- Building modern microservice-based applications using Python, Go, AI (GenAI),LLM and  AWS.
- Using Python to write functional programs and blending them with Data Engineering.
- Creating Next Gen Data Pipelines.
- Building low latency, high-throughput APIs.
- Project ownership from start to end. 
- Working with internal and external stakeholders.
- Debugging production issues across the entire stack.
- Improving engineering standards, tooling, and processes.
- Working as a Polyglot Developer.
- Leading/Mentoring team members.

Principal Software Engineer

Packt

Technical Book Title: 
Building ETL Pipelines with Python

The Book's Goal:
There are no books or free material available online which gives hands-on knowledge on how to create production-grade, robust, and resilient data pipelines using Python, which can be scaled to process  Petabytes of data. Most of the available books and free material are academic in nature and lack an in-depth explanation of scalable pipelines that adhere to industry standards.

Author

MentorCruise

Acted as a mentor at MentorCruise, guiding and supporting peers in their journey into the tech industry, sharing my expertise in Python, Cloud, and Go technologies.

Peer Mentor

Type: Development from Scratch
Role: Python Engineer (Cloud)

Technology Used: Python,Dask,Flask,React,MySql,PySpark,Docker,Go,AWS,Kubernetes,Microservices,GraphQL

Responsibilities:

-Migrated monolith applications to microservice-based services using AWS, Python, Go, and Flask.
-Wrote extendible code using TDD approach and containerized apps using Docker.
-Created production-grade POC.
-Worked with various data pipelines using AirFlow, Dask Pandas, and Numpy.
-Leveraged AWS lambda, AWS ECS, AWS Elasticache, and DynamoDB for various services.
-Created high-performance, scalable RestFul APIs using Python(Flask) /AWS API Gateway for various enterprise applications.
-Worked on Single Page Applications using React by consuming RESTFul APIs.

Senior Python Developer at Cigna

Project Type : Data Processing and ETL with Python and Pyspark 
Role: Python Data Engineer | Python Developer

- Responsible for creating a Data Pipeline using Airflow, Kafka, and Python.
- Processed billions of records with limited nodes and capacity.
- Built a persistent system similar to DaaS(Data as service) to make the data available 24x7 for users.

Technology Used: Python,Flask,Spark,Kafka,JavaScript, SQL,Docker,Go, Go,RPC,AMQP,Nameko,Microservices ,Kubernetes,Lettuce,Pandas,Numpy, Elasticsearch, Redis,React,AWS lambda, AWS ECS, AWS Elasticache, DynamoDB,AWS API Gateway,Dask,Numba,Django,Python Developer

Python Data Engineer at Cigna

Cognizant

Project: Crew Management Systems
Client: Alaska Airlines Inc. 
Type: Development from Scratch 
Role: Source Code Developer | Python Developer | Data Engineer
Technology: Python, Django, RabbitMQ, JavaScript, Shell Scripting, SQL, NoSQL
Brief:  
• Developed Distributed and Robust applications using Python, Django, RabbitMQ, and TinyDB.
• Data Analysis and Visualization using Pandas, Numpy and Matplotlib. 
• Implemented business methods in Python to retrieve and manipulate data.
• Created complex SQL queries and stored procedures for performing various CRUD operations.
• Used HTML, CSS, JSON, Yaml, and JavaScript for front-end applications. 
• Designed and developed database object parsing, dependency builder, and documenting software in Python using object-oriented features.
•Successfully Implemented RabbitMQ in a distributed fashion to make information available in real-time to help admins and decision-makers.

