#Server and DB development services

[smokingcreams]

Re: 8tracks

HUGE UPDATE:

As I said on my earlier post today the CTO of 8tracks answered some questions on the discord server of mixer.fm

IF YOU'RE INTERESTED IN INFORMATION ABOUT 8TRACKS AND THE ANSWERS THE CTO OF 8TRACKS GAVE, PLEASE, KEEP READING THIS POST BECAUSE IT'S A LOT BUT YOU WON'T REGRET IT.

Okay, so he first talked about how they were involved in buying 8tracks, then how everything failed because of money and issues with the plataform then he talked about this new app called MixerFM they developed that works with web3 (8tracks is a web2 product), that if they get to launch it they'll get to launch 8tracks too because both apps will work with the same data.

Here is what they have already done in his own words:

*Built a multitenant backend system that supports both MIXER and 8tracks

*Fully rebuilt the 8tracks web app

*Fixed almost all legacy issues

*Developed iOS and Android apps for MIXER

What is next?

They need to migrate the 8tracks database from the old servers to the new environment. That final step costs about $50,000 and on his own words "I am personally committed to securing the funds to make it happen. If we pull this off since there is a time limit , we will have an chance to launch both 8tracks and MIXER. … so for all you community members that are pinging me to provide more details on X and here on discord, here it is"

Here the screenshots of his full statement:

NOW THE QUESTIONS HE ANSWERED:

*I transcribed them*

1. "What's the status of the founding right now?"

"Fundraising, for music its difficult"

2. "Our past data, is intact, isn't?"

"All data still exists from playlist 1"

3. "Will we be able to access our old playlists?"

"All playlists if we migrate the data will be saved. If we dont all is lost forever"

4. "How will the new 8tracks relaunch and MIXER be similar, and how will they be different?"

"8tracks / human created / mix tape style as it was before

mixer - ai asiated mix creation, music quest where people earn crypto for work they provode to music protocol ( solve quests earn money for providing that service )"

5. "Why is 8tracks being relaunched when they could just launch MIXER with our 8tracks database?"

"One app is web2 ( no crypto economy and incentives / ) mixer is web3 ( economy value exchange between users, artists, publishers, labels, advertisers) value (money) is shared between stakeholders and participants of app. Company earns less / users / artist earn more."

6. "Will we need to create a separate account for mixer? Or maybe a way to link our 8tracks to mixer?"

"New account no linking planned"

7. "What do you mean by fixed almost all legacy issues and fully rebuilt the 8tracks web app?"

"We have rebuilt most of 8tracks from scratch i wish could screen record a demo. In Last year we have rebuilt whole 8tracks ! No more issues no more bugs no more hacked comments"

8. "Will the song database be current and allow new songs? For example if someone makes a K pop playlist theres the capability for new songs and old not just all songs are from 2012. There will be songs from 2020 onwards to today?"

"Current cut of date is 2017, we have planed direct label deals to bring music DB up to speed with all new songs until 2025. This means no more song uploads"

9. "The apps would be available for android and outside USA?"

"USA + Canada + Germany + UK + Sweden + Italy + Greece + Portugal + Croatia in my personal rollout plan / but usa canada croatia would be top priority"

10. "Will 8tracks have a Sign in with Apple option?"

"It will have nothing if we don’t migrate the database but yes if we do it will have it"

11. ""Will Collections return?"

"Ofc If we save the database its safe to assume collections will return"

12. "Will the 8tracks forums return?"

"No that one i will deleted People spending too much time online"

13. "No more songs uploads forever or no more songs until…?"

"Idk, this really depends on do we save database or no. Maybe we restart the process of song uploads to rebuild the and create a worlds first open music database If anyone has any songs to upload that is

We operate under different license"

14. "What is your time limit? for the funding, I mean"

"Good question I think 2-3 months"

15. "From now?"

"Correct"

16. "When is the release date for Mixer?"

"Mixer would need 2-3 more months of work to be released Maybe even less of we would use external database services and just go with minimum features"

17. "Do you have the link for it?"

"Not if we dont secure the database that is number one priority"

*That was the end of the questions and answers*

Then he said:

"You need to act bring here (discord) people and help me set up go fund me camping of investor talks fail so we secure the database and migrate data so we can figure out whats next"

He also said he'll talk with the CEO about buying him the idea of community funding, that all who participate should have a lifetime subscription and "some more special thing", we suggested a message on the 8tracks official accounts (twitter, their blog, tumblr) and he was okay with the idea but he said they need to plan it carefully since the time is limited.

Okay, guys, that's so far what he said, I hope this information helps anyone, I don't know if they get to do a community funding but take in consideration it's a plausible option and that what they want from us is to participate in any way like for example spreading the message, if most people know about it the best, they also want you to join their server so here's the link to the website of mixerfm and where you can join the server:

Keep tagging few people who were discussing about this:

@junket-bank, @haorev, @americanundead, @eatpandulce, @throwupgirl, @avoid-avoidance, @rodeokid, @shehungthemoon, @promostuff-art @tumbling-and-tchaikovsky

#8tracks

[recreationaldivorce]

1, 3, 19!

1. base distro

my main desktop is artix linux; my laptop is void linux; my server is alpine linux (plus some VMs i use for development)

i am not actually the biggest systemd hater i just happen to not use it lol. i actually tried to use debian on my server at first but i couldn't get it to work with my hosting service's network for some reason, but with alpine if i did manual network setup during install it would Just Work. perhaps i can blame systemd for this

3. listening to music

i run a local mpd server and use ncmpcpp as a client, with my music library synced with syncthing. however i'm thinking i should move my music library to my server and stream from there bc my music library is taking up a shit ton of space on my phone and laptop both of which have limited storage (laptop storage is soldered on i think, and i don't think my phone storage is upgradeable either, but tbf i should double check those—in any case even if it were upgradeable that would cost Money and i shrimply don't think a massive music library synced between 3 devices is the wisest use of limited storage). so i may need to look into self-hosted music streaming solutions. although it is nice to be able to listen to music without using mobile data when i'm out and about.

19. file sync/sharing

a bit all over the place. as i said above i use syncthing for a few things but i'm increasingly moving away from that towards my nextcloud just bc if i'm syncing eg a 10GB file, there's no need for it to take up 30GB between 3 devices when i can just have it take up 10GB once on a remote server that i can access from any device only when it's needed. i am still sticking with syncthing for some things that are more sensitive so i want to reduce the number of devices it goes through: ie my keepass db, and some luks headers i have stored. also currently using a bit of a mess between syncthing and git hosting for my dotfiles but i'm trying to migrate to one chezmoi git repo since that can handle differences between devices and is much more elegant than my current glued-together scripts and git repos lol

for file sharing it's a bit all over the place. onionshare or bittorrent for some things, my own nextcloud for personal file sharing with people who can't wrap their heads around onionshare or bittorrent and just want a web browser link. i also use disroot's nextcloud instance for when i need to do the latter but not have it tied to me in any way. also sometimes i just send attachments in whatever platform we're using to communicate like just a signal attachment or something.

ask game

[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.

[websyn]

Demystifying Microsoft Azure Cloud Hosting and PaaS Services: A Comprehensive Guide

In the rapidly evolving landscape of cloud computing, Microsoft Azure has emerged as a powerful player, offering a wide range of services to help businesses build, deploy, and manage applications and infrastructure. One of the standout features of Azure is its Cloud Hosting and Platform-as-a-Service (PaaS) offerings, which enable organizations to harness the benefits of the cloud while minimizing the complexities of infrastructure management. In this comprehensive guide, we'll dive deep into Microsoft Azure Cloud Hosting and PaaS Services, demystifying their features, benefits, and use cases.

