Building A Django Middleware (injecting Data into A View's Context)

What is a Django middleware and what is used for?

I had an interesting use case recently where I needed to inject dynamic data into a Django view’s context.

The data didn’t come from the database. I needed to serve different objects depending on the request META HTTP_ACCEPT_LANGUAGE, and to make that data accessible from a JavaScript frontend.

Building a Django middleware has been the natural solution. A Django middleware is like a plug-in that you can hook into the Django’s request/response cycle.

In this post you’ll learn how to build your own Django middleware and how to inject data into a view’s context directly from the middleware.


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Building A Django Middleware (injecting Data into A View's Context)
 iOS App Dev

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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

Ahebwe  Oscar

Ahebwe Oscar


How Django Middleware Works?

How Django Middleware Works?

 April 25, 2021  Deepak@321  0 Comments

Welcome to my Blog, in this article we learn about How Django Middleware Works?

Django Middleware is a lightweight, low-level plugin system that modifies Django’s input and output. It is a framework that integrates Django for the processing of queries and answers. You can use middleware if you want to change the request object.

Django maintains a list of middleware for each project. Middleware allows you to edit requests from the browser before they reach Django, and to view the response from the view before they reach the browser. The middleware is applied in the same order as it is added to the list in the Django settings. If a new Django project has added a number of middlewares, in most cases they cannot be removed. Middleware is a checkmark that modifies the Django query and response objects.

In order for middleware to play a role, it is dependent on other middleware. For example, AuthenticationMiddleware stores the authenticated user session and executes the SessionMiddleware.

#django #django middleware #django middleware works #how django middleware works #structure of middleware in django

Ahebwe  Oscar

Ahebwe Oscar


Django admin full Customization step by step

Welcome to my blog , hey everyone in this article you learn how to customize the Django app and view in the article you will know how to register  and unregister  models from the admin view how to add filtering how to add a custom input field, and a button that triggers an action on all objects and even how to change the look of your app and page using the Django suit package let’s get started.


Custom Titles of Django Admin

Exclude in Django Admin

Fields in Django Admin

#django #create super user django #customize django admin dashboard #django admin #django admin custom field display #django admin customization #django admin full customization #django admin interface #django admin register all models #django customization

Gerhard  Brink

Gerhard Brink


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.


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

Cyrus  Kreiger

Cyrus Kreiger


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