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Interactive Notebooks for Data processing

All data professionals need a common language and an environment where the team can work together. To support data exploration, prototyping, collaboration, documentation, reproducibility, presentation, and integration with data infrastructure, many experts use interactive notebooks. These tools provide a user-friendly and interactive environment for data processing and analysis, which makes them a popular choice for data experts. So, what is an interactive notebook?

What is a Notebook

An interactive notebook is a tool of building an interactive computing environment user interface that combines in one window work with code, source data, and the result of calculations and generated graphics in a single editable document. The main application of notebooks is machine learning, neural networks, data visualization, and statistics.

This environment is often used for phased development when you need to check the work of different code fragments in steps because the code in notebooks is stored in independent cells and can be run in any order or one by one. It allows users to experiment quickly with algorithms and find the optimal solution. The easiest way to work with a notebook is to write code and see what happens after launch.

The main functions of the interactive notebook are:

• working with code in REPL mode in a single interface;

• interactive data visualization;

• access and processing of data from external sources;

• implementation of mathematical calculations;

• documentation including formatted formulas, source code, and data used.

Popular Notebooks

Let's look at the most popular notebooks among software developers, which have become universal data processing tools.

Jupyter Notebook. It is a free interactive environment for Python. Here you can run code, get results and continue to work with them. You can work with each fragment separately and in any sequence. For example, write one function and check its operation without running the entire program. And you can also display the results immediately after writing the code fragment. For example, build an intermediate graph, save it as a separate file, and then use it in the presentation. In Jupyter Notebook, you can set up sharing and share your notebooks with other users - by email, via Dropbox, or Jupyter Notebook Viewer.

Main advantages of the Jupyter Notebook:

Multi-language support: Jupyter Notebook supports multiple programming languages, including Python, R, and Julia. It makes it a flexible tool for data processing and analysis, as users can choose the language that best fits their needs.

Interactive environment: Jupyter Notebook provides an interactive data processing environment, allowing users to explore and manipulate data in real-time. It is beneficial for data analysis, as users can quickly iterate on their code and see the results immediately.

Rich output: Jupyter Notebook supports rich output formats, including HTML, LaTeX, PDF, images, and videos. It makes it easy to create and share documents that contain not only code and data but also visualizations and narrative text.

Collaboration: Jupyter Notebook is designed to be shared and collaborated on, making it easy for teams to work together on data processing projects. Notebooks can be shared via email, GitHub, or the Jupyter Notebook Viewer.

Reproducibility: Jupyter Notebook supports version control, which makes it easy to track changes to code and data over time. It helps to ensure that data processing steps are repeatable and reproducible.

Large ecosystem: Jupyter Notebook has a large and active community of users and developers, which means there are many third-party libraries, plugins, and extensions available. It makes it easy to extend the functionality of the Jupyter Notebook and customize it to fit specific needs.

Apache Zeppelin. It is a tool for big data analytics in the Hadoop ecosystem. It simplifies the development of Spark applications and is aimed at enterprise users, providing LDAP integration, permission management, and interactive visualization with sufficient cluster resources. It includes some software development features, focusing on interactive big data analysis opportunities.

Primary Benefits of Apache Zeppelin Notebook:

  • it supports multiple programming languages, including Python, R, SQL, Scala, and many others;

  • it has built-in data visualization capabilities, which makes it easy to create charts, graphs, and other visualizations of data;

  • it is designed for collaboration, with features like version control, real-time collaboration, and integration with popular collaboration tools like Slack and JIRA;

  • it has built-in integration with Big Data technologies like Apache Spark, Hadoop, and Hive;

  • it is highly customizable, with support for custom interpreters, plugins, and extensions;

  • it is an open-source project, that means it is free to use and has a large and active community of contributors.

Databricks Notebooks is a web-based collaborative development environment for data scientists, analysts, and engineers. It is an integral part of the Databricks Unified Analytics Platform and provides an interactive workspace for creating and running code, visualizing data, and building machine learning models.

Users can write code in a notebook, a web-based document containing live code, visualizations, and narrative text. Notebooks can be organized into folders, shared with collaborators, and version-controlled using Git.

Key features of Databricks Notebooks:

  • it has the ability to seamlessly integrate with other tools in the Databricks platform, such as Databricks Delta for data management and Apache Spark for distributed data processing;

  • it includes built-in support for popular libraries and frameworks for machine learning, such as TensorFlow, Keras, and Scikit-learn.

Databricks Notebooks can be run on the Databricks cloud platform or on-premises using the Databricks Runtime for on-premises. The platform provides a scalable and secure environment for data processing and machine learning and supports collaboration and reproducibility in data science projects.

Datalore is an online environment for Jupyter Notebook developed at JetBrains that is designed to analyze and visualize data in Python.

Advantages of Datalore:

  • the automatic start of the calculation of only those operations that were dependent on edits;

  • granting access to different computing capacities depending on the task;

  • saving the entire analysis process to the cloud;

  • changes in the workbook are saved automatically.

If something goes wrong, you can roll back to previous analysis options and track the timeline of changes using the built-in version control system.

Google Colab is a cloud-based platform provided by Google that allows users to write, run, and share interactive Jupyter notebooks.

Main Advantages of Colab:

  • Free cloud computing resources

  • Collaborative environment for real-time collaboration

  • Pre-installed libraries and packages for easy setup

  • Seamless integration with Google services like Drive and BigQuery

  • Supports interactive elements and rich media

  • Integrated documentation and code cells for better organization

  • Easy sharing and publishing of notebooks

  • Integration with Google Cloud services for additional resources

Which interactive notebook to select depends on the project goal and dataset size.

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