Understanding Microsoft Azure Cloud Hosting

Cloud hosting, as the name suggests, involves hosting applications and services on virtual servers that are accessed over the internet. Microsoft Azure provides a robust cloud hosting environment, allowing businesses to scale up or down as needed, pay for only the resources they consume, and reduce the burden of maintaining physical hardware. Here are some key components of Azure Cloud Hosting:

Virtual Machines (VMs): Azure offers a variety of pre-configured virtual machine sizes that cater to different workloads. These VMs can run Windows or Linux operating systems and can be easily scaled to meet changing demands.

Azure App Service: This PaaS offering allows developers to build, deploy, and manage web applications without dealing with the underlying infrastructure. It supports various programming languages and frameworks, making it suitable for a wide range of applications.

Azure Kubernetes Service (AKS): For containerized applications, AKS provides a managed Kubernetes service. Kubernetes simplifies the deployment and management of containerized applications, and AKS further streamlines this process.

Exploring Azure Platform-as-a-Service (PaaS) Services

Platform-as-a-Service (PaaS) takes cloud hosting a step further by abstracting away even more of the infrastructure management, allowing developers to focus primarily on building and deploying applications. Azure offers an array of PaaS services that cater to different needs:

Azure SQL Database: This fully managed relational database service eliminates the need for database administration tasks such as patching and backups. It offers high availability, security, and scalability for your data.

Azure Cosmos DB: For globally distributed, highly responsive applications, Azure Cosmos DB is a NoSQL database service that guarantees low-latency access and automatic scaling.

Azure Functions: A serverless compute service, Azure Functions allows you to run code in response to events without provisioning or managing servers. It's ideal for event-driven architectures.

Azure Logic Apps: This service enables you to automate workflows and integrate various applications and services without writing extensive code. It's great for orchestrating complex business processes.

Benefits of Azure Cloud Hosting and PaaS Services

Scalability: Azure's elasticity allows you to scale resources up or down based on demand. This ensures optimal performance and cost efficiency.

Cost Management: With pay-as-you-go pricing, you only pay for the resources you use. Azure also provides cost management tools to monitor and optimize spending.

High Availability: Azure's data centers are distributed globally, providing redundancy and ensuring high availability for your applications.

Security and Compliance: Azure offers robust security features and compliance certifications, helping you meet industry standards and regulations.

Developer Productivity: PaaS services like Azure App Service and Azure Functions streamline development by handling infrastructure tasks, allowing developers to focus on writing code.

Use Cases for Azure Cloud Hosting and PaaS

Web Applications: Azure App Service is ideal for hosting web applications, enabling easy deployment and scaling without managing the underlying servers.

Microservices: Azure Kubernetes Service supports the deployment and orchestration of microservices, making it suitable for complex applications with multiple components.

Data-Driven Applications: Azure's PaaS offerings like Azure SQL Database and Azure Cosmos DB are well-suited for applications that rely heavily on data storage and processing.

Serverless Architecture: Azure Functions and Logic Apps are perfect for building serverless applications that respond to events in real-time.

In conclusion, Microsoft Azure's Cloud Hosting and PaaS Services provide businesses with the tools they need to harness the power of the cloud while minimizing the complexities of infrastructure management. With scalability, cost-efficiency, and a wide array of services, Azure empowers developers and organizations to innovate and deliver impactful applications. Whether you're hosting a web application, managing data, or adopting a serverless approach, Azure has the tools to support your journey into the cloud.

[modulesap]

SAP ABAP (Advanced Business Application Programming) is a high-level programming language created by SAP, primarily used for developing applications within the SAP ecosystem. It is the core language used for customizing and extending SAP ERP systems, such as SAP S/4HANA, SAP ECC, and others.

🔍 What Is SAP ABAP Used For?

ABAP is mainly used for:

Developing custom reports, interfaces, forms, and enhancements.

Implementing business logic on the application server.

Integrating SAP with other systems.

Creating custom modules or modifying standard SAP behavior.

💡 Main Features of SAP ABAP

Feature

Description

Tightly Integrated with SAP

ABAP is native to SAP and integrates deeply with modules like SD, MM, FI, etc.

Data Dictionary

Central repository for managing database definitions (tables, views, etc.) used throughout ABAP programs.

Modularization

Supports modular programming with Function Modules, Includes, Subroutines, Classes, and Methods.

Event-Driven Programming

Especially in reports and module pool programming, ABAP responds to user actions or system events.

Open SQL

Allows database-independent access to SAP-managed databases, simplifying cross-platform support.

ABAP Objects (OOP)

Modern ABAP supports Object-Oriented Programming with classes, inheritance, polymorphism, etc.

SAP NetWeaver Compatibility

ABAP runs on the SAP NetWeaver platform, which provides essential services like user management, DB access, etc.

Enhancement & Customization

Offers tools like User Exits, BAdIs, Enhancement Points to modify SAP standard behavior without affecting core code.

Robust Debugging Tools

ABAP Workbench includes a powerful debugger, runtime analysis, and performance monitoring tools.

Cross-Platform Integration

With tools like RFC, BAPI, and OData, ABAP allows communication with external systems (e.g., mobile apps, web portals).

Call us on +91-84484 54549

Mail us on contact@anubhavtrainings.com

Website: Anubhav Online Trainings | UI5, Fiori, S/4HANA Trainings

[shreja]

Introduction to Microsoft Azure

What is Microsoft Azure? Microsoft Azure is the cloud computing service from Microsoft that offers a wide range of services to help individuals and organizations develop, deploy, and manage applications and services through Microsoft-managed data centers across the world. It supports different cloud models like IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). Key Features of Microsoft Azure ● Virtual Machines (VMs): Quickly deploy Windows or Linux virtual servers. ● App Services: Host web and mobile applications with scaling built-in. ● Azure Functions: Execute code without managing servers (serverless computing). ● Azure SQL Database: Scalable, fully-managed relational databases. ● Azure Kubernetes Service (AKS): Simplified Kubernetes management. ● Azure DevOps: Continuous integration and continuous delivery (CI/CD) tools. ● Azure Blob Storage: Solution for unstructured data storage. ● Azure Active Directory (AAD): Identity and access management. ● AI & Machine Learning Tools: Create and deploy intelligent apps. ● Hybrid Cloud Capabilities: On-premises and cloud integration seamlessly. Core Service Categories Category Compute Networking Storage Databases Analytics AI & ML IoT Security DevOps Examples Virtual Machines, App Services Virtual Network, Azure Load Balancer Blob Storage, Azure Files Azure SQL, Cosmos DB Azure Synapse, HDInsight Cognitive Services, Azure ML Studio IoT Hub, Azure Digital Twins Security Center, Key Vault Azure DevOps, GitHub Actions ✅ Benefits of Using Azure ● Scalable and Flexible: Scale up or down immediately as needed. ● Cost-Effective: Pay-as-you-go pricing model. ● Secure and Compliant: Enterprise-grade security with over 90 compliance offerings. ● Global Infrastructure: In more than 60 regions globally. ● Developer-Friendly: Supports a wide range of programming languages and frameworks. Who Uses Azure? ● Large Enterprises – For large-scale infrastructure and data solutions. ● Startups – To build, test, and deploy apps quickly. ● Developers – As a full-stack dev environment. ● Educational Institutions and Governments – For secure, scalable systems. Common Use Cases ● Website and app hosting ● Cloud-based storage and backup ● Big data analytics ● Machine learning projects ● Internet of Things (IoT) solutions ● Disaster recovery

[souhaillaghchimdev]

Cloud Computing for Programmers

Cloud computing has revolutionized how software is built, deployed, and scaled. As a programmer, understanding cloud services and infrastructure is essential to creating efficient, modern applications. In this guide, we’ll explore the basics and benefits of cloud computing for developers.

