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Snowflake VS Jupyter

Compare Snowflake VS Jupyter and see what are their differences

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Snowflake logo Snowflake

Snowflake is the only data platform built for the cloud for all your data & all your users. Learn more about our purpose-built SQL cloud data warehouse.

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.
  • Snowflake Homepage
    Homepage //
    2024-07-19
  • Jupyter Landing page
    Landing page //
    2023-06-22

Snowflake features and specs

  • Scalability
    Snowflake offers virtually unlimited scalability. It separates compute and storage, so both can scale independently according to the needs of the workload.
  • Performance
    Snowflake's architecture is optimized for performance, offering automatic clustering and parallel processing which enable faster query execution.
  • Ease of Use
    The platform provides a user-friendly interface and automates many maintenance tasks, such as indexing and partitioning, making it easier for both data engineers and analysts to use.
  • Data Sharing
    Snowflake enables seamless data sharing among different accounts without the need to duplicate data, improving collaboration and data management.
  • Security
    Snowflake includes comprehensive security features such as end-to-end encryption, role-based access control, and VPC/VPN network policies.
  • Multi-Cloud Support
    Snowflake supports multiple cloud providers, including AWS, Azure, and Google Cloud, giving organizations flexibility in choosing their infrastructure.

Possible disadvantages of Snowflake

  • Cost
    While powerful, Snowflake can become expensive, especially if not managed properly, due to its pay-as-you-go pricing model.
  • Vendor Lock-In
    Once an organization is deeply integrated with Snowflake, switching to another solution can be complex and costly, contributing to vendor lock-in.
  • Learning Curve
    Though easier than many traditional databases, there is still a learning curve associated with mastering Snowflake’s unique architecture and features.
  • Third-Party Ecosystem
    While Snowflake integrates well with many third-party tools, it may not support all the tools and services you are currently using, requiring additional effort for integration.
  • Network Performance
    Snowflake's performance can be impacted by network latency, especially if large datasets are being transferred over the internet between Snowflake and on-premises systems.

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.

Snowflake videos

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Jupyter videos

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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 Snowflake and Jupyter)

Snowflake

Jupyter

Big Data
100 100%
Big Data
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Warehousing
100 100%
Data Warehousing
0% 0
Data Dashboard
26 26%
Data Dashboard
74% 74

User comments

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Reviews

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

Snowflake Reviews

Top 6 Cloud Data Warehouses in 2023
Snowflake accommodates data analysts of all levels since it does not use Python or R programming language. It is also well known for its secure and compressed storage for semi-structured data. Besides this, it allows you to spin multiple virtual warehouses based on your needs while parallelizing and isolating individual queries boosting their performance. You can interact...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Snowflake is one of the most popular data warehousing solutions on the market and delivers an incredible experience across multiple public clouds. By using Snowflake, companies can pull data from various business intelligence tools to do reporting and analytics without any database administration, thus avoiding high overhead costs. Unlike other data warehousing services,...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
Plus, Snowflake doesn’t include data integrations, so teams will have to bolt on an ETL tool to pipe their data into the warehouse. Those third-party pipelines add extra cost and overhead in the form of setup and maintenance that some teams may not want to absorb.
Source: blog.panoply.io
Top Big Data Tools For 2021
This platform can be used for data warehousing, data science, data engineering, sharing, and application development. It enables you to easily secure your data and execute various analytic workloads. Snowflake also ensures a seamless experience when working with multiple public clouds.
Source: blog.bismart.com

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.
Source: searchbusinessanalytics.techtarget.com
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.
Source: towardsdatascience.com

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Snowflake. While we know about 216 links to Jupyter, we've tracked only 4 mentions of Snowflake. 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.

Snowflake mentions (4)

  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Snowflake, a data warehousing company founded by ex-Oracle and ex-VectorWise experts, responded with a blog post that critically reviewed Databricks' findings, reported different results for the same benchmark, and claimed comparable price/performance to Databricks. - Source: dev.to / almost 3 years ago
  • Personal Support at Internet Scale
    Snowflake: Snowflake is fast, and works well as a product analytics database. - Source: dev.to / over 3 years ago
  • Less than 1TB of data what tools should I get better at?
    If you just go to snowflake.com you can sign up for a demo account for free for a month and I'm fairly certain you can get more than one of these accounts (I would recycle emails doing it all the time.) Once you have an account there's lots of docs and videos out there either using the Database via their UI or via python using their connector. They also have a pyspark connector but you might want to just learn... Source: over 3 years ago
  • *BOMATO*
    Early stage funding & VCs clearly demarcate between tech companies and tech enabled companies. But, once the PE comes into the picture at the scale of BlackStone, the border between doordash.com and snowflake.com starts to blur. The motivation is to make some bucks by going to IPO and they know how to get it done. Source: almost 4 years ago

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 / 3 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 / 4 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 / 8 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 / 11 months ago
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What are some alternatives?

When comparing Snowflake and Jupyter, you can also consider the following products

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

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.

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

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

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