Full Stack Engineer (Python) at Alaska Airlines

Client: JPMorgan Chase and Co.
Type: Development from Scratch, Deployment, Testing, Support, Maintenance
Role: Senior Python Developer 
Technology Used: Python, Groovy, JavaScript, Flask, Shell Scripting, SQL, NoSQL, Sybase

Brief:  

IVoRI (Independent Valuation Reporting Infrastructure) is a JPMC strategic project. 
The IVoRI application (a reporting tool) replaces the Cognos Reporting Tool.
The project includes work streams created for the multi-year IRIS (Independent Revaluation Infrastructure) supporting the VCG (Valuation Control Group) business.
RESPONSIBILITIES:
Originated new and innovative high-performance applications using Python –WxPython | Enaml and other Python modules. 
•      Created Data Pipelines using Python.
•      Designed, Developed, and maintained the reporting App IVoRI using WxPython and many more libraries.
•	Migrated the ETL process from Informatica to Python Pandas and other custom libraries.
•	Troubleshoot issues and bug fixes, and implement enhancements as required. 
•	Spearheaded running of Unit Test Cases for better test coverage. 
•	Implemented the TDD process.
•	Collaborated on analysis, coding, and migration of existing applications. 
•	Delivered superior client services through interfacing with clients for clarifications, feedback, deliveries, and project planning.

Senior Python Developer at JPMorgan Chase & Co.

Project - Amex Personalization 
Client: American Express UK.
Type: Development
Role: Developer
Technology Used: Java, Python, SQL 

Brief: 

The Personalization Utility is aimed at transforming the process that American Express markets its products and services to its clients. This project presents offers and content most relevant to the customers. The project simultaneously aims to deliver the greatest benefit to the enterprise as a whole as it allows American Express to tailor its services and products to meet customer preferences efficiently and make interactions faster and easier.

RESPONSIBILITIES:
•	Served as lead Source Code Developer using Python and Java.
•	Automated multiple data-related tasks saving 80 man-hours per week.
•	Automated the content management tasks to update the seasonal card offers.

Project: Employee Roster Management 
Client: 3M Company
Type: Development from Scratch.
Role: Developer
Technology Used: Java, Python, MYSQL

RESPONSIBILITIES:
•	Utilized IT expertise as a full-stack developer using Java, Javascript, Spring, HTML, and CSS.
•	Cultivated strong relationships with clients and kept communications channels flowing freely for frequent interaction for progressive status.
•	Performed code coverage tasks for the assigned modules; monitored thousands of lines of code to ensure smooth software operations.

Backend Engineer at Amex

Opinions expressed are solely my own and do not express the views or opinions of my employer.

I am a seasoned professional who can design, develop, and deliver scalable and robust applications using Python, Go, Cloud, and AI. I am also an author, a GenAI strategist, a data professional and an architect, with a passion for innovation and learning.

Currently, I am working as a Principal Software Engineer at ADP, one of the world's largest providers of human resources and payroll solutions. In this role, I am leading the development of modern microservice-based applications using Python, Go, AWS, and AI, as well as building ETL pipelines with Python to process strategic data. I work collaboratively with senior leaders, clients, SRE and Security teams to ensure the successful completion of projects that focus on cloud, machine learning, artificial intelligence, and microservices. I am driven by the mission of creating value for customers and stakeholders, and empowering my team members with my expertise and mentorship.

I'm updating my collection of free API courses, and they're better than ever. 

These courses have helped me and a ton of other people step up their tech game, and I think they could do the same for you.

I put together these courses to help you boost your API skills and get ready for job interviews and more. 

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If you're curious about APIs and want to understand them better, these courses are perfect:

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2. API Made Easy: A simplified approach to understanding APIs. (lnkd.in/er9JiGxw)