What is Cloud Computing?

Cloud computing allows you to access computing resources (servers, databases, storage, etc.) over the internet instead of owning physical hardware. Major cloud providers include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Key Cloud Computing Models

IaaS (Infrastructure as a Service): Provides virtual servers, storage, and networking (e.g., AWS EC2, Azure VMs)

PaaS (Platform as a Service): Offers tools and frameworks to build applications without managing servers (e.g., Heroku, Google App Engine)

SaaS (Software as a Service): Cloud-hosted apps accessible via browser (e.g., Gmail, Dropbox)

Why Programmers Should Learn Cloud

Deploy apps quickly and globally

Scale applications with demand

Use managed databases and storage

Integrate with AI, ML, and big data tools

Automate infrastructure with DevOps tools

Popular Cloud Services for Developers

AWS: EC2, Lambda, S3, RDS, DynamoDB

Azure: App Services, Functions, Cosmos DB, Blob Storage

Google Cloud: Compute Engine, Cloud Run, Firebase, BigQuery

Common Use Cases

Hosting web and mobile applications

Serverless computing for microservices

Real-time data analytics and dashboards

Cloud-based CI/CD pipelines

Machine learning model deployment

Getting Started with the Cloud

Create an account with a cloud provider (AWS, Azure, GCP)

Start with a free tier or sandbox environment

Launch your first VM or web app

Use the provider’s CLI or SDK to deploy code

Monitor usage and set up billing alerts

Example: Deploying a Node.js App on Heroku (PaaS)

# Step 1: Install Heroku CLI heroku login # Step 2: Create a new Heroku app heroku create my-node-app # Step 3: Deploy your code git push heroku main # Step 4: Open your app heroku open

Tools and Frameworks

Docker: Containerize your apps for portability

Kubernetes: Orchestrate containers at scale

Terraform: Automate cloud infrastructure with code

CI/CD tools: GitHub Actions, Jenkins, GitLab CI

Security Best Practices

Use IAM roles and permissions

Encrypt data at rest and in transit

Enable firewalls and VPCs

Regularly update dependencies and monitor threats

Conclusion

Cloud computing enables developers to build powerful, scalable, and reliable software with ease. Whether you’re developing web apps, APIs, or machine learning services, cloud platforms provide the tools you need to succeed in today’s tech-driven world.

[dynamicscommunity101]

AX 2012 Interview Questions and Answers for Beginners and Experts

Microsoft Dynamics AX 2012 is a powerful ERP answer that facilitates organizations streamline their operations. Whether you're a newbie or an professional, making ready for an interview associated with AX 2012 requires a radical knowledge of its core standards, functionalities, and technical factors. Below is a list of commonly requested AX 2012 interview questions together with their solutions.

Basic AX 2012 Interview Questions

What is Microsoft Dynamics AX 2012?Microsoft Dynamics AX 2012 is an company aid planning (ERP) solution advanced with the aid of Microsoft. It is designed for large and mid-sized groups to manage finance, supply chain, manufacturing, and client relationship control.

What are the important thing features of AX 2012?

Role-primarily based user experience

Strong financial control skills

Advanced warehouse and deliver chain management

Workflow automation

Enhanced reporting with SSRS (SQL Server Reporting Services)

What is the distinction between AX 2009 and AX 2012?

AX 2012 introduced a new data version with the introduction of surrogate keys.

The MorphX IDE changed into replaced with the Visual Studio development environment.

Improved workflow and role-based totally get right of entry to manipulate.

What is the AOT (Application Object Tree) in AX 2012?The AOT is a hierarchical shape used to keep and manipulate objects like tables, bureaucracy, reports, lessons, and queries in AX 2012.

Explain the usage of the Data Dictionary in AX 2012.The Data Dictionary contains definitions of tables, information types, family members, and indexes utilized in AX 2012. It guarantees facts integrity and consistency across the device.

Technical AX 2012 Interview Questions

What are the distinctive sorts of tables in AX 2012?

Regular tables

Temporary tables

In Memory tables

System tables

What is the distinction between In Memory and TempDB tables?

In Memory tables shop information within the purchaser memory and aren't continual.

Temp DB tables save brief statistics in SQL Server and are session-unique.

What is X++ and the way is it utilized in AX 2012?X++ is an item-oriented programming language used in AX 2012 for growing business good judgment, creating custom modules, and automating processes.

What is the cause of the CIL (Common Intermediate Language) in AX 2012?CIL is used to convert X++ code into .NET IL, enhancing overall performance by using enabling execution at the .NET runtime degree.

How do you debug X++ code in AX 2012?Debugging may be accomplished the use of the X++ Debugger or with the aid of enabling the Just-In-Time Debugging function in Visual Studio.

Advanced AX 2012 Interview Questions

What is a Query Object in AX 2012?A Query Object is used to retrieve statistics from tables using joins, tiers, and sorting.

What are Services in AX 2012, and what sorts are to be had?

Document Services (for replacing statistics)

Custom Services (for exposing X++ logic as a carrier)

System Services (metadata, question, and user consultation offerings)

Explain the concept of Workflows in AX 2012.Workflows allow the automation of commercial enterprise techniques, together with approvals, via defining steps and assigning responsibilities to users.

What is the purpose of the SysOperation Framework in AX 2012?It is a substitute for RunBaseBatch framework, used for walking techniques asynchronously with higher scalability.

How do you optimize overall performance in AX 2012?

Using indexes effectively

Optimizing queries

Implementing caching strategies

Using batch processing for massive facts operations

Conclusion

By understanding those AX 2012 interview questions, applicants can successfully put together for interviews. Whether you're a novice or an experienced expert, gaining knowledge of those topics will boost your self assurance and help you secure a role in Microsoft Dynamics AX 2012 tasks.

[learning-code-ficusoft]

Using Docker for Full Stack Development and Deployment

1. Introduction to Docker

What is Docker? Docker is an open-source platform that automates the deployment, scaling, and management of applications inside containers. A container packages your application and its dependencies, ensuring it runs consistently across different computing environments.

Containers vs Virtual Machines (VMs)

Containers are lightweight and use fewer resources than VMs because they share the host operating system’s kernel, while VMs simulate an entire operating system. Containers are more efficient and easier to deploy.

Docker containers provide faster startup times, less overhead, and portability across development, staging, and production environments.

Benefits of Docker in Full Stack Development

Portability: Docker ensures that your application runs the same way regardless of the environment (dev, test, or production).

Consistency: Developers can share Dockerfiles to create identical environments for different developers.

Scalability: Docker containers can be quickly replicated, allowing your application to scale horizontally without a lot of overhead.

Isolation: Docker containers provide isolated environments for each part of your application, ensuring that dependencies don’t conflict.

2. Setting Up Docker for Full Stack Applications

Installing Docker and Docker Compose

Docker can be installed on any system (Windows, macOS, Linux). Provide steps for installing Docker and Docker Compose (which simplifies multi-container management).

Commands:

docker --version to check the installed Docker version.

docker-compose --version to check the Docker Compose version.

Setting Up Project Structure

Organize your project into different directories (e.g., /frontend, /backend, /db).

Each service will have its own Dockerfile and configuration file for Docker Compose.

