Data plays an integral role in businesses across industries. Leveraging data is a powerful tool that provides organizations with insights into past trends, observations about current operations, and projections about expected outcomes. Two of the leading data analytics platforms, ThoughSpot and Jupyter Notebook, are widely used for data analysis and visualization. But how can you decide which one is right for your organization’s needs? This article will explore the functions of each platform, as well as the pros and cons of working with each.
What is ThoughtSpot?
Traditional data analysis typically involves a steady back and forth between Data Scientists and the teams working with them to devise which question will spring from a prior one. Each step of the game requires its own visualization of findings. Thanks to ThoughtSpot, this is no longer the case. Employees can have actionable information in the moment rather than waiting for months of research, visualizations, and results.
ThoughtSpot represents the future of data analytics. This business intelligence tool moves queries from the silo and into the search box. Answers to queries can be found quickly by entering a question into the search bar. ThoughtSpot uses a relational search engine to instantaneously extract pertinent findings from data. It then uses the search terms to create a visualization of this information. This allows all users, even those without technical training or a background in coding, to create dashboards and reports in just a few seconds. The metrics created are able to be shared across a company so that everyone can have access to this information.
In 2020, ThoughtSpot reported a 108% growth in revenue. This rapid growth is in part due to ThoughtSpot’s dedication to next-generation analytics endeavors, such as the integration of AI, search, and cloud-based applications.
Uses for ThoughtSpot
ThoughtSpot has a variety of uses and features for managing and visualizing data. Some of its most popular capabilities pertain to:
- Replicating and sharing data
- Keyword validation in real-time
- Transparency of queries
- Automated insights
- Search suggestions
- Pinboard and chart creation
- Drag-and-drop feature
These features allow Data Scientists and Data Analysts to streamline the process of data analysis and subsequent visualizations.
Pros & Cons of ThoughtSpot
Those working with ThoughtSpot cite both benefits and drawbacks to using this software.
Pros:
- Single BI solution: ThoughSpot users do not have to combine multiple platforms when gathering data, but can search for and locate information all on one platform.
- Unlimited data scaling: As your business or organization grows, ThoughtSpot is able to support unlimited data scaling. Solutions can be easily scaled up or down. In addition, this application’s servers use automatic load balancing to ensure their best performance.
- Powerful search: This search-driven analytic software allows users to easily and quickly locate necessary data using a search bar function.
- Automated insights: With one mouse click, businesses can access automated insights.
Cons:
- The lack of customizations for visualizations is a drawback for those working with data analytics.
- There aren’t many ThoughSpot tutorials, since this application is not widely used.
- Due to bugs, ThoughtSpot users often have to reach out for support to fix issues.
Despite the drawbacks to using ThoughtSpot for data analysis and visualization, the ability to work in a single platform and perform search-driven analytics are reasons many users gravitate toward ThoughtSpot.
What is Jupyter Notebook?
Jupyter Notebook is a free, open-source web application with which users can create and share documents containing visualizations, equations, text, and live code. It can document and run code, review the outcome, and create a data visualization based on the insights gathered without having to leave the environment. Jupyter Notebook is a powerful tool for cleaning and transforming data.
One of the main advantages of Jupyter Notebook is that users can view and execute code in the same user interface, which allows them to make changes to code and to see the results in real-time. Even though computational notebooks have been around for some time, Jupyter Notebook’s streamlined interface and customizable features have helped it grow in popularity over the past several years.
The Jupyter family of products extends beyond Jupyter Notebook to offer several other environments for data manipulation and visualization:
- Project Jupyter: This organization focuses on developing open standards, open-source software, and services that support interactive computing in several dozens of programming languages. It was created to support various interactive computing products, such as Jupyter Notebook, JupyterLab, and JupyterHub.
- JupyterLab: It provides users with an interactive, web-based environment that works with various activities and documents, including terminals, text editors, and Jupyter Notebook. Its flexible design enables those working with data to arrange or configure the user interface to support various workflows in an integrated manner. Users can place activities or documents side by side and integrate them, which can lead to new workflows for interactive computing projects.
- JupyterHub: It offers users access to a plethora of resources and computational environments without having to perform installations or maintenance tasks. Work can be completed in one’s own workspace within a shared resource, which system administrators can then oversee. This tool is scalable and customizable, and provides Data Scientists, researchers, or students with the necessary tools to efficiently complete projects.
Uses for Jupyter Notebook
Jupyter Notebook has applications in a variety of industries, in particular those focused on data science and data analytics. Data Analysts most commonly use Jupyter Notebook for the following tasks:
- Numerical simulations
- Narrative text
- Statistical modeling
- Exploratory analysis
- Data visualization
- Data cleaning
By using these features, Data Analysts can easily transform raw data from numbers into a visual story.
Pros & Cons of Jupyter Notebook
Users cite many benefits, as well as a few drawbacks, to working with Jupyter Notebook.
Pros:
- Its cell-based approach is especially effective for prototyping.
- The server side is easy to host, which provides additional security.
- Users can showcase their work, including code as well as the results.
- One user can use another’s work as a starting point for their own efforts.
- It is both platform-independent as well as language-independent. It can be processed by several languages, as well as converted to any file format, such as PDF or HTML.
- It is especially helpful for data visualizations. The shared notebook allows users to narrate visualizations, as well as share datasets and code, which makes it a great tool for projects that require interactive changes.
Cons:
- Because Jupyter Notebook code is written in cells rather than functions/classes/objects, duplicate code is often created.
- Collaborative coding efforts are problematic because it’s easy to get out of synch when copying parts from one another’s work.
While it’s important to note the disadvantages to working with Jupyter Notebook, most users cite its many advantages as reasons to continue using this tool.
The Bottom Line
Both ThoughtSpot and Jupyter Notebook offer many cutting-edge features and tools that are designed to handle large datasets. For those who are looking to streamline the querying and visualization process, ThoughtSpot is for you. This tool creates reports and dashboards in seconds, which can be shared company-wide. On the other hand, for users who wish to perform all their tasks on the same user interface, from running code to generating stunning visualizations, Jupyter Notebook provides an effective and efficient platform.
Whether your organization chooses to work with Jupyter Notebook or ThoughtSpot, or perhaps consider a different BI platform such as Tableau or Qlik for data-handling needs, the good news is that many options are available.
Hands-On Data Analytics & Data Science Classes
If you’re interested in discovering how to use Jupyter Notebook, as well as other tools for managing and visualizing big data, Noble Desktop’s data science classes provide a great educational opportunity. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. data analytics courses are also available through Noble for those with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and Data Analytics.
Another great way to learn more about the tools used in data analytics and data science is to enroll in one of Noble Desktop’s in-person or live online Python classes. In these classes, industry experts help students master the most powerful programming language.
Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.
For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs. In addition, more than three dozen Python bootcamps can also be found using this tool.