3. API Methods: Explore different API methods and their uses. (lnkd.in/ey9v7-hU)

4. API Terms: Get familiar with common API terminologies. (lnkd.in/eRsPMzpd)

5. API Authentication: Learn how to secure your APIs with authentication. (lnkd.in/eNPfpAdE)

6. API Status Codes: Understand the meaning behind API status codes. (lnkd.in/egXizUrS)

7. REST API vs GraphQL: Compare and contrast these two popular API architectures. (lnkd.in/eZHREdgC)

8. API Integration: Discover how to integrate APIs into your projects. (lnkd.in/eDASPP5m)

9. API Integration in Depth: Take a deeper dive into API integration. (lnkd.in/eZwFVrH7)

10. API Testing: Learn how to test APIs for reliability and performance. (lnkd.in/emgmWJqH)

11. APIs with Python: Build APIs using the Python programming language. (lnkd.in/eM23ah2y)

12. API Scaling: Explore strategies for scaling APIs to handle high traffic. (lnkd.in/e3mZSvmn)

13. Building Robust APIs: Learn best practices for developing reliable APIs. (lnkd.in/eBXzbFyg)

14. APIs with Postman: Use Postman to design, test, and document APIs. (lnkd.in/ezue3d4B)

15. Testing APIs with Postman: Learn how to test APIs using Postman. (lnkd.in/eCPnGTGi)

16. API Security: Discover techniques for securing your APIs. (lnkd.in/e79ZYfPa)

17. APIs for Everyone: An accessible introduction to APIs for all skill levels. (lnkd.in/e4WGDffA)

Start learning today and give your career a big boost! Keep following me for more cool stuff like this.

Happy learning!

Follow me here if you like my content - Brij kishore Pandey

Brij kishore Pandey

Essential Data Terms to Know in 2024:

1. Data Mining: The process of discovering hidden patterns, correlations, and insights in large datasets using methods like statistics, machine learning, and database systems.

2. Data Analytics: The science of examining raw data to draw conclusions, uncover trends, and support decision-making. It involves techniques such as data mining, predictive analytics, and statistical analysis.

3. Data Visualization: The practice of translating complex data into visual representations like charts, graphs, and maps, making it easier to understand and communicate insights.

4. Data Contract: A formal agreement between a service provider and a client that defines the structure, format, and constraints of the data being exchanged, ensuring compatibility and reliability.

5. Data Modeling: The process of creating a conceptual representation of data structures, defining entities, attributes, and relationships to organize and manage data effectively.

6. Data Integration: The practice of combining data from disparate sources into a unified view, enabling a comprehensive analysis of information across an organization.

7. Data Cleaning: The process of identifying and correcting inaccurate, incomplete, or irrelevant data to improve data quality and ensure reliable analysis.

8. Data Warehouse: A centralized repository that stores structured data from various sources, optimized for querying, reporting, and data analysis.

9. Data Mart: A subset of a data warehouse focused on a specific business function or department, designed for faster query performance and ease of use.

10. Data Lake: A storage architecture that holds vast amounts of raw, unstructured, and semi-structured data in its native format until needed for analysis.

11. Delta Lake: An open-source storage layer that brings reliability, scalability, and performance improvements to data lakes, enabling ACID transactions and data versioning.

12. Data Pipeline: A series of processes that move and transform data from source systems to target destinations, ensuring data quality, consistency, and availability.

13. Data Mesh: A decentralized approach to data architecture that treats data as a product, with domain-specific teams responsible for data ownership, quality, and governance.

14. Data Lake House: A modern data architecture that combines the flexibility of data lakes with the structure and performance of data warehouses, enabling diverse workloads and use cases.

15. Data Swamp: An unmanaged, chaotic data environment where data is stored without proper organization, governance, or quality control, making it difficult to derive value.

16. Data Fabric: An architecture that weaves together data from multiple sources, creating a unified, integrated view of an organization's data assets for seamless access and analysis.

#data #dataengineering #softwareengineering  

Single Sign-On (SSO) Simplified: Secure Access Made Easy 

In today's complex IT landscape, managing user access across multiple applications can be a daunting task. 

That's where Single Sign-On (SSO) comes to the rescue! 🦸‍♂️

With SSO, users can access a suite of applications with just one set of login credentials, eliminating the need for multiple usernames and passwords. 

This not only improves security but also enhances user convenience. 

Here's a quick rundown of how SSO works:

1. Identity Provider Setup 🔑
   - An organization sets up an SSO server that acts as an identity provider.
   - This server becomes the central hub for managing user credentials and authentication.
   - Popular SSO solutions include Okta, OneLogin, and PingIdentity.

2. Application Integration 🌐
   - Applications that want to leverage SSO are configured as service providers.
   - These applications establish a connection with the SSO server to validate user logins.