3. Creating Dockerfiles for Frontend and Backend

Dockerfile for the Frontend:

For a React/Angular app:

Dockerfile

FROM node:14 WORKDIR /app COPY package*.json ./ RUN npm install COPY . . EXPOSE 3000 CMD ["npm", "start"]

This Dockerfile installs Node.js dependencies, copies the application, exposes the appropriate port, and starts the server.

Dockerfile for the Backend:

For a Python Flask app

Dockerfile

FROM python:3.9 WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . EXPOSE 5000 CMD ["python", "app.py"]

For a Java Spring Boot app:

Dockerfile

FROM openjdk:11 WORKDIR /app COPY target/my-app.jar my-app.jar EXPOSE 8080 CMD ["java", "-jar", "my-app.jar"]

This Dockerfile installs the necessary dependencies, copies the code, exposes the necessary port, and runs the app.

4. Docker Compose for Multi-Container Applications

What is Docker Compose? Docker Compose is a tool for defining and running multi-container Docker applications. With a docker-compose.yml file, you can configure services, networks, and volumes.

docker-compose.yml Example:

yaml

version: "3" services: frontend: build: context: ./frontend ports: - "3000:3000" backend: build: context: ./backend ports: - "5000:5000" depends_on: - db db: image: postgres environment: POSTGRES_USER: user POSTGRES_PASSWORD: password POSTGRES_DB: mydb

This YAML file defines three services: frontend, backend, and a PostgreSQL database. It also sets up networking and environment variables.

5. Building and Running Docker Containers

Building Docker Images:

Use docker build -t <image_name> <path> to build images.

For example:

bash

docker build -t frontend ./frontend docker build -t backend ./backend

Running Containers:

You can run individual containers using docker run or use Docker Compose to start all services:

bash

docker-compose up

Use docker ps to list running containers, and docker logs <container_id> to check logs.

Stopping and Removing Containers:

Use docker stop <container_id> and docker rm <container_id> to stop and remove containers.

With Docker Compose: docker-compose down to stop and remove all services.

6. Dockerizing Databases

Running Databases in Docker:

You can easily run databases like PostgreSQL, MySQL, or MongoDB as Docker containers.

Example for PostgreSQL in docker-compose.yml:

yaml

db: image: postgres environment: POSTGRES_USER: user POSTGRES_PASSWORD: password POSTGRES_DB: mydb

Persistent Storage with Docker Volumes:

Use Docker volumes to persist database data even when containers are stopped or removed:

yaml

volumes: - db_data:/var/lib/postgresql/data

Define the volume at the bottom of the file:

yaml

volumes: db_data:

Connecting Backend to Databases:

Your backend services can access databases via Docker networking. In the backend service, refer to the database by its service name (e.g., db).

7. Continuous Integration and Deployment (CI/CD) with Docker

Setting Up a CI/CD Pipeline:

Use Docker in CI/CD pipelines to ensure consistency across environments.

Example: GitHub Actions or Jenkins pipeline using Docker to build and push images.

Example .github/workflows/docker.yml:

yaml

name: CI/CD Pipeline on: [push] jobs: build: runs-on: ubuntu-latest steps: - name: Checkout Code uses: actions/checkout@v2 - name: Build Docker Image run: docker build -t myapp . - name: Push Docker Image run: docker push myapp

Automating Deployment:

Once images are built and pushed to a Docker registry (e.g., Docker Hub, Amazon ECR), they can be pulled into your production or staging environment.

8. Scaling Applications with Docker

Docker Swarm for Orchestration:

Docker Swarm is a native clustering and orchestration tool for Docker. You can scale your services by specifying the number of replicas.

Example:

bash

docker service scale myapp=5

Kubernetes for Advanced Orchestration:

Kubernetes (K8s) is more complex but offers greater scalability and fault tolerance. It can manage Docker containers at scale.

Load Balancing and Service Discovery:

Use Docker Swarm or Kubernetes to automatically load balance traffic to different container replicas.

9. Best Practices

Optimizing Docker Images:

Use smaller base images (e.g., alpine images) to reduce image size.

Use multi-stage builds to avoid unnecessary dependencies in the final image.

Environment Variables and Secrets Management:

Store sensitive data like API keys or database credentials in Docker secrets or environment variables rather than hardcoding them.

Logging and Monitoring:

Use tools like Docker’s built-in logging drivers, or integrate with ELK stack (Elasticsearch, Logstash, Kibana) for advanced logging.

For monitoring, tools like Prometheus and Grafana can be used to track Docker container metrics.

10. Conclusion

Why Use Docker in Full Stack Development? Docker simplifies the management of complex full-stack applications by ensuring consistent environments across all stages of development. It also offers significant performance benefits and scalability options.

Recommendations:

Encourage users to integrate Docker with CI/CD pipelines for automated builds and deployment.

Mention the use of Docker for microservices architecture, enabling easy scaling and management of individual services.

WEBSITE: https://www.ficusoft.in/full-stack-developer-course-in-chennai/

[cloudolus]

Amazon RDS Performance Insights | Monitor and Optimize Database Performance

Amazon RDS Performance Insights is an advanced monitoring tool that helps you analyze and optimize your database workload in Amazon RDS and Amazon Aurora. It provides real-time insights into database performance, making it easier to identify bottlenecks and improve efficiency without deep database expertise.  

Key Features of Amazon RDS Performance Insights:  

✅ Automated Performance Monitoring – Continuously collects and visualizes performance data to help you monitor database load.   ✅ SQL Query Analysis – Identifies slow-running queries, so you can optimize them for better database efficiency.   ✅ Database Load Metrics – Displays a simple Database Load (DB Load) graph, showing the active sessions consuming resources.   ✅ Multi-Engine Support – Compatible with MySQL, PostgreSQL, SQL Server, MariaDB, and Amazon Aurora.   ✅ Retention & Historical Analysis – Stores performance data for up to two years, allowing trend analysis and long-term optimization.   ✅ Integration with AWS Services – Works seamlessly with Amazon CloudWatch, AWS Lambda, and other AWS monitoring tools.  

How Amazon RDS Performance Insights Helps You:  

🔹 Troubleshoot Performance Issues – Quickly diagnose and fix slow queries, high CPU usage, or locked transactions.   🔹 Optimize Database Scaling – Understand workload trends to scale your database efficiently.   🔹 Enhance Application Performance – Ensure your applications run smoothly by reducing database slowdowns.   🔹 Improve Cost Efficiency – Optimize resource utilization to prevent over-provisioning and reduce costs.  

How to Enable Amazon RDS Performance Insights:   1️⃣ Navigate to AWS Management Console.   2️⃣ Select Amazon RDS and choose your database instance.   3️⃣ Click on Modify, then enable Performance Insights under Monitoring.   4️⃣ Choose the retention period (default 7 days, up to 2 years with paid plans).   5️⃣ Save changes and start analyzing real-time database performance!  

Who Should Use Amazon RDS Performance Insights?   🔹 Database Administrators (DBAs) – To manage workload distribution and optimize database queries.   🔹 DevOps Engineers – To ensure smooth database operations for applications running on AWS.   🔹 Developers – To analyze slow queries and improve app performance.   🔹 Cloud Architects – To monitor resource utilization and plan database scaling effectively.  

