start' period when functions are invoked after being inactive.
  • Cost Management Complexity
    While serverless can reduce costs, it requires monitoring and adjustment to prevent unexpected expenses, especially with unpredictable workloads.
  • Vendor Ecosystem Dependence
    Users planning to fully leverage Nimbella might find themselves reliant on its specific ecosystem and offerings, which could impact flexibility and extensibility.

PyPOTS videos

No PyPOTS videos yet. You could help us improve this page by suggesting one.

Add video

Nimbella videos

+ Add

Easily Create and Manage Your Custom Slack Commands with Nimbella Commander

More videos:

  • Review - DOP 65: Serverless Made Easy With Nimbella
  • Review - IBM Webinar with Nimbella

Category Popularity

0-100% (relative to PyPOTS and Nimbella)

PyPOTS

Nimbella

Productivity
22 22%
Productivity
78% 78
Developer Tools
9 9%
Developer Tools
91% 91
Open Source
32 32%
Open Source
68% 68
Web Scraping
100 100%
Web Scraping
0% 0

User comments

Share your experience with using PyPOTS and Nimbella. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, PyPOTS should be more popular than Nimbella. It has been mentiond 3 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

PyPOTS mentions (3)

  • [R] SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023.
    Absolutely my pleasure! Please pay a visit to the toolbox PyPOTS https://pypots.com if you're interested in modelling partially-observed time series (POTS). It deserves your attention ;-). Source: almost 2 years ago
  • Missing values in time series collected from the real world are common to see and very pesky. A new state-of-the-art and fast neural network called SAITS is proposed to impute missing data in partially-observed multivariate time series. The code is open source on GitHub.
    If your research lies in time-series modeling, you may also be interested in the work PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series https://pypots.com/. Its full paper is available on arXiv as well https://arxiv.org/abs/2305.18811, which has been peer-reviewed and accepted by the 9th SIGKDD international workshop Mining and Learning from Time Series (MiLeTS'23). Source: almost 2 years ago
  • We built PyPOTS: an open-source toolbox for data mining on partially-observed time series
    Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modelling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated... Source: almost 2 years ago

Nimbella mentions (1)

  • Kubernetes in simple words: explained by Eric Swildens
    To ease the development of Kubernetes we offer Nimbella serverless platform that is available on prem, private, hybrid, and public cloud. - Source: dev.to / almost 4 years ago

What are some alternatives?

When comparing PyPOTS and Nimbella, you can also consider the following products

Apify Python SDK - Build and manage web scraping Actors in the cloud.

Serverless - Toolkit for building serverless applications

Awesome Python - Your go-to Python Toolbox. A curated list of awesome Python frameworks, packages, software and resources. 1303 projects organized into 177 categories.

Up by apex - Deploy serverless apps and APIs in seconds to AWS Lambda

Learning Django Web Development - From idea to prototype, a learner guide for Django web dev

Webiny - The Enterprise CMS platform that you can host on your cloud

Loading...