Nowadays, the amount of data grows exponentially, and the more information we see, the harder it gets to process it. That’s why we need data visualization — in charts and dashboards, preferably interactive. It helps us humans save a lot of time and effort to view, analyze, and understand data, and make the right, informed decisions based on that.
Without more ado, let’s go meet the top JS libraries for data visualization!
Free for any use, but all charts will include a small, branded link. To remove the link, you need to purchase a paid license (from $180), which also gives you access to priority support.
AnyChart is a robust, lightweight and feature-rich JS chart library with rendering in SVG/VML. It actually gives web developers a great opportunity to create any different charts that will help to make decisions based on what is seen.
The watermarked version is free. To get rid of the branding, as well as to use AnyChart for any commercial purpose, it’s necessary to buy a license (from $49).
A free open-source JS charts library. Released under the MIT license.
Chartist.js is an open-source, unintrusive JS library which can also be used to create nice responsive charts. Generally, Chartist is good for those who need a very simple chart — line, bar, or pie — and who do not require much in terms of data visualization. Good appearance, no need to have many great features in this case.
Open source, free for all kinds of use.
FusionCharts is another good interactive charting library with hundreds of charts ready for use out of the box. The charts accept both JSON and XML data formats and are rendered via HTML5/SVG or VML.
Free for non-commercial, paid for commercial use (from $497).
Google Charts is an excellent choice for projects that do not require complicated customization and prefer simplicity and stability.
The license is free, but the library is not open source. It does not allow you to host Google’s JS files on your server, so it may not suit you if you have some sensitive data.
Free for use by nonprofits. Paid for commercial use (from $50).
Open-source, free library.
ZingChart is a helpful tool for making interactive and responsive charts. This library is fast and flexible, and allows managing big data and generating charts with large amounts of data with ease.
The branded license provides full access to the ZingChart library for free. Commercial usage requires a paid license (from $199).
Of course, there are some features that make one library faster, more beautiful or flexible than the other. But in the end, no matter what libraries this list contains, the overall winner is always the one that meets your specific requirements. For different people and companies, the choice of the best JS chart library can also be different.
My advice is — check out these top libraries as whenever you need JS charts and for whatever project, chances are extremely high that you will find one or several of them to be the best fit. For a longer list, look at a comparison on Wikipedia.
Hey I think you should also add LightningChart JS to that list.
It comes with loads of features, with fast rendering utilising effective algorithms and GPU acceleration using WebGL. So, one can plot millions of data points – even 100 million data points in real-time, and not have performance issues.
Check this out as they have simple and flexible API’s with a bunch of chart types that offer a lot of customisation, you can create innumerable types of charts to visualise different kinds of data as per your requirements with good visualisations as well !
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.
Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.
Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.
#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation
The DHTMLX diagram library allows creating easily configurable graphs for visualization of hierarchical data. Besides org charts, you can create almost any type of hierarchical diagrams. You can choose from organizational charts, flowcharts, block and network diagrams, decision trees, mind maps, UML Class diagrams, mixed diagrams, and any other types of diagrams. This variety of diagrams can be generated using a built-in set of shapes or with the help of custom shapes.
You can set up any diagram shape you need with text, icons, images, and any other custom content via templates in a few lines of code. All these parameters can be later changed from the UI via the sidebar options in the editor.
The edit mode gives an opportunity to make changes on-the-fly without messing with the source code. An interactive interface of the editor supports drag-and-drop and permits you to change each item of your diagram. You can drag diagram items with your mouse and set the size and position property of an item via the editor. The multiselection feature can help to speed up your work in the editor, as it enables you to manipulate several shapes.
The library has an exporting feature. You can export your diagram to a PDF, PNG, or JSON format. Zooming and scrolling options will be useful in case you work with diagrams containing a big number of items. There is also a search feature that helps you to quickly find the necessary shape and make your work with complex diagrams even more convenient by expanding and collapsing shapes when necessary. To show the structure of an organization compactly, you can use the vertical mode.
The documentation page will appeal both to beginners and experienced developers. A well-written beginner’s guide contains the source code with explanations. A bunch of guides will help with further configuration, so you’ll be able to create a diagram that better suits your needs. At the moment, there are three types of licenses available. The commercial license for the team of five or fewer developers costs $599, the enterprise license goes for $1299 per company, and the ultimate license has a price tag of $2899.
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Data visualization is a fundamental ingredient of data science. It helps us understand the data better by providing insights. We also use data visualization to deliver the results or findings.
Python, being the predominant choice of programming language in the data science ecosystem, offers a rich selection of data visualization libraries. In this article, we will do a practical comparison of 3 popular ones.
The libraries we will cover are Seaborn, Altair, and Plotly. The examples will consist of 3 fundamental data visualization types which are scatter plot, histogram, and line plot.
We will do the comparison by creating the same visualizations with all 3 libraries. We will be using the Melbourne housing dataset available on Kaggle for the examples.
#data-visualization #python #data-science #programming #clash of python data visualization libraries #libraries