Amazon RDS Performance Insights simplifies database monitoring, making it easy to detect issues and optimize workloads for peak efficiency. Start leveraging it today to improve the performance and scalability of your AWS database infrastructure! 🚀  

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[morpheusindia]

Revolutionizing the IT Industry: The Role of a Full Stack Developer in Pune

Introduction: 

Innovation, adaptability, and technical genius are key components of the IT industry's success. Full Stack Developers are essential in determining how software solutions will develop in the future since they combine creativity and knowledge to produce reliable applications. This interesting job, which is based in Pune, is intended for individuals who are prepared to take on new challenges, develop their abilities, and participate in innovative projects.

Morpheus Consulting offers a fantastic chance for individuals who have a love for coding and a talent for solving problems to become a part of a vibrant company that uses the newest technology in the IT sector.

How Full Stack Developers Drive IT Innovation

1.Designing and Developing with Precision:

Developers create long-lasting systems, not just code. Custom software that satisfies the highest industry requirements is produced by Full Stack Developers, who are skilled in Azure platform services, Azure DevOps, and Microservices architecture. At Morpheus Consulting, we make sure your technical abilities are properly utilized by connecting you with chances where you can succeed using frameworks like MVC.NET,.NET Core, and Angular/React.

2.Leveraging Database Technologies:

Robust databases are necessary for modern applications. Developers with expertise in Cosmos DB, MongoDB, or MS SQL Server are quite valuable. The goal of the Full Stack Developer position is to power creative solutions through flawless database connectivity, and Morpheus Consulting makes sure that your experience matches businesses who appreciate this ability.

3.Version Control and Collaboration:

To maintain code integrity and promote teamwork, it is essential to use tools such as GIT. Morpheus Consulting guarantees that developers can work in settings that value teamwork and the newest tools available on the market.

4.Expanding Horizons with Emerging Tech:

Staying ahead is crucial in the always changing IT market. This position offers developers interested in containerization platforms like Docker and Kubernetes exciting challenges. Morpheus Consulting connects you with businesses that support innovation and career advancement.

5.Building Reliable Solutions:

Modern systems are designed by developers. Success is driven by Full Stack Developers, who write maintainable code and guarantee the dependability of system design. We at Morpheus Consulting enable you to participate in innovative projects that have an effect on several industries.

What Makes an Ideal Full Stack Developer?

Strong Technical Foundation:Proficiency in NET Core, Angular/React, and troubleshooting intricate issues is crucial.

Problem-Solving Attitude:A talent for finding creative ways to overcome obstacles.

Growth-Oriented Mindset:A strong desire to study and keep on top of trends in both one's personal and professional life.

Collaboration Skills:Excellent communication and a development strategy focused on teamwork.

Adaptability:Readiness to adopt new technology and flourish in fast-paced settings.

Morpheus Consulting works extensively with applicants to match them with positions that align with their career goals and technical capabilities.

The Impact of Full Stack Developers in IT

Full Stack Developers advance the IT sector by fusing technical know-how with creative ideas. They are essential to developing software that is impactful, scalable, and maintained. Professionals might find possibilities with Morpheus Consulting that optimize their contributions to the ever expanding IT industry.

Conclusion: 

As a Full Stack Developer, are you prepared to go on an adventurous journey? Working with cutting-edge technologies, resolving challenging issues, and advancing IT innovation are all opportunities presented by this Pune position. Morpheus Consulting is ready to help you on your career path by making sure you locate opportunities that align with your goals and abilities.

Apply now to take on this challenging and impactful role with confidence, and rest assured that Morpheus Consulting will be your trusted partner in navigating your professional journey.

For more Recruitment / Placement / HR / Consultancy services, connect with Morpheus Consulting:

📞: (+91) 8376986986

📧: contact@mhc.co.in

🌐: www.mhc.co.in

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[ruchi30]

Enhancing Sound Control with High-Quality Acoustic Enclosures by Envirotech Systems Ltd

Noise pollution becomes such an important factor in the present industrial and commercial sectors that businesses now almost all over the globe are adopting it in their priority list. The strong, well-designed acoustic enclosures can attenuate the noise successfully and even bring it down to the acceptable limits as prescribed in the standards or regulations and consequently even improve working conditions for operators. Envirotech Systems Ltd is an expert manufacturer of superior acoustic enclosures offering a wide range of designs for all industrial requirements.

Understanding Acoustic Enclosures Acoustic enclosures are really beautifully crafted enclosures that contain all noisy appliance or areas with the aim of reducing noise levels. They are designed with sound absorber materials and precision techniques, allowing a good and efficient decrease in sounds while still allowing access and air ventilation. These are intended to serve the twofold purposes of reducing the noise impact on the environment and protecting the worker from long exposure to high noise levels.

Applications of Acoustic Enclosures Regardless of the industry, acoustic enclosures been all-around conventions to such extent:

Installing them on Industrial Machines: Most of the noises attributable to air conditions, compressors, and generators are in the space. Acoustic enclosure controls such noise emissions from machines in industries.

Power Generation Plants: Turbines, boilers, and exhaust systems use custom noise-control measures to remain complaint with the environment.

Health Facilities: Machines such as MRI generate so much noise that space must have acoustic enclosures through which patients and staff can get access.

Data Centers: The continuous hum of servers and cooling systems can all but be completely managed using very specific personal acoustic enclosures.

In commercial spaces are found HVAC systems, or any other mechanical units of commercial buildings. These units can also benefit from placing acoustic envelopes over them so as to scene a peaceful environment. Ambient systems themselves-environmentally-friendly-systems limited, devise as well as develop the acoustic enclosures in conformity with custom applications, so as to maximize both noise reduction and efficiency.

Key Features of Acoustic Enclosures by Envirotech Systems Ltd Our acoustic enclosures are built with superior attention to detail and come with various advanced features:

Major Noise Reduction Our enclosures provide good sound attenuation that can reduce noise levels by an average of 20 to 50 dB, depending on the application. These make them excellent for industries that suffer from high noise emissions but are to strict control limits.

Customization Every surrounding premises have a very unique noise problem, and so our design team develops customized acoustics cages depending on your equipment, space restrictions, and operational requirements.

Endurance and Toughness Our soundproof enclosures made from terrific materials such as steel and composite panels have high endurance for harsh environments and long-term use.

Ventilation Systems Proper ventilation helps prevent the accumulation of heat from machinery that is sealed. We incorporate noise-dampened ducts to ensure normal airflow, which does not compromise noise retention, into our designs.

Access and Maintenance Envirotech Systems Ltd soundproof enclosures have easily operable doors, panel, and service hatches to ensure maintenance and inspection of equipment without hassle.

Compliance With Standards International noise control standards and local regulations are observed in our designs to provide reliability in any industry.

Benefits of Acoustic Enclosures

Enhanced Workplace Safety Long exposures to very high levels of noise lead to ear damage as well as increased stress among employees. Acoustic enclosures thus protect workers by creating a quiet workplace.

Improved Productivity A quieter work atmosphere leads to an increased concentration of the employees with less error and better all-round productivity.

Environmental Compliance Through acoustic enclosures, industries can thus comply with noise pollution legislation to avoid penalties and keep a good image in the eyes of the general public.

Maintenance of Machines Using acoustic enclosures, exposure to environmental dust and temperature extremes can serve to prolong the life of the equipment.

Cost Saving When invested wisely in good quality acoustic enclosures, it may save many costs due to the possible fines from noise-related problems and also reduce the maintenance of the equipment.

Why Choose Envirotech Systems Ltd? Envirotech Systems Ltd is one of the leading manufacturers of acoustic solutions committed to delivering sound, robust and economical noise control products.

Proficiency and Experience We have years of experience providing successful acoustic enclosure installations in industries, such as manufacturing, healthcare, and energy.

