#install mongodb

[arashtadstudio]

How to Install and Uninstall MongoDB on Linux Debian 12

[learntechsolution]

Django provides a signals framework that allows certain senders to notify a set of receivers when certain actions occur. This can be very useful for decoupling components of your application and keeping your code modular. Django's built-in signals can be used for model events by utilizing the django.db.models.signals module. Here's a step-by-step guide on how to use Django signals for model events

[learn-techsolution]

Django provides a signals framework that allows certain senders to notify a set of receivers when certain actions occur. This can be very useful for decoupling components of your application and keeping your code modular. Django's built-in signals can be used for model events by utilizing the django.db.models.signals module. Here's a step-by-step guide on how to use Django signals for model events

[learntech-solution]

Django provides a signals framework that allows certain senders to notify a set of receivers when certain actions occur. This can be very useful for decoupling components of your application and keeping your code modular. Django's built-in signals can be used for model events by utilizing the django.db.models.signals module. Here's a step-by-step guide on how to use Django signals for model events

[learn-tech-solution]

Django provides a signals framework that allows certain senders to notify a set of receivers when certain actions occur. This can be very useful for decoupling components of your application and keeping your code modular. Django's built-in signals can be used for model events by utilizing the django.db.models.signals module. Here's a step-by-step guide on how to use Django signals for model events

[virtualizationhowto]

Graylog Docker Compose Setup: An Open Source Syslog Server for Home Labs

Graylog Docker Compose Install: Open Source Syslog Server for Home #homelab GraylogInstallationGuide #DockerComposeOnUbuntu #GraylogRESTAPI #ElasticsearchAndGraylog #MongoDBWithGraylog #DockerComposeYmlConfiguration #GraylogDockerImage #Graylogdata

A really great open-source log management platform for both production and home lab environments is Graylog. Using Docker Compose, you can quickly launch and configure Graylog for a production or home lab Syslog. Using Docker Compose, you can create and configure all the containers needed, such as OpenSearch and MongoDB. Let’s look at this process. Table of contentsWhat is Graylog?Advantages of…

[full-stack1]

Certainly! Let’s explore how to build a full-stack application using Node.js. In this comprehensive guide, we’ll cover the essential components and steps involved in creating a full-stack web application.

Building a Full-Stack Application with Node.js, Express, and MongoDB

1. Node.js: The Backbone of Our Application

Node.js is a runtime environment that allows us to run JavaScript on the server-side.

It’s built on Chrome’s V8 JavaScript engine and uses an event-driven, non-blocking I/O model, making it lightweight and efficient.

Node.js serves as the backbone of our application, providing the environment in which our server-side code will run.

2. Express.js: Simplifying Server-Side Development

Express.js is a minimal and flexible Node.js web application framework.

It provides a robust set of features for building web and mobile applications.

With Express.js, we can:

Set up middlewares to respond to HTTP requests.

Define routing rules.

Add additional features like template engines.

3. MongoDB: Storing Our Data

MongoDB is a document-oriented database program.

It uses JSON-like documents with optional schemas and is known for its flexibility and scalability.

We’ll use MongoDB to store our application’s data in an accessible and writable format.

Building Our Full-Stack Application: A Step-by-Step Guide

Setting Up the Environment:

Install Node.js:sudo apt install nodejs

Initialize a new Node.js project:mkdir myapp && cd myapp npm init -y

Install Express.js:npm install express

Creating the Server:

Create a basic Express server:const express = require('express'); const app = express(); const port = 3000; app.get('/', (req, res) => { res.send('Hello World!'); }); app.listen(port, () => { console.log(`Server running at http://localhost:${port}`); });

Defining Routes:

Define routes for different parts of our application:app.get('/user', (req, res) => { res.send('User Page'); });

Connecting to MongoDB:

Use Mongoose (a MongoDB object modeling tool) to connect to MongoDB and handle data storage.

Remember, this is just the beginning! Full-stack development involves frontend (client-side) work as well. You can use React, Angular, or other frontend libraries to build the user interface and connect it to your backend (Node.js and Express).

