A powerful Data Table Plugin for VueJS

Vue-good-table

An easy to use, clean and powerful data table for VueJS with essential features like sorting, column filtering, pagination and much more - xaksis.github.io/vue-good-table/

Installing

Install with npm:

npm install --save vue-good-table

Import globally in app:

import VueGoodTablePlugin from 'vue-good-table';

// import the styles 
import 'vue-good-table/dist/vue-good-table.css'

Vue.use(VueGoodTablePlugin);

Import into your component

import { VueGoodTable } from 'vue-good-table';

// add to component
components: {
  VueGoodTable,
}

Import into your component using Typescript

// add to component
components: {
  'vue-good-table': require('vue-good-table').VueGoodTable,
}
Example table with grouped rows and column filters

Advanced Screenshot

Features

Upgrade Guide

Hey there! coming from 1.x? find the upgrade guide here

Authors

Download Details:

Author: xaksis

Live Demo: https://xaksis.github.io/vue-good-table/

GitHub: https://github.com/xaksis/vue-good-table

#vuejs #vue #javascript #vue-js

What is GEEK

Buddha Community

A powerful Data Table Plugin for VueJS
Siphiwe  Nair

Siphiwe Nair

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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

Gerhard  Brink

Gerhard Brink

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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 — 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

Siphiwe  Nair

Siphiwe Nair

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Leveraging the Power of Big Data and Small Data

Data has become a catchall expression for organizations. The amount of data filling up the organization through regularly exhausting channels is faltering. Last two years more data has been created in all of earlier history. The speed at which businesses are moving today, combined with the sheer volume of data made by the digitized world, requires other ways to derive value from information.

The expressions “Big Data” and “Small Data” have become popular in the last five to ten years. However, it’s not in every case clear about what both of these terms mean or how they assist us with a better understanding of our customers.

Big Data will be data developed in untold manners, for example, through transactions, clicks, radio-frequency identification readers (RFID), financial data, sensors, and an increasing number of IoT connected devices. Small Data, then again, is the data we assemble through primary research. It isn’t simply assembled from qualitative research -in-home ethnographies, online communities, focus groups, etc – yet in addition from quantitative study research. It’s the place where we ask or notice individuals legitimately to reveal their mentalities, inspirations, and values.

#big data #latest news #leveraging the power of big data and small data #big data and small data #small data #big data

Cyrus  Kreiger

Cyrus Kreiger

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How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling

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Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data