3. User Authentication 🔐
   - When a user logs into the SSO server, their credentials are authenticated.
   - Upon successful authentication, an encrypted token is generated.

4. Seamless Access 🚪
   - When the user attempts to access a connected application, they are redirected to the SSO server.
   - If the token is valid, the SSO server sends it to the application for authentication.

5. Token Decryption 🔓
   - The application decrypts the token to verify the user's identity.
   - The user is then granted access to the application without the need to re-enter credentials.

6. Automated Provisioning 🤖
   - The token often includes user attributes like name, email, and roles.
   - This allows the application to provision access based on the user's profile without separate setup.

SSO offers several key benefits:
✅ Convenience for end users with a single set of credentials
✅ Enhanced security through centralized authentication
✅ Rapid access to new applications without re-registration
✅ Automated provisioning based on user attributes


What are your thoughts on SSO? 

The Journey of a Web Page: From Request to Render

𝗬𝗼𝘂𝗿 𝗕𝗿𝗼𝘄𝘀𝗲𝗿'𝘀 𝗥𝗼𝗹𝗲:
1. 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵𝗶𝗻𝗴 𝗮 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻: Your browser initiates communication by sending a connection request to the website's server.
2. 𝗥𝗲𝗾𝘂𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗮𝗴𝗲: Once connected, your browser sends an HTTP request for the specific page you want to access.
3. 𝗣𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝘁𝗼 𝗥𝗲𝗻𝗱𝗲𝗿: The browser's engine prepares to process the incoming data and manage the page loading.

 𝗟𝗼𝗰𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲:
1. 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗲𝗮𝗿𝗰𝗵: The browser begins its search for the website at the domain name system (DNS) level.
2. 𝗡𝗮𝗿𝗿𝗼𝘄𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗰𝗼𝗽𝗲: The search narrows down to the specific DNS record containing the website's name.
3. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗱𝗱𝗿𝗲𝘀𝘀: The browser retrieves the website's IP address, enabling a direct request.

𝗧𝗵𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲'𝘀 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲:
1. 𝗦𝘁𝗮𝘁𝘂𝘀 𝗨𝗽𝗱𝗮𝘁𝗲𝘀: The website responds with status codes, providing updates on the request's progress:
   - 1xx: Preparation stages
   - 2xx: Success! The page is being sent
   - 3xx: Redirection to another URL
   - 4xx: Client-side error (e.g., 404 Not Found)
   - 5xx: Server-side error

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗪𝗲𝗯 𝗣𝗮𝗴𝗲:
1. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: The browser starts by processing the HTML code to establish the page's basic structure.
2. 𝗩𝗶𝘀𝘂𝗮𝗹 𝗦𝘁𝘆𝗹𝗶𝗻𝗴: Next, it interprets the CSS code, which determines the page's appearance.
3. 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: The browser combines HTML, CSS, and JavaScript (for interactivity) to fully assemble the page.
4. 𝗙𝗶𝗻𝗮𝗹 𝗥𝗲𝗻𝗱𝗲𝗿𝗶𝗻𝗴: Lastly, it calculates the layout and renders the page on your screen.

This simplified overview captures the essence of web page loading, a complex process we often take for granted.



Follow me for more discussions on AI and technology. 😊

Implementing CI/CD in Agile Teams

By adopting CI/CD, Agile teams can deliver high-quality software to users more quickly and reliably. This comprehensive guide outlines the step-by-step process for implementing CI/CD in your Agile team.

Step 1: Product Owner Crafts Engaging User Stories
The Product Owner plays a crucial role in creating user stories that clearly define the features and functionality to be developed. These stories serve as the foundation for the development process.

Step 2: Development Team Brings User Stories to Life
Using tools like Jira or Rally, the development team manages and tracks the progress of user stories. This ensures that the team stays aligned and focused on delivering value to the end-users.

Step 3: Foster Transparency and Collaboration
Effective communication is key to the success of any Agile team. Tools like Slack or Teams facilitate seamless collaboration, while version control systems like GitHub or Bitbucket ensure transparency and enable efficient code management.