Feel free to explore more about each component and dive deeper into building your full-stack application! 😊 12

[meandertraining]

Importance of MERN Stack

What is MERN Stack?

Four essential technologies make up the full-stack JavaScript framework MERN Stack:

MongoDB: A NoSQL database system known for its flexibility and scalability, MongoDB stores data in a JSON-like format, making it ideal for handling large volumes of data.

Express.js: A minimalist web application framework for Node.js, Express.js simplifies the process of building robust and scalable web applications by providing a set of features for web and mobile applications.

React.js: Developed by Facebook, React.js is a powerful JavaScript library for building interactive user interfaces. Its component-based architecture allows developers to create reusable UI components, resulting in a more modular and maintainable codebase.

Node.js: A server-side JavaScript runtime environment, Node.js enables developers to build fast and scalable network applications. With its event-driven, non-blocking I/O model, Node.js is well-suited for building real-time web applications.

Why Choose MERN Stack?

Streamlined Development: With MERN Stack, developers can leverage the power of JavaScript across the entire development stack, from frontend to backend. This unified approach reduces development time and eliminates the need to switch between different programming languages and frameworks.

SEO-Friendly Architecture: MERN Stack's server-side rendering capabilities, coupled with its support for modern JavaScript frameworks like React.js, make it highly SEO-friendly. This ensures that web applications built with MERN Stack are easily discoverable by search engines, leading to improved search engine rankings and increased organic traffic.

Optimized Performance: MERN Stack's asynchronous, non-blocking architecture allows for seamless communication between frontend, backend, and database components, resulting in faster response times and improved performance. This translates to a smoother user experience and higher customer satisfaction.

Improved Security: In today's digital environment, security is of the highest priority. MERN Stack provides built-in security features, such as authentication and authorization mechanisms, as well as support for encryption and data validation, to ensure that web applications are protected against common security threats.

Scalability and Flexibility: Whether you're building a small-scale application or a large-scale enterprise solution, MERN Stack offers the scalability and flexibility you need to grow and adapt to changing business requirements. With its modular architecture and support for microservices, MERN Stack allows for easy scaling and maintenance of complex applications.

Getting Started with MERN Stack

Are you prepared to explore the MERN Stack world? Here is a detailed how-to for getting started:

Install Node.js: Begin by installing Node.js, which includes npm (Node Package Manager), on your local machine. Node.js will serve as the runtime environment for your server-side code.

Set Up MongoDB: Install MongoDB, a NoSQL database system, and set up a local or remote MongoDB instance to store your application data.

Create an Express.js Server: Use Express.js to create a server-side application that will handle HTTP requests and serve as the backend for your web application.

Build Your React.js Frontend: Use React.js to create a client-side application that will handle user interface interactions and communicate with the backend server.

Integrate MongoDB with Express.js: Connect your Express.js server to your MongoDB database using Mongoose, a MongoDB object modeling tool for Node.js.

Deploy Your Application: Once your application is complete, deploy it to a hosting provider of your choice to make it accessible to users worldwide.

Conclusion

MERN Stack offers a powerful and versatile framework for building modern web applications that are fast, scalable, and secure. Whether you're a seasoned developer or just getting started, MERN Stack provides the tools and resources you need to bring your ideas to life. So why wait? Start exploring the endless possibilities of MERN Stack today and unlock the potential of your web development projects with Meander Training, Meander training is a platform where you can start your web development journey, it provides industrial training with certification.

[datavalleyai]

Azure Data Engineering Tools For Data Engineers

Azure is a cloud computing platform provided by Microsoft, which presents an extensive array of data engineering tools. These tools serve to assist data engineers in constructing and upholding data systems that possess the qualities of scalability, reliability, and security. Moreover, Azure data engineering tools facilitate the creation and management of data systems that cater to the unique requirements of an organization.

In this article, we will explore nine key Azure data engineering tools that should be in every data engineer’s toolkit. Whether you’re a beginner in data engineering or aiming to enhance your skills, these Azure tools are crucial for your career development.

