Working with various Big Data analytics systems necessitates the use of Big Data visualization. Decision-making becomes considerably easier if the flow of raw data is represented with visuals. In this blog, we discuss Big Data visualization tools.
To meet and surpass the customer’s expectations, Big Data visualization tools should provide the following features:
- Capability to process a variety of incoming data types.
- Capability to apply numerous filters to change the results
- Possibility of interacting with data sets during analysis
- Capability to link to other software in order to receive or supply input to it.
- Capability to give users collaborative possibilities
Despite the fact that there are a plethora of specialized tools for Big Data visualization, both open-source and proprietary, there is a group of them that stands out significantly, as they include all or any of the aforementioned aspects. We will cover four of the most popular tools for Big Data visualization to assist you in selecting the best fit for your situation.
Jupyter Notebook
Jupyter Notebook is a one-stop shop for Big Data visualization.
JupyteR is an open-source project that enables Big Data research, visualization, and real-time software development collaboration spanning more than a dozen programming languages. The interface contains a field for code entry, and the tool executes the code to produce the visually-readable image based on the visualization style selected.
This, however, is only the tip of the iceberg. Jupyter notebooks can be shared across team members to facilitate internal collaboration and improve teamwork on data processing. Jupyter’s flexibility to connect with numerous frameworks, such as Spark, transforms it into an all-around capable solution for processing data from huge, data-intensive applications with disparate input sources.
Tableau
Tableau is the greatest tool for visualizing AI, Big Data, and Machine Learning applications. Tableau is a global leader in Big Data visualization, particularly for enabling interactive data visualization for the outputs of Big Data operations, deep learning algorithms, and various sorts of AI-driven apps.
Tableau can be linked with Amazon Web Services (AWS), MySQL, Hadoop, Teradata, and SAP, giving it a versatile tool for creating precise graphs and intuitive data visualization. As a result, the C-suite and middle-chain managers may make informed decisions based on insightful and easily understandable Tableau graphs.
Google Chart
Google chart: a free and strong integration of all Google’s capabilities.
Google is now synonymous with leadership, and just as Google Chrome is unquestionably the most popular browser available, Google Chart is among the best solutions for big data visualization, not to mention it is completely free and receives enthusiastic support from Google. Why is this so? Because the data parsed by this system is plainly utilized to train Google’s AI, it’s a win-win situation for all stakeholders.
Google Chart provides a wide range of visualization formats, ranging from simple pie charts and time series to multi-dimensional interactive matrices. There are several adjustment options available, and a full help section is provided if significant customization is required.
D3.js
D3.js may be used to visualize Big Data in virtually any way.
D3.js is an abbreviation for Data-Driven Document, a JS toolkit enabling interactive Big Data presentation in basically ANY real-time fashion. Given that this is not a tool, a user should have a basic understanding of Javascript in order to work with the data and present it in a human-readable format. To elaborate, because this library renders the data in SVG and HTML5 formats, earlier browsers such as IE7 and 8 are unable to take advantage of D3.js features.
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