In the simplest terms, data visualization refers to how we represent our data with visuals like charts, graphs, and so on. It is the representation of data in graphical format, usually a mapping or a relationship between data points and lines of a chart or graph. Generally, it entails designing the right visual interface for our data.
The form of the visualization usually depends on the relationship between the chosen graphical elements or types and the data points involved. The end goal is to communicate information derived from data sources clearly and effectively via visual or graphical means.
Data viz, as it is sometimes called, is particularly useful to help us tell stories about our data and, in the process, understand the representation of a large set of data point at a glance. Essentially, representing data in an aesthetically pleasing and easily consumable way are the major goals of data viz.
We’ll compare their pros and cons, key features and functionalities, philosophy, and so on. At the end of the day, the goal is to understand why we might pick one of these visualization libraries based on their available features and our particular use case.
Now let’s highlight these libraries in turn below
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
Technology has taken a place of more productiveness and give the best to the world. In the current situation, everything is done through the technical process, you don’t have to bother about doing task, everything will be done automatically.This is an article which has some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top trending technologies followed in 2021 and its impression in the coming future in the world.
First in the list of newest technologies is surprisingly Data Science. Data Science is the automation that helps to be reasonable for complicated data. The data is produces in a very large amount every day by several companies which comprise sales data, customer profile information, server data, business data, and financial structures. Almost all of the data which is in the form of big data is very indeterminate. The character of a data scientist is to convert the indeterminate datasets into determinate datasets. Then these structured data will examine to recognize trends and patterns. These trends and patterns are beneficial to understand the company’s business performance, customer retention, and how they can be enhanced.
Next one is DevOps, This technology is a mixture of two different things and they are development (Dev) and operations (Ops). This process and technology provide value to their customers in a continuous manner. This technology plays an important role in different aspects and they can be- IT operations, development, security, quality, and engineering to synchronize and cooperate to develop the best and more definitive products. By embracing a culture of DevOps with creative tools and techniques, because through that company will gain the capacity to preferable comeback to consumer requirement, expand the confidence in the request they construct, and accomplish business goals faster. This makes DevOps come into the top 10 trending technologies.
Next one is Machine learning which is constantly established in all the categories of companies or industries, generating a high command for skilled professionals. The machine learning retailing business is looking forward to enlarging to $8.81 billion by 2022. Machine learning practices is basically use for data mining, data analytics, and pattern recognition. In today’s scenario, Machine learning has its own reputed place in the industry. This makes machine learning come into the top 10 trending technologies. Get the best machine learning course and make yourself future-ready.
To want to know more click on Top 10 Trending Technologies in 2021
You may also read more blogs mentioned below
#top trending technologies #top 10 trending technologies #top 10 trending technologies in 2021 #top trending technologies in 2021 #top 5 trending technologies in 2021 #top 5 trending technologies
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
In today’s tech world, data is everything. As the focus on data grows, it keeps multiplying by leaps and bounds each day. If earlier mounds of data were talked about in kilobytes and megabytes, today terabytes have become the base unit for organizational data. This coming in of big data has transformed paradigms of data storage, processing, and analytics.
Instead of only gathering and storing information that can offer crucial insights to meet short-term goals, an increasing number of enterprises are storing much larger amounts of data gathered from multiple resources across business processes. However, all this data is meaningless on its own. It can add value only when it is processed and analyzed the right way to draw point insights that can improve decision-making.
Processing and analyzing big data is not an easy task. If not handled correctly, big data can turn into an obstacle rather than an effective solution for businesses. Effective handling of big data management requires to use of tools that can steer you toward tangible, substantial results. For that, you need a set of great big data tools that will not only solve this problem but also help you in producing substantial results.
Data storage tools, warehouses, and data lakes all play a crucial role in helping companies store and sort vast amounts of information. However, the true power of big data lies in its analytics. There are a host of big data tools in the market today to aid a business’ journey from gathering data to storing, processing, analyzing, and reporting it. Let’s take a closer look at some of the top big data tools that can help you inch closer to your goal of establishing data-driven decision-making and workflow processes.
#big data #big data tools #big data management #big data tool #top 10 big data tools for 2021! #top-big-data-tool
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.