Microsoft Azure Databricks

Azure Databricks is a managed version of Databricks, a popular data analytics and machine learning platform. It offers one-click installation, faster workflows, and collaborative workspaces for data scientists and engineers. Azure Databricks seamlessly integrates with Azure’s computation and storage resources, making it an excellent choice for collaborative data projects.

Microsoft Azure Data Factory

Microsoft Azure Data Factory (ADF) is a fully-managed, serverless data integration tool designed to handle data at scale. It enables data engineers to acquire, analyze, and process large volumes of data efficiently. ADF supports various use cases, including data engineering, operational data integration, analytics, and data warehousing.

Microsoft Azure Stream Analytics

Azure Stream Analytics is a real-time, complex event-processing engine designed to analyze and process large volumes of fast-streaming data from various sources. It is a critical tool for data engineers dealing with real-time data analysis and processing.

Microsoft Azure Data Lake Storage

Azure Data Lake Storage provides a scalable and secure data lake solution for data scientists, developers, and analysts. It allows organizations to store data of any type and size while supporting low-latency workloads. Data engineers can take advantage of this infrastructure to build and maintain data pipelines. Azure Data Lake Storage also offers enterprise-grade security features for data collaboration.

Microsoft Azure Synapse Analytics

Azure Synapse Analytics is an integrated platform solution that combines data warehousing, data connectors, ETL pipelines, analytics tools, big data scalability, and visualization capabilities. Data engineers can efficiently process data for warehousing and analytics using Synapse Pipelines’ ETL and data integration capabilities.

Microsoft Azure Cosmos DB

Azure Cosmos DB is a fully managed and server-less distributed database service that supports multiple data models, including PostgreSQL, MongoDB, and Apache Cassandra. It offers automatic and immediate scalability, single-digit millisecond reads and writes, and high availability for NoSQL data. Azure Cosmos DB is a versatile tool for data engineers looking to develop high-performance applications.

Microsoft Azure SQL Database

Azure SQL Database is a fully managed and continually updated relational database service in the cloud. It offers native support for services like Azure Functions and Azure App Service, simplifying application development. Data engineers can use Azure SQL Database to handle real-time data ingestion tasks efficiently.

Microsoft Azure MariaDB

Azure Database for MariaDB provides seamless integration with Azure Web Apps and supports popular open-source frameworks and languages like WordPress and Drupal. It offers built-in monitoring, security, automatic backups, and patching at no additional cost.

Microsoft Azure PostgreSQL Database

Azure PostgreSQL Database is a fully managed open-source database service designed to emphasize application innovation rather than database management. It supports various open-source frameworks and languages and offers superior security, performance optimization through AI, and high uptime guarantees.

Whether you’re a novice data engineer or an experienced professional, mastering these Azure data engineering tools is essential for advancing your career in the data-driven world. As technology evolves and data continues to grow, data engineers with expertise in Azure tools are in high demand. Start your journey to becoming a proficient data engineer with these powerful Azure tools and resources.

Unlock the full potential of your data engineering career with Datavalley. As you start your journey to becoming a skilled data engineer, it’s essential to equip yourself with the right tools and knowledge. The Azure data engineering tools we’ve explored in this article are your gateway to effectively managing and using data for impactful insights and decision-making.

To take your data engineering skills to the next level and gain practical, hands-on experience with these tools, we invite you to join the courses at Datavalley. Our comprehensive data engineering courses are designed to provide you with the expertise you need to excel in the dynamic field of data engineering. Whether you’re just starting or looking to advance your career, Datavalley’s courses offer a structured learning path and real-world projects that will set you on the path to success.

Course format:

Subject: Data Engineering Classes: 200 hours of live classes Lectures: 199 lectures Projects: Collaborative projects and mini projects for each module Level: All levels Scholarship: Up to 70% scholarship on this course Interactive activities: labs, quizzes, scenario walk-throughs Placement Assistance: Resume preparation, soft skills training, interview preparation

Subject: DevOps Classes: 180+ hours of live classes Lectures: 300 lectures Projects: Collaborative projects and mini projects for each module Level: All levels Scholarship: Up to 67% scholarship on this course Interactive activities: labs, quizzes, scenario walk-throughs Placement Assistance: Resume preparation, soft skills training, interview preparation

For more details on the Data Engineering courses, visit Datavalley’s official website.