Step 4: Implement Continuous Integration
Continuous Integration (CI) is the practice of regularly merging code changes into a central repository. Tools like SonarQube and DevOps are used to perform static code analysis, trigger builds, and provide valuable feedback to the development team.

Step 5: Ensure Code Quality with Unit Tests and Code Coverage
Unit tests and code coverage are essential for maintaining code quality. Tools like JaCoCo and JUnit help implement unit tests and measure code coverage. Build and deployment automation can be achieved using Jenkins and Maven.

Step 6: Rigorous Testing and Quality Assurance
To ensure the software meets the highest quality standards, tools like Selenium are used for regression and User Acceptance Testing (UAT). Nexus or Artifactory can be utilized as binary repositories to store and manage dependencies.

Step 7: Prioritize Security and Infrastructure Management
Security is a critical aspect of any software development process. Tools like Qualys can be employed to conduct routine security scans. Infrastructure management tools such as Terraform and Ansible help automate the provisioning and configuration of resources.

Additional Tips for Successful CI/CD Implementation:
- Start small and gradually expand your CI/CD pipeline as your needs grow.
- Ensure buy-in from your team and communicate the benefits of CI/CD clearly.
- Automate as much as possible to improve the efficiency of your CI/CD pipeline.
- Strive to reach key automation milestones, including unit test execution, build automation, code coverage checks, code quality checks, security scanning, automated deployments with gating, feedback automation to production teams, binary storage automation into a repo manager, and infrastructure setup automation.

Let's keep things simple: CICD with Agile is complex, don't make it harder than it needs to be.

As tech professionals, understanding database options is crucial. Today, we'll explore the main types and their best uses:

Join me for a  FREE workshop and discover how to dramatically boost your MongoDB performance by 100x for Analytics and AI.

✅ RSVP here - https://brij.guru/learn

Here's what you'll learn:

• Proven techniques to optimize your MongoDB queries
  
• Configuration tweaks for a significant performance jump
  
• Practical strategies to handle large datasets efficiently
  
• How to  to achieve 100x faster aggregations and analytics on your JSON data
  
• See real-world benchmarks showcasing the performance difference on sample queries.
  
• Learn about real-time CDC integration from MongoDB® using a native CDC connector.

Relational Databases (RDBMS):  The workhorses! Think MySQL, Oracle, SQL Server. These organize data in tables with clear relationships, making them perfect for complex queries and data integrity. They're the go-to for most business applications.

NoSQL Databases:  Flexibility is key here! MongoDB and Cassandra are popular examples. NoSQL handles unstructured data with ease and boasts impressive performance for massive datasets. Ideal for big data, content management, and real-time applications.

Graph Databases:  Let's get connected! Neo4j shines when it comes to networked data and social relationships. Graph databases excel at fraud detection, recommendations, and knowledge graphs.

Time Series Databases:  All about the time! InfluxDB is built for timestamped, sequenced data. It's a great fit for IoT, DevOps, and sensor data analysis.

Object Databases: Think objects, not tables! db4o is an example here. Object databases excel at working with media files, search engines, and engineering design systems.

Choosing the Right Database:  It's all about your needs! Data structure, relationships, scalability, and performance all play a role. A solid grasp of the options empowers you to architect optimal systems and leverage the best tools.


Have I overlooked anything? 

Please share your thoughts—your insights are priceless to me.

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5. Introduction to Postman API : https://lnkd.in/ewqGKRPU
   
6. API For Beginners : https://lnkd.in/eRVAQDdX
   
7. RestAPI Manual Testing For Beginners : https://lnkd.in/ewE7Y4Xx
   
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♻️ Repost for others

HTTP Methods: Beyond the Basics 

In this post, we'll explore 9 of the most popular HTTP methods and their use cases. 

By understanding these methods, you can leverage the full potential of HTTP and build more robust, efficient, and secure web applications. 

1️⃣ GET Method: This is the most common method, used for retrieving data from a server. When you enter a URL in your browser, it sends a GET request to fetch the webpage. GET requests should never modify data on the server.

