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Amazon Kinesis Firehose VS Jupyter

Compare Amazon Kinesis Firehose VS Jupyter and see what are their differences

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Amazon Kinesis Firehose logo Amazon Kinesis Firehose

Amazon Kinesis Firehose can capture, transform, and load streaming data into AWS.

Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
  • Amazon Kinesis Firehose Landing page
    Landing page //
    2023-04-12
  • Jupyter Landing page
    Landing page //
    2023-06-22

Amazon Kinesis Firehose features and specs

  • Managed Service
    Amazon Kinesis Firehose is a fully managed service, which means it eliminates the operational overhead of managing and scaling infrastructure.
  • Real-time Processing
    It provides real-time data streaming capabilities, allowing for near-instantaneous processing and analysis of incoming data.
  • Seamless Integration
    Firehose integrates seamlessly with other AWS services like S3, Redshift, and Elasticsearch, making it easy to store and analyze data.
  • Automatic Scaling
    The service automatically scales to match the throughput of your data streams, ensuring consistent performance without manual intervention.
  • Data Transformation
    It provides built-in data transformation capabilities using AWS Lambda, allowing you to transform or enrich data before delivery.

Possible disadvantages of Amazon Kinesis Firehose

  • Cost
    While convenient, using Firehose can become expensive, especially with larger volumes of data or complex transformations.
  • Latency
    There might be slight latency involved in the data delivery process, which could be a concern for applications requiring ultra-low latency.
  • Dependency on AWS Ecosystem
    Firehose is tightly integrated with the AWS ecosystem, which could be a limitation if your architecture involves services outside of AWS.
  • Limited Customization
    As a managed service, users have limited control over the underlying infrastructure, which might be restrictive for those needing highly customized solutions.
  • Learning Curve
    New users might face a learning curve when getting started, especially if unfamiliar with AWS and its specific terminologies and best practices.

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

Amazon Kinesis Firehose videos

Introduction to Amazon Kinesis Firehose

More videos:

  • Review - Stream Data Analytics with Amazon Kinesis Firehose and Redshift

Jupyter videos

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

Category Popularity

0-100% (relative to Amazon Kinesis Firehose and Jupyter)
Data Dashboard
11 11%
89% 89
Data Science And Machine Learning
Data Management
100 100%
0% 0
Data Warehousing
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Amazon Kinesis Firehose and Jupyter

Amazon Kinesis Firehose Reviews

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Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Amazon Kinesis Firehose. While we know about 216 links to Jupyter, we've tracked only 6 mentions of Amazon Kinesis Firehose. 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.

Amazon Kinesis Firehose mentions (6)

  • What is an event-driven architecture and why storing events is important ?
    First, you may not know the Kinesis Data Firehose service. Here's the AWS definition: Amazon Kinesis Data Firehose is an Extract, Transform, and Load (ETL) service that captures, transforms, and reliably delivers streaming data to data lakes, data stores, and analytics services. (https://aws.amazon.com/kinesis/data-firehose/). - Source: dev.to / about 2 years ago
  • Serverless Event Driven AI as a Service
    As you can see in the diagram, we are feeding all events from Event Bus via a catch-all rule into Kinesis Data Firehose. Firehose is a fully managed service that streams into specific destinations like Data Warehouses or Data Lakes. Unlike it's bigger brother of using Kinesis Data Streams directly, there are no setting up of shards and it's mostly configuration free. We are only defining a buffer interval which is... - Source: dev.to / over 2 years ago
  • Logging EventBridge events to S3 with Firehose
    When using EventBridge I always log all events to an S3 bucket for auditing, analytics and debugging purposes. A super easy method to do this is to create a Kinesis Data Firehose stream and create a rule that captures all events that points to the Firehose stream. The Firehose stream can then flush the events on S3 in an interval/size of choice based on configuration. - Source: dev.to / over 2 years ago
  • S3 Isn't Getting Cheaper
    Have you looked at Kinesis Firehose? It was pretty much build for this use case although you will still need to see if you can define a partitioning scheme probably in combination with an S3 Select query to meet your query requirements. https://aws.amazon.com/kinesis/data-firehose/?nc=sn&loc=0. - Source: Hacker News / almost 3 years ago
  • Advice on S3 Application
    Is continuous backup important ? e.g. If the stuff fails for one day and you lose that day's upload is that ok? Do you want it to push updates more frequently than once a day? If you want to continuously push updates then Kinesis Firehose might be worth looking into. Source: over 3 years ago
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Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 4 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 5 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 12 months ago
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What are some alternatives?

When comparing Amazon Kinesis Firehose and Jupyter, you can also consider the following products

Analytics Canvas - Analytics Canvas is a data management platform with a specific focus on Google data tools, enabling self-serve data preparation and automation for those working with Analytics, Ads, Search Console, Sheets, BigQuery, Data Studio and more.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Data Scientist Workbench - A web-based notebook that enables interactive data analytics.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Lavastorm Analytics - Lavastorm is the agile data management and analytics solution.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.