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

Uriah  Dietrich

Uriah Dietrich

1618457700

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data

Marcelle  Smith

Marcelle Smith

1594451700

Angular Route Guard: Implement Route Guard in Angular 10

Angular route guard allows us to grant or remove access to certain parts of the navigation. Another route guard, the CanDeactivate guard, even enable you to prevent a user from accidentally leaving a component with unsaved changes.

Why we need Angular guards

To prevent unauthorized access to certain parts of our navigation, use route guards in Angular.

The client-side route guards like this are not meant to be a security feature. They won’t prevent a smart user from figuring out a way to get to the protected routes.

Such security should be implemented on the server-side. So you need to develop the logic for the server-side, and based on the response, we will change the routes.

Route guards are instead meant as a way to improve the UX for your apps.

Types of routing guards

Route guards in Angular can prevent users from navigating to parts of an app without authorization.

There are 4 route guards available in Angular.

  1. CanActivate: It controls if a route can be activated.
  2. CanActivateChild: It controls if children of a route can be activated.
  3. CanLoad: It controls if a route can even be loaded. This becomes useful for lazy-loaded feature modules. They won’t also load if the guard returns false.
  4. CanDeactivate: It controls if the user can leave a route. Note that this guard doesn’t prevent the user from closing the browser tab or navigating to a different address. It only prevents actions from within the application itself.

To use route guards, consider using component-less routes as this facilitates guarding child routes.

How to Create Guard Service in Angular

To create a service for your guard, type the following command.

ng generate guard your-guard-name

In your guard class, implement the guard you want to use. The following example uses CanActivate to guard the route.

In your guard class, implement the guard you want to use. The following example uses CanActivate to guard the route.export class YourGuard implements CanActivate {
  canActivate(
    next: ActivatedRouteSnapshot,
    state: RouterStateSnapshot): boolean {
      // your  logic goes here
  }
}

#angular #angular 10 #angular route

Ian  Robinson

Ian Robinson

1624399200

Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics