JavaScript Dev

JavaScript Dev

1613858700

Full Featured Data Visualization Library

🍞 πŸ“ˆ Spread your data on TOAST UI Chart. TOAST UI Chart is Beautiful Statistical Data Visualization library.

πŸ“¦ Packages

The functionality of TOAST UI Chart is available when using the Plain JavaScript, React, Vue Component.

πŸ“™ Documents

😍 Why TOAST UI Chart?

Simple, Easy to Use, And It’s Beautiful!

TOAST UI Chart makes your data pop and presents it in a manner that is easy to understand. Furthermore, it provides a wide range of theme options for customizing the charts to be suitable for all of your services. Chart components like the title, axes, legends, tooltips, plots, series, and more can be customized through the options.

image

Variety of powerful features!

Responsive

Add different options and animations according to the charts’ sizes by using the responsive option.

responsive

Zoomable

Make the data presented in the Line, Area, and Treemap Charts zoomable with the zoomable option.

zoomable

Live Update

View and manage new data as they are added realtime with the addData API and the options.series.shift option.

Area Line Heatmap
area live update line heatmap
LineArea Column ColumnLine
lineArea column columnline
Synchronize Tooltip

Use and synchronize the tooltip features at the moment the cursor hovers over the chart with the showTooltip API and the on custom event.

synctooltip

🎨 Features

Charts

The TOAST UI Chart provides many types of charts to visualize the various forms of data.

Area Line Bar Column
area chart line chart bar chart column chart
Bullet BoxPlot Treemap Heatmap
bullet chart boxplot chart treemap chart heatmap chart
Scatter Bubble Radar Pie
scatter chart bubble chart radar chart pie chart
LineArea LineScatter ColumnLine NestedPie
lineArea chart lineScatter chart columnLine chart nestedPie chart
RadialBar
radialBar chart coming soon coming soon next

In addition, a variety of powerful features can be found on the demo page below. πŸ‘‡ πŸ‘‡ πŸ‘‡

🐾 Examples

Here are more examples and play with TOAST UI Chart!

πŸ”§ Pull Request Steps

TOAST UI products are open source, so you can create a pull request(PR) after you fix issues. Run npm scripts and develop yourself with the following process.

Setup

Fork main branch into your personal repository. Clone it to local computer. Install node modules. Before starting development, you should check to have any errors.

$ git clone https://github.com/{your-personal-repo}/tui.chart.git
$ npm install
$ cd apps/chart
$ npm install
$ npm run test

Develop

Let’s start development! You can develop UI through webpack-dev-server or storybook, and you can write tests through Jest. Don’t miss adding test cases and then make green rights.

Run webpack-dev-server
$ npm run serve
Run storybook
$ npm run storybook
Run test
$ npm run test

Pull Request

Before PR, check to test lastly and then check any errors. If it has no error, commit and then push it!

For more information on PR’s step, please see links of Contributing section.

πŸ’¬ Contributing

🌏 Browser Support

Chrome Chrome IE Internet Explorer Edge Edge Safari Safari Firefox Firefox
Yes 10+ Yes Yes Yes

🍞 TOAST UI Family

πŸš€ Used By

Download Details:

Author: nhn

Demo: https://ui.toast.com/tui-chart

Source Code: https://github.com/nhn/tui.chart

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Full Featured Data Visualization Library
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

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

Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

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

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

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 β€” 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.

Introduction

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

Ray  Patel

Ray Patel

1623171540

Clash of Python Data Visualization Libraries

Seaborn, Altair, and Plotly

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

Gerhard  Brink

Gerhard Brink

1624699032

Introduction to Data Libraries for Small Data Science Teams

At smaller companies access to and control of data is one of the biggest challenges faced by data analysts and data scientists. The same is true at larger companies when an analytics team is forced to navigate bureaucracy, cybersecurity and over-taxed IT, rather than benefit from a team of data engineers dedicated to collecting and making good data available.

Creative, persistent analysts find ways to get access to at least some of this data. Through a combination of daily processes to save email attachments, run database queries, and copy and paste from internal web pages one might build up a mighty collection of data sets on a personal computer or in a team shared drive or even a database.

But this solution does not scale well, and is rarely documented and understood by others who could take it over if a particular analyst moves on to a different role or company. In addition, it is a nightmare to maintain. One may spend a significant part of each day executing these processes and troubleshooting failures; there may be little time to actually use this data!

I lived this for years at different companies. We found ways to be effective but data management took up way too much of our time and energy. Often, we did not have the data we needed to answer a question. I continued to learn from the ingenuity of others and my own trial and error, which led me to the theoretical framework that I will present in this blog series: building a self-managed data library.

A data library is _not _a data warehousedata lake, or any other formal BI architecture. It does not require any particular technology or skill set (coding will not be required but it will greatly increase the speed at which you can build and the degree of automation possible). So what is a data library and how can a small data analytics team use it to overcome the challenges I’ve described?

#big data #cloud & devops #data libraries #small data science teams #introduction to data libraries for small data science teams #data science