[ajpandey1]

Amazon Web Service & Adobe Experience Manager:- A Journey together (Part-2)

In the fist part we discussed how one day digital market leader meet with the a friend AWS in the Cloud and become very popular pair. Also what gift they bring for the digital marketing persons.

Now AEM asked to come to my home.

So AEM insides about its parts and structure explored.

AEM Platform :

AUTHOR:-

The content and layout of an AEM experience are created and managed in the author environment. It offers features for authoring content modifications, reviewing them, and publishing the approved versions of the content to the publish environment.

PUBLISH:-

The audience receives the experience from publishing environment. With the option to customize the experience based on demographics or targeted messaging, it renders the actual pages.

Both AUTHOR and PUBLISH instances are Java web applications that have identical installed software. They are differentiated by configuration only.

DISPATCHER:-

Dispatcher environment is the responsible for caching (storing) content and Load balancing.Helps realize a fast & dynamic web authoring environment.

Mainly dispatcher works as part of HTTP server like Apache HTTP. It store as much as possible static content according to specified rules.

So end user feel faster accessing of content and reducing load of PUBLISH. The dispatcher places the cached documents in the document root of the web server.

How AEM Store Content in Repository:-

AEM is storing data without any discrimination as it treated all the family member (data) are content only . Its following philosophy of "everything is content" and stored in the same house(Repository).

Its called CRX i.e. implementation of JCR coming from parent Content Repository API for Java and Apache Jackrabbit Oak.

The basement(base) of the building is driven by MK MicroKernels as in the picture its Tar or MongoDB. The Oak storage layer provides an abstraction layer for the actual storage of the content. MK act as driver or persistence layer here. two way of storing content , TAR MK and MongoDB MK.

TAR--> tar files-->segments

The Tar storage uses tar files. It stores the content as various types of records within larger segments. Journals are used to track the latest state of the repository.

MongoDB-->MongoDB database-->node

MongoDB storage leverages its sharding and clustering feature. The repository tree is stored in one MongoDB database where each node is a separate document.

Tar MicroKernel (TarMK)--for-->Performance

MongoDB--for-->scalability

For Publish instances, its always recommended to go with TarMK.

In more than one Publish instance each running on its own Tar MK then this combination is called TarMK Farm. This is the default deployment for publish environments.

Author instance is having freedom to go with either TAR or MongoDB. it depends on the requirement, if its performance oriented and limited number then it can go with the TarMK but if it require more scalable instances then it would go with the MongoDB. TarMK for a single author instance or MongoDB when horizontal scaling.

Now story of TarMK with Author, a cold standby TarMK instance can be configured in another availability zone to provide backup as fail-over.

TarMK is the default persistence system in AEM for both instances, but it can go with different persistent manger (MongoDB).

Gift of TarMK:-performance-optimized,for typical JCR use cases and is very fast, uses an industry-standard data format, can quickly and easily backed up, high performance and reliable data storage, minimal operational overhead and lower total cost of ownership (TCO).

Now story of MongoDB it basically come into picture when more hands required, means more user/author (more than

1,000 users/day, 100 concurrent users)and high volumes of page edits required. To accommodate these horizontal scalability required and solution is with MongoDB. It leverage MongoDB features like high availability, redundancy and automated fail-overs.

MongoDB MK can give lower performance in some scenario as its establish external connection with MongoDB.

A minimum deployment with MongoDB typically involves a

MongoDB replica consisting of

1)one primary node

2)two secondary nodes,

with each node running in its separate availability zone.

AEM--store--binary data--into ---data store.

AEM--store--content data--into ---node store.

And both stored independently.

Amazon Simple Storage Service (Amazon S3) is best high performant option for shared datastore between publish and author instances to store binary files(Assets like image etc).

Continue....

