Known for its ecosystem of open-source products and libraries, the Apache Software Foundation offers data scientists tools that require collaboration and compatibility within their software. From Apache Hadoop to Spark, the organization provides a variety of database management systems, analytics technologies, and machine learning resources. Apache Zeppelin is the most recent addition to Apache’s data science tools and a must for data scientists interested in working in an open-source interface. Comparable to Jupyter Notebook and other programming interfaces, Apache Zeppelin is compatible with several languages and popular among data engineers and developers. Every Data Scientist should think about giving this tool a try!
What is Apache Zeppelin?
Apache Zeppelin is a web-based notebook environment used for data analytics and visualization. Apache describes Zeppelin as having “four primary functions: data ingestion, data discovery, data analytics, and data visualization and collaboration.” Data ingestion is the data collection stage of the data science lifecycle and includes the process of uploading or transferring data to the notebook. As an open-source product, Apache Zeppelin is compatible with SQL databases and many data science tools used for collection and storage. Data scientists can also make discoveries about their data by pairing Apache Zeppelin with additional tools.
As an open-source data science tool, Apache Zeppelin is not only compatible with Apache software and libraries but also with programming languages such as SQL and Python. Specifically, the Apache Zeppelin Interpreter allows data scientists to use various plug-ins that interpret different languages such as Apache Spark, Scala, and RStudio. Like other notebooks, Zeppelin is used by individual data scientists or data science teams, so users have multiple licensing options.
Data visualization using Zeppelin is compatible with interpreter tools like SparkSQL, which analyzes SQL data and displays it to an audience. Like Microsoft Excel, Zeppelin enables constructing Pivot Tables using drag and drop functions, an excellent option for beginner data scientists new to data analytics. Finally, Zeppelin includes several collaboration features such as integration with GoogleDocs and sharing the notebook URL with critical stakeholders and data stewards.
Apache Spark and Zeppelin
Apache Spark is a plug-in built into the notebook that acts as the primary interpreter for Apache Zeppelin. Spark is an engine for scalable computing, enabling data science professionals to create horizontally scalable projects more easily, i.e., adding multiple machines over time. As an interpreter, Spark is compatible with languages like Python, R, SQL, and Java, so it is an essential tool to program into the Apache Zeppelin notebook interface. With Spark embedded in the interface, data scientists can access various data analytics tools for business intelligence and the creation of data infrastructure. Apache Spark drives data analytics and engineering and is an effective interpreter for training machine learning models, querying SQL databases, and interpreting structured and unstructured data.
Apache Zeppelin Vs. Jupyter Notebook
Notebooks are versatile tools for data science students and professionals and are very popular for their ease of access, capacity for collaboration, and data organization. Jupyter Notebook is an environment created and developed by Jupyter Labs. Like Apache Zeppelin, Jupyter Notebook is a freely available, open-source notebook that data science students and professionals use to display and visualize their workflow in a comprehensible way. Jupyter Notebook is also compatible with multiple programming languages and documents and enables easy sharing and collaboration across data science teams.
The primary difference between Apache Zeppelin and Jupyter Notebook is Jupyter’s popularity. The greatest value of working with open source platforms is the community of users and available resources that complement the product. JupyterLab is used by large tech companies, universities, and schools, so resources are abundant for people interested in using the Jupyter Notebook. And because it’s been around a little longer (Jupyter Notebook was created in 2012 and Zeppelin Notebook in 2013), Jupyter was the first to become established in big industries and integrated into the data science industry.
Why Data Scientists Should Use Apache Zeppelin
Despite the popularity and ease of use of Jupyter Notebook, there are multiple reasons for data science students and professionals to prefer Apache Zeppelin. Apache Zeppelin allows data scientists interested in open-source tools and software development to collaborate on projects. Data is shared via the Notebook URL, and publishing features and changes in the notebook are immediately available to everyone on the team. For teams working on software development, technology, and other data science projects in real-time, collaboration and sharing are essential for managing workflows.
Apache Zeppelin also has a dynamic form feature, making it an essential resource for data scientists. Users create templates with text inputs within the notebook environment. These dynamic forms use the format of notes or paragraphs, with different languages, formats, and levels of security and accessibility. For example, dynamic forms can be programmed with checkboxes, multiple selections, and password protection. Data scientists use dynamic forms to display survey data and enable interactive engagement with the notebook. This feature is unique to Apache Zeppelin, making it the go-to notebook for data science teams working on collaborative projects.
Want More Experience with Data Science Notebooks?
Several different data science notebooks are helpful for beginners and more advanced data scientists. Noble Desktop’s data science classes include training in some of the most popular notebooks and Apache software. For example, the Python Bootcamps use Jupyter Notebook to provide students with hands-on experience in programming and data visualization. The Data Science Certificate also includes experience with Jupyter Notebook, as well as Python and SQL programming languages, making this training program worthwhile for data scientists interested in Apache Zeppelin or Jupyter.