Current Technology All of our latest manufacturing processes produce advanced, cutting-edge materials to ensure unmatched quality that can compete on unmatched levels of performance.

Dedicated Support Team Our experts extend one-stop service from the beginning consultation and design to installation including after-sales services for customers.

Sustainability Initially, we try to ensure that our processes are environmentally-friendly, using recyclable materials and energy-efficient production methods.

The Design Process We at Envirotech Systems Ltd design systems based on customized solutions for client satisfaction:

Site Analysis It is the on-site analysis for assessing levels/sources of noise, equipment specifications, and environmental factors.

Customized Development of Design Through advanced simulation tools our engineers simulate acoustic enclosures into optimum noise-reduction performance.

Testing and Prototyping Prototypes are tested before production to larger scale for exact performance under realistic conditions.

Manufacturing and Installation Manufactured at state-of-the-art facilities, installations are carried out by skilled engineers, once approved.

Real-World Impact of Acoustic Enclosures A Case Study on Mitigating Generator Noise in a Manufacturing Plant A major manufacturing plant received complaints from the nearby residential areas because of the noise generated by their industrial generators. Envirotech Systems Ltd designed and installed a customized acoustic enclosure that reduced the noise level by 40 dB. This avoided the complaints but at the same time was more effective in operations.

Customer Says "Noise levels at our site have been reduced dramatically after the installation of the acoustic enclosure by Envirotech Systems Ltd. A professional team, and the solution was tailor-made for us."

Operations Manager, Leading Manufacturing Company

Conclusion There are absolutely amazing and interesting acoustic enclosures made by Envirotech Systems Ltd. This is one among such companies that understand the need for keeping a plant efficient without noise, and it creates economical soundproof structures applicable to industrial buildings.

If you are looking for a sounding board to help you deal with all your noise control problems, your search stops at Envirotech Systems Ltd. Call us today and find out about our complete range of acoustic enclosures and how we can help turn your space into a quieter, safer, and more productive space.

[mtsuhail]

How Java Full-Stack Developers Can Leverage Cloud Technologies

The rapid growth of cloud computing has transformed the way applications are built, deployed, and managed. For Java full-stack developers, leveraging cloud technologies has become essential for building scalable, reliable, and efficient applications. Whether you’re integrating cloud storage, deploying microservices, or utilizing serverless computing, understanding how to use cloud platforms with Java can significantly enhance your development workflow.

In this blog, we’ll explore five key ways Java full-stack developers can leverage cloud technologies to improve their applications and workflows.

1. Deploying Java Applications on the Cloud

The Advantage

Cloud platforms like AWS, Google Cloud, and Microsoft Azure offer robust infrastructure to host Java applications with minimal configuration. This enables developers to focus more on building the application rather than managing physical servers.

How to Leverage It

Use Cloud Infrastructure: Utilize cloud compute services such as AWS EC2, Google Compute Engine, or Azure Virtual Machines to run Java applications.

Containerization: Containerize your Java applications using Docker and deploy them to cloud container services like AWS ECS, Google Kubernetes Engine (GKE), or Azure Kubernetes Service (AKS).

Managed Services: Use cloud-based Java application hosting solutions like AWS Elastic Beanstalk, Google App Engine, or Azure App Service for automatic scaling and monitoring.

2. Implementing Microservices with Cloud-Native Tools

The Advantage

Cloud environments are perfect for microservices-based architectures, allowing Java developers to break down applications into small, independent services. This makes applications more scalable, maintainable, and fault-tolerant.

How to Leverage It

Cloud Native Frameworks: Use Spring Boot and Spring Cloud to build microservices and deploy them on cloud platforms. These frameworks simplify service discovery, load balancing, and fault tolerance.

API Gateway: Implement API Gateway services such as AWS API Gateway, Azure API Management, or Google Cloud Endpoints to manage and route requests to your microservices.

Service Mesh: Use service meshes like Istio (on Kubernetes) to manage microservices communication, monitoring, and security in the cloud.

3. Utilizing Serverless Computing

The Advantage

Serverless computing allows Java developers to focus solely on writing code, without worrying about server management. This makes it easier to scale applications quickly and cost-effectively, as you only pay for the compute power your functions consume.

How to Leverage It

AWS Lambda: Write Java functions to run on AWS Lambda, automatically scaling as needed without managing servers.

Azure Functions: Similarly, use Java to build functions that execute on Azure Functions, enabling event-driven computing.

Google Cloud Functions: Integrate Java with Google Cloud Functions for lightweight, serverless event-driven applications.

4. Storing Data in the Cloud

The Advantage

Cloud storage offers highly available and scalable database solutions, which are perfect for Java full-stack developers building applications that require robust data management systems.

How to Leverage It

Relational Databases: Use managed database services like Amazon RDS, Google Cloud SQL, or Azure SQL Database for scalable, cloud-hosted SQL databases such as MySQL, PostgreSQL, or MariaDB.

NoSQL Databases: Implement NoSQL databases like AWS DynamoDB, Google Cloud Firestore, or Azure Cosmos DB for applications that need flexible, schema-less data storage.

Cloud Storage: Store large amounts of unstructured data using cloud storage solutions like AWS S3, Google Cloud Storage, or Azure Blob Storage.

5. Monitoring and Scaling Java Applications in the Cloud

The Advantage

One of the main benefits of the cloud is the ability to scale your applications easily, both vertically and horizontally. Additionally, cloud platforms provide powerful monitoring and logging tools to track the performance of your Java applications in real-time.

How to Leverage It

Auto-Scaling: Use auto-scaling groups in AWS, Google Cloud, or Azure to automatically adjust the number of instances based on demand.

Monitoring and Alerts: Implement cloud monitoring services like AWS CloudWatch, Google Stackdriver, or Azure Monitor to track metrics and receive alerts when issues arise.

Log Management: Use cloud logging tools such as AWS CloudTrail, Google Cloud Logging, or Azure Log Analytics to collect and analyze logs for troubleshooting.

Conclusion

By embracing cloud technologies, Java full-stack developers can build more scalable, resilient, and cost-efficient applications. Whether you’re deploying microservices, leveraging serverless computing, or integrating cloud storage, the cloud provides a wealth of tools to enhance your development process.

Cloud platforms also enable you to focus more on building your applications rather than managing infrastructure, ultimately improving productivity and accelerating development cycles.

Are you ready to leverage the cloud in your Java full-stack projects? Start exploring cloud platforms today and take your Java development to new heights!

[qcs01]

Unlocking the Power of Serverless Architecture with AWS Lambda, Azure Functions, and Google Cloud Functions

In today's fast-paced tech environment, businesses seek efficient ways to deploy applications without being bogged down by infrastructure management. This is where serverless architecture comes into play, offering a revolutionary approach to running code without provisioning or managing servers. Let's explore how you can leverage platforms like AWS Lambda, Azure Functions, and Google Cloud Functions to transform your application development while reducing costs and operational complexity.

What is Serverless Architecture?

Serverless architecture is a cloud-computing execution model that automatically manages the infrastructure for running your code. Unlike traditional server-based models, serverless architecture offloads all the heavy lifting to cloud providers, allowing you to focus purely on writing and deploying your application logic.

Platforms like AWS Lambda, Azure Functions, and Google Cloud Functions empower developers to execute code in response to events without ever worrying about the underlying servers or scaling the infrastructure.

Benefits of Serverless Architecture

Reduced Operational Overhead

By using serverless platforms, you eliminate the need for managing servers, storage, and networking infrastructure. This allows your team to focus more on application development and less on infrastructure maintenance.

