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Snowflake provides a single platform for data warehousing, data lakes, data engineering, data science, data application development, and secure sharing and consumption of real-time and shared data


  1. Introduction to Snowflake for data analysts

  2. How to collaborate in Snowflake

  3. How to share queries in Snowflake

  4. What file formats does Snowflake support?

  5. Snowpark: A Beginner’s Guide

Data science and analytics

Data science and analytics are interdisciplinary fields that involve extracting insights and knowledge from data through various techniques, including statistical analysis, machine learning, data mining, and visualization.


Jupyter is an open-source project that provides a web-based interactive computing platform. The name "Jupyter" is derived from the core programming languages it initially supported: Julia, Python, and R.


With an intuitive, real-time collaborative environment, teams can seamlessly work together from anywhere. Deepnote simplifies visualizing complex datasets with features like connecting to various data sources and showing them to stakeholders.


Collaboration within data teams is critical as it pools diverse expertise and perspectives, leading to more comprehensive and robust analyses. By working together, team members can validate findings and reduce the risk of oversight or bias in data interpretation.

Google Cloud

Google Cloud is a suite of cloud computing services provided by Google that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, file storage, and YouTube. It offers a range of services including computing, data storage, data analytics, and machine learning. Organizations of all sizes can utilize Google Cloud to create anything from simple websites to complex applications.


Amazon Web Services (AWS) is a comprehensive and widely adopted cloud platform that offers over 200 fully featured services from data centers globally. AWS provides infrastructure and various services such as computing power, storage options, and networking that can be used to build applications with increased flexibility, scalability, and reliability.


SQL, or Structured Query Language, is a powerful tool for managing and manipulating relational databases, allowing for complex data querying and analysis. It is the standard language for relational database management systems, used widely in various data-driven applications.

Data platforms

Data platforms are technology solutions that collect, store, manage, and analyze large amounts of data. They handle the growing volume, velocity, and variety of data from digital activities. These platforms provide infrastructure for data warehousing, big data processing, and analytics, enabling organizations to gain insights and make data-driven decisions.


Versioning with notebooks involves maintaining a record of changes and updates made to the documents, making it easier to track revisions and collaborate with others.


How to use state of the art AI with Data Science & Deepnote


A collection of guides for specific use cases



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