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

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 — 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

Paula  Hall

Paula Hall

1624431580

How to add a new column to Pandas DataFrame?

In this tutorial, we are going to discuss different ways to add a new column to pandas data frame.


Table of Contents

What is a pandas data frame?

Pandas data frameis a two-dimensional heterogeneous data structure that stores the data in a tabular form with labeled indexes i.e. rows and columns.

Usually, data frames are used when we have to deal with a large dataset, then we can simply see the summary of that large dataset by loading it into a pandas data frame and see the summary of the data frame.

In the real-world scenario, a pandas data frame is created by loading the datasets from an existing CSV file, Excel file, etc.

But pandas data frame can be also created from the listdictionary, list of lists, list of dictionaries, dictionary of ndarray/lists, etc. Before we start discussing how to add a new column to an existing data frame we require a pandas data frame.

#pandas #dataframe #pandas dataframe #column #add a new column #how to add a new column to pandas dataframe

I am Developer

1599536794

Laravel 8 New Features | Release Notes - Tuts Make

In this post, i will show you what’s new in laravel 8 version.

#What’s new in Laravel 8?

  • 1 - Change Path Of Default Models Directory
  • 2 - Removed Controllers Namespace Prefix
  • 3 - Enhancements on php artisan serve
  • 4 - Enhanced Rate Limiting
  • 5 - Enhanced on Route Caching
  • 6 - Update on Pagination Design
  • 8 - Dynamic Blade Componenets
  • 7 - Update Syntax for Closure Based Event Listeners
  • 8 - Queueable Model Event Listeners
  • 9 - Maintenance mode: secret access
  • 10 - Maintenance mode: pre-rendered page
  • 11 - Queued job batching
  • 12 - Queue backoff()
  • 13 - Laravel Factory

https://www.tutsmake.com/laravel-8-new-features-release-notes/

#laravel 8 features #laravel 8 release date #laravel 8 tutorial #news - laravel 8 new features #what's new in laravel 8 #laravel 8 release notes

I am Developer

1597489654

Laravel - Dynamically Add or Remove Input Fields

In this post, i will share with you how to dynamically add/remove input fields in laravel forms.

As well as, dynamically add/remove input field and save data to database laravel.

Laravel – Add/remove input field Dynamically with Jquery

Dynamically add/remove input fields using submit form with jQuery ajax with validation and store fields into database in laravel:

  1. Step 1: Install Laravel App
  2. Step 2: Add Database Details
  3. Step 3: Create Migration & Model
  4. Step 4: Add Routes
  5. Step 5: Create Controller by Artisan
  6. Step 6: Create Blade View
  7. Step 7: Run Development Server

https://www.tutsmake.com/laravel-dynamically-add-or-remove-input-fields-jquery/

#dynamically add input fields and save data to database laravel #laravel - dynamically add or remove input fields using jquery #add/remove input fields dynamically with jquery laravel #add remove input fields dynamically with jquery and submit to database in laravel