Cost-Efficient

With serverless, you only pay for the compute resources you use. This pay-as-you-go model ensures that you don’t waste money on idle server capacity, making it a highly cost-effective approach for businesses of all sizes.

Automatic Scaling

Serverless platforms automatically scale up or down based on demand. Whether you have one user or millions, the architecture adjusts the resources accordingly, providing optimal performance without any manual intervention.

Implementation Approach: AWS Lambda, Azure Functions, and Google Cloud Functions

Here's a step-by-step guide to implementing serverless architecture using the leading cloud platforms:

1. AWS Lambda

Create a Lambda Function: Go to the AWS Lambda console and create a new function. You can choose a runtime environment like Node.js, Python, or Java.

Set Triggers: Configure the triggers, such as an API Gateway, S3 bucket, or DynamoDB table, that will invoke your Lambda function.

Deploy and Test: Deploy the code and test its functionality. Lambda automatically scales to handle incoming requests, ensuring smooth operations.

2. Azure Functions

Set Up Your Function App: Navigate to the Azure portal and create a new Function App.

Choose a Development Environment: Select your preferred language and environment, such as C#, JavaScript, or Python.

Integrate and Deploy: Integrate your functions with services like Azure Cosmos DB or Event Grid, then deploy your code and start monitoring its performance.

3. Google Cloud Functions

Create a Cloud Function: Head to the Google Cloud Console and create a new function.

Configure the Trigger: Select an event source like HTTP, Pub/Sub, or Firebase that will trigger your function.

Deploy and Monitor: Deploy the code and monitor the function's performance in real-time, ensuring it meets the required scalability and efficiency.

Use Cases for Serverless Architecture

Microservices Development: Break down applications into smaller, independent services that can be deployed and scaled individually.

Real-time Data Processing: Process data streams in real-time with minimal latency using triggers from data sources.

Web and Mobile Backends: Build and manage scalable backends for web and mobile apps without server management.

Why Choose HawkStack for Serverless Architecture Implementation?

At HawkStack Technologies, we specialize in helping businesses implement serverless solutions that drive growth and innovation. Our team of experts ensures that your transition to serverless architecture is seamless, secure, and scalable. We leverage platforms like AWS Lambda, Azure Functions, and Google Cloud Functions to build solutions tailored to your business needs.

Conclusion

Serverless architecture is not just a trend; it's a game-changer in how modern applications are built and deployed. By utilizing platforms like AWS Lambda, Azure Functions, and Google Cloud Functions, businesses can drastically reduce operational overhead, cut costs, and focus on innovation.

Partner with HawkStack Technologies to explore the full potential of serverless architecture and take your business to the next level. Let's build the future of scalable and efficient cloud solutions together!

Ready to make the shift to serverless architecture? Contact us today to get started!

[jcmarchi]

Charity Majors, CTO & Co-Founder at Honeycomb – Interview Series

New Post has been published on https://thedigitalinsider.com/charity-majors-cto-co-founder-at-honeycomb-interview-series/

Charity Majors, CTO & Co-Founder at Honeycomb – Interview Series

Charity is an ops engineer and accidental startup founder at Honeycomb. Before this she worked at Parse, Facebook, and Linden Lab on infrastructure and developer tools, and always seemed to wind up running the databases. She is the co-author of O’Reilly’s Database Reliability Engineering, and loves free speech, free software, and single malt scotch.

You were the Production Engineering Manager at Facebook (Now Meta) for over 2 years, what were some of your highlights from this period and what are some of your key takeaways from this experience?

I worked on Parse, which was a backend for mobile apps, sort of like Heroku for mobile. I had never been interested in working at a big company, but we were acquired by Facebook. One of my key takeaways was that acquisitions are really, really hard, even in the very best of circumstances. The advice I always give other founders now is this: if you’re going to be acquired, make sure you have an executive sponsor, and think really hard about whether you have strategic alignment. Facebook acquired Instagram not long before acquiring Parse, and the Instagram acquisition was hardly bells and roses, but it was ultimately very successful because they did have strategic alignment and a strong sponsor.

I didn’t have an easy time at Facebook, but I am very grateful for the time I spent there; I don’t know that I could have started a company without the lessons I learned about organizational structure, management, strategy, etc. It also lent me a pedigree that made me attractive to VCs, none of whom had given me the time of day until that point. I’m a little cranky about this, but I’ll still take it.

Could you share the genesis story behind launching Honeycomb?

Definitely. From an architectural perspective, Parse was ahead of its time — we were using microservices before there were microservices, we had a massively sharded data layer, and as a platform serving over a million mobile apps, we had a lot of really complicated multi-tenancy problems. Our customers were developers, and they were constantly writing and uploading arbitrary code snippets and new queries of, shall we say, “varying quality” — and we just had to take it all in and make it work, somehow.

We were on the vanguard of a bunch of changes that have since gone mainstream. It used to be that most architectures were pretty simple, and they would fail repeatedly in predictable ways. You typically had a web layer, an application, and a database, and most of the complexity was bound up in your application code. So you would write monitoring checks to watch for those failures, and construct static dashboards for your metrics and monitoring data.

This industry has seen an explosion in architectural complexity over the past 10 years. We blew up the monolith, so now you have anywhere from several services to thousands of application microservices. Polyglot persistence is the norm; instead of “the database” it’s normal to have many different storage types as well as horizontal sharding, layers of caching, db-per-microservice, queueing, and more. On top of that you’ve got server-side hosted containers, third-party services and platforms, serverless code, block storage, and more.

The hard part used to be debugging your code; now, the hard part is figuring out where in the system the code is that you need to debug. Instead of failing repeatedly in predictable ways, it’s more likely the case that every single time you get paged, it’s about something you’ve never seen before and may never see again.

That’s the state we were in at Parse, on Facebook. Every day the entire platform was going down, and every time it was something different and new; a different app hitting the top 10 on iTunes, a different developer uploading a bad query.

Debugging these problems from scratch is insanely hard. With logs and metrics, you basically have to know what you’re looking for before you can find it. But we started feeding some data sets into a FB tool called Scuba, which let us slice and dice on arbitrary dimensions and high cardinality data in real time, and the amount of time it took us to identify and resolve these problems from scratch dropped like a rock, like from hours to…minutes? seconds? It wasn’t even an engineering problem anymore, it was a support problem. You could just follow the trail of breadcrumbs to the answer every time, clicky click click.

It was mind-blowing. This massive source of uncertainty and toil and unhappy customers and 2 am pages just … went away. It wasn’t until Christine and I left Facebook that it dawned on us just how much it had transformed the way we interacted with software. The idea of going back to the bad old days of monitoring checks and dashboards was just unthinkable.

But at the time, we honestly thought this was going to be a niche solution — that it solved a problem other massive multitenant platforms might have. It wasn’t until we had been building for almost a year that we started to realize that, oh wow, this is actually becoming an everyone problem.

For readers who are unfamiliar, what specifically is an observability platform and how does it differ from traditional monitoring and metrics?

Traditional monitoring famously has three pillars: metrics, logs and traces. You usually need to buy many tools to get your needs met: logging, tracing, APM, RUM, dashboarding, visualization, etc. Each of these is optimized for a different use case in a different format. As an engineer, you sit in the middle of these, trying to make sense of all of them. You skim through dashboards looking for visual patterns, you copy-paste IDs around from logs to traces and back. It’s very reactive and piecemeal, and typically you refer to these tools when you have a problem — they’re designed to help you operate your code and find bugs and errors.