[hindintech]

You can learn NodeJS easily, Here's all you need:

1.Introduction to Node.js

• JavaScript Runtime for Server-Side Development

• Non-Blocking I/0

2.Setting Up Node.js

• Installing Node.js and NPM

• Package.json Configuration

• Node Version Manager (NVM)

3.Node.js Modules

• CommonJS Modules (require, module.exports)

• ES6 Modules (import, export)

• Built-in Modules (e.g., fs, http, events)

4.Core Concepts

• Event Loop

• Callbacks and Asynchronous Programming

• Streams and Buffers

5.Core Modules

• fs (File Svstem)

• http and https (HTTP Modules)

• events (Event Emitter)

• util (Utilities)

• os (Operating System)

• path (Path Module)

6.NPM (Node Package Manager)

• Installing Packages

• Creating and Managing package.json

• Semantic Versioning

• NPM Scripts

7.Asynchronous Programming in Node.js

• Callbacks

• Promises

• Async/Await

• Error-First Callbacks

8.Express.js Framework

• Routing

• Middleware

• Templating Engines (Pug, EJS)

• RESTful APIs

• Error Handling Middleware

9.Working with Databases

• Connecting to Databases (MongoDB, MySQL)

• Mongoose (for MongoDB)

• Sequelize (for MySQL)

• Database Migrations and Seeders

10.Authentication and Authorization

• JSON Web Tokens (JWT)

• Passport.js Middleware

• OAuth and OAuth2

11.Security

• Helmet.js (Security Middleware)

• Input Validation and Sanitization

• Secure Headers

• Cross-Origin Resource Sharing (CORS)

12.Testing and Debugging

• Unit Testing (Mocha, Chai)

• Debugging Tools (Node Inspector)

• Load Testing (Artillery, Apache Bench)

13.API Documentation

• Swagger

• API Blueprint

• Postman Documentation

14.Real-Time Applications

• WebSockets (Socket.io)

• Server-Sent Events (SSE)

• WebRTC for Video Calls

15.Performance Optimization

• Caching Strategies (in-memory, Redis)

• Load Balancing (Nginx, HAProxy)

• Profiling and Optimization Tools (Node Clinic, New Relic)

16.Deployment and Hosting

• Deploying Node.js Apps (PM2, Forever)

• Hosting Platforms (AWS, Heroku, DigitalOcean)

• Continuous Integration and Deployment-(Jenkins, Travis CI)

17.RESTful API Design

• Best Practices

• API Versioning

• HATEOAS (Hypermedia as the Engine-of Application State)

18.Middleware and Custom Modules

• Creating Custom Middleware

• Organizing Code into Modules

• Publish and Use Private NPM Packages

19.Logging

• Winston Logger

• Morgan Middleware

• Log Rotation Strategies

20.Streaming and Buffers

• Readable and Writable Streams

• Buffers

• Transform Streams

21.Error Handling and Monitoring

• Sentry and Error Tracking

• Health Checks and Monitoring Endpoints

22.Microservices Architecture

• Principles of Microservices

• Communication Patterns (REST, gRPC)

• Service Discovery and Load Balancing in Microservices

[learntechsolution]

Adding authentication to Django REST Framework (DRF) views involves configuring authentication classes in your DRF settings. DRF provides a variety of authentication classes that you can choose from based on your project's requirements. Here's a general guide on how to add authentication to DRF views

[learn-techsolution]

Adding authentication to Django REST Framework (DRF) views involves configuring authentication classes in your DRF settings. DRF provides a variety of authentication classes that you can choose from based on your project's requirements. Here's a general guide on how to add authentication to DRF views

[learntech-solution]

Adding authentication to Django REST Framework (DRF) views involves configuring authentication classes in your DRF settings. DRF provides a variety of authentication classes that you can choose from based on your project's requirements. Here's a general guide on how to add authentication to DRF views

[learn-tech-solution]

Adding authentication to Django REST Framework (DRF) views involves configuring authentication classes in your DRF settings. DRF provides a variety of authentication classes that you can choose from based on your project's requirements. Here's a general guide on how to add authentication to DRF views