Modern observability has a single source of truth; arbitrarily wide structured log events. From these events you can derive your metrics, dashboards, and logs. You can visualize them over time as a trace, you can slice and dice, you can zoom in to individual requests and out to the long view. Because everything’s connected, you don’t have to jump around from tool to tool, guessing or relying on intuition. Modern observability isn’t just about how you operate your systems, it’s about how you develop your code. It’s the substrate that allows you to hook up powerful, tight feedback loops that help you ship lots of value to users swiftly, with confidence, and find problems before your users do.

You’re known for believing that observability offers a single source of truth in engineering environments. How does AI integrate into this vision, and what are its benefits and challenges in this context?

Observability is like putting your glasses on before you go hurtling down the freeway. Test-driven development (TDD) revolutionized software in the early 2000s, but TDD has been losing efficacy the more complexity is located in our systems instead of just our software. Increasingly, if you want to get the benefits associated with TDD, you actually need to instrument your code and perform something akin to observability-driven development, or ODD, where you instrument as you go, deploy fast, then look at your code in production through the lens of the instrumentation you just wrote and ask yourself: “is it doing what I expected it to do, and does anything else look … weird?”

Tests alone aren’t enough to confirm that your code is doing what it’s supposed to do. You don’t know that until you’ve watched it bake in production, with real users on real infrastructure.

This kind of development — that includes production in fast feedback loops — is (somewhat counterintuitively) much faster, easier and simpler than relying on tests and slower deploy cycles. Once developers have tried working that way, they’re famously unwilling to go back to the slow, old way of doing things.

What excites me about AI is that when you’re developing with LLMs, you have to develop in production. The only way you can derive a set of tests is by first validating your code in production and working backwards. I think that writing software backed by LLMs will be as common a skill as writing software backed by MySQL or Postgres in a few years, and my hope is that this drags engineers kicking and screaming into a better way of life.

You’ve raised concerns about mounting technical debt due to the AI revolution. Could you elaborate on the types of technical debts AI can introduce and how Honeycomb helps in managing or mitigating these debts?

I’m concerned about both technical debt and, perhaps more importantly, organizational debt. One of the worst kinds of tech debt is when you have software that isn’t well understood by anyone. Which means that any time you have to extend or change that code, or debug or fix it, somebody has to do the hard work of learning it.

And if you put code into production that nobody understands, there’s a very good chance that it wasn’t written to be understandable. Good code is written to be easy to read and understand and extend. It uses conventions and patterns, it uses consistent naming and modularization, it strikes a balance between DRY and other considerations. The quality of code is inseparable from how easy it is for people to interact with it. If we just start tossing code into production because it compiles or passes tests, we’re creating a massive iceberg of future technical problems for ourselves.

If you’ve decided to ship code that nobody understands, Honeycomb can’t help with that. But if you do care about shipping clean, iterable software, instrumentation and observability are absolutely essential to that effort. Instrumentation is like documentation plus real-time state reporting. Instrumentation is the only way you can truly confirm that your software is doing what you expect it to do, and behaving the way your users expect it to behave.

How does Honeycomb utilize AI to improve the efficiency and effectiveness of engineering teams?

Our engineers use AI a lot internally, especially CoPilot. Our more junior engineers report using ChatGPT every day to answer questions and help them understand the software they’re building. Our more senior engineers say it’s great for generating software that would be very tedious or annoying to write, like when you have a giant YAML file to fill out. It’s also useful for generating snippets of code in languages you don’t usually use, or from API documentation. Like, you can generate some really great, usable examples of stuff using the AWS SDKs and APIs, since it was trained on repos that have real usage of that code.

However, any time you let AI generate your code, you have to step through it line by line to ensure it’s doing the right thing, because it absolutely will hallucinate garbage on the regular.

Could you provide examples of how AI-powered features like your query assistant or Slack integration enhance team collaboration?

Yeah, for sure. Our query assistant is a great example. Using query builders is complicated and hard, even for power users. If you have hundreds or thousands of dimensions in your telemetry, you can’t always remember offhand what the most valuable ones are called. And even power users forget the details of how to generate certain kinds of graphs.

So our query assistant lets you ask questions using natural language. Like, “what are the slowest endpoints?”, or “what happened after my last deploy?” and it generates a query and drops you into it. Most people find it difficult to compose a new query from scratch and easy to tweak an existing one, so it gives you a leg up.

Honeycomb promises faster resolution of incidents. Can you describe how the integration of logs, metrics, and traces into a unified data type aids in quicker debugging and problem resolution?

Everything is connected. You don’t have to guess. Instead of eyeballing that this dashboard looks like it’s the same shape as that dashboard, or guessing that this spike in your metrics must be the same as this spike in your logs based on time stamps….instead, the data is all connected. You don’t have to guess, you can just ask.

Data is made valuable by context. The last generation of tooling worked by stripping away all of the context at write time; once you’ve discarded the context, you can never get it back again.

Also: with logs and metrics, you have to know what you’re looking for before you can find it. That’s not true of modern observability. You don’t have to know anything, or search for anything.

When you’re storing this rich contextual data, you can do things with it that feel like magic. We have a tool called BubbleUp, where you can draw a bubble around anything you think is weird or might be interesting, and we compute all the dimensions inside the bubble vs outside the bubble, the baseline, and sort and diff them. So you’re like “this bubble is weird” and we immediately tell you, “it’s different in xyz ways”. SO much of debugging boils down to “here’s a thing I care about, but why do I care about it?” When you can immediately identify that it’s different because these requests are coming from Android devices, with this particular build ID, using this language pack, in this region, with this app id, with a large payload … by now you probably know exactly what is wrong and why.

It’s not just about the unified data, either — although that is a huge part of it. It’s also about how effortlessly we handle high cardinality data, like unique IDs, shopping cart IDs, app IDs, first/last names, etc. The last generation of tooling cannot handle rich data like that, which is kind of unbelievable when you think about it, because rich, high cardinality data is the most valuable and identifying data of all.

How does improving observability translate into better business outcomes?

This is one of the other big shifts from the past generation to the new generation of observability tooling. In the past, systems, application, and business data were all siloed away from each other into different tools. This is absurd — every interesting question you want to ask about modern systems has elements of all three.

Observability isn’t just about bugs, or downtime, or outages. It’s about ensuring that we’re working on the right things, that our users are having a great experience, that we are achieving the business outcomes we’re aiming for. It’s about building value, not just operating. If you can’t see where you’re going, you’re not able to move very swiftly and you can’t course correct very fast. The more visibility you have into what your users are doing with your code, the better and stronger an engineer you can be.

Where do you see the future of observability heading, especially concerning AI developments?

Observability is increasingly about enabling teams to hook up tight, fast feedback loops, so they can develop swiftly, with confidence, in production, and waste less time and energy.

It’s about connecting the dots between business outcomes and technological methods.

And it’s about ensuring that we understand the software we’re putting out into the world. As software and systems get ever more complex, and especially as AI is increasingly in the mix, it’s more important than ever that we hold ourselves accountable to a human standard of understanding and manageability.

From an observability perspective, we are going to see increasing levels of sophistication in the data pipeline — using machine learning and sophisticated sampling techniques to balance value vs cost, to keep as much detail as possible about outlier events and important events and store summaries of the rest as cheaply as possible.

AI vendors are making lots of overheated claims about how they can understand your software better than you can, or how they can process the data and tell your humans what actions to take. From everything I have seen, this is an expensive pipe dream. False positives are incredibly costly. There is no substitute for understanding your systems and your data. AI can help your engineers with this! But it cannot replace your engineers.

Thank you for the great interview, readers who wish to learn more should visit Honeycomb.