2️⃣ POST Method: POST is used to send data to a server to create a new resource. For example, when you submit a form on a website, the data is typically sent via a POST request. The data is included in the body of the request.

3️⃣ PUT Method: PUT is used to update an existing resource on the server. It replaces the entire resource with the new data provided in the request body. If the resource doesn't exist, PUT can also be used to create it.

4️⃣ PATCH Method: Similar to PUT, PATCH is used to modify an existing resource. However, instead of replacing the entire resource, PATCH only updates the specified parts of the resource. This is useful when you only need to make partial updates.

5️⃣ DELETE Method: As the name suggests, the DELETE method is used to remove a resource from the server. It permanently deletes the specified resource.

6️⃣ HEAD Method: HEAD is similar to GET, but it only retrieves the headers of the response, not the body. This is useful for checking the status of a resource or its metadata without downloading the entire content.

7️⃣ OPTIONS Method: OPTIONS is used to describe the communication options available for a given resource. It allows the client to determine the supported HTTP methods, headers, and other capabilities of the server.

8️⃣ TRACE Method: TRACE is used for diagnostic purposes. It echoes back the received request, allowing the client to see if any changes or additions were made by intermediate servers.

9️⃣ CONNECT Method: CONNECT is used to establish a tunnel to the server, typically for secure connections using SSL/TLS protocols. It is commonly used by proxies to facilitate HTTPS connections.

By mastering these HTTP methods, you can build more sophisticated web applications, optimize network communication, and ensure proper usage of the HTTP protocol. 

Do you have any experience using these less common HTTP methods in your projects? 

API Design Best Practices: A Technical Deep Dive

Beyond the foundational concepts, crafting a stellar API requires attention to detail.

Here's a technical breakdown of best practices to elevate your API design:

𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗗𝗲𝘀𝗶𝗴𝗻:

• Nouns in URLs:  Use descriptive nouns (e.g., /𝚞𝚜𝚎𝚛𝚜, /𝚙𝚛𝚘𝚍𝚞𝚌𝚝𝚜) to represent resources, not verbs indicating actions.
  
• HTTP Verbs:  Leverage HTTP verbs (GET, POST, PUT, DELETE) to denote actions on resources (GET: retrieve,  POST: create, PUT: update, DELETE: remove).
  
• Plural Nouns for Collections:  Identify collections of resources with plural nouns (e.g., /𝚞𝚜𝚎𝚛𝚜, not /𝚞𝚜𝚎𝚛).
  
• Nested Resources:  Model hierarchical relationships with nested URLs (e.g., /𝚞𝚜𝚎𝚛𝚜/:𝚞𝚜𝚎𝚛𝙸𝚍/𝚙𝚘𝚜𝚝𝚜).

𝗗𝗮𝘁𝗮 𝗙𝗼𝗿𝗺𝗮𝘁𝘀 & 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴:

• Standardized Formats:  Opt for industry-standard data formats like JSON or XML for request and response payloads.
  
• Descriptive Error Codes:  Utilize HTTP status codes (e.g., 200: success, 400: bad request, 404: not found) and provide clear error messages for troubleshooting.
  
• Validation:  Implement robust input validation on the server-side to prevent malformed requests.

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆:

• Caching:  Utilize caching mechanisms to reduce server load and improve response times for frequently accessed data.
  
• Pagination:  Enable result pagination (e.g., limit, offset parameters) to handle large datasets efficiently.
  
• Rate Limiting:  Implement rate limiting to prevent abuse and ensure fair access for all users.

𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:

• Authentication & Authorization:  Enforce proper authentication and authorization mechanisms to control access to sensitive data and functionalities.
  
• HTTPS:  Always enforce HTTPS for secure communication and data encryption.

𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:

• Versioning:  Implement API versioning to manage changes and ensure compatibility with existing integrations.
  
• Documentation:  Provide comprehensive API documentation covering endpoints, parameters, responses, and code samples.
  
• Testing:  Write thorough unit and integration tests to ensure API functionality and maintainability.

By adhering to these technical best practices, you can create a robust, well-documented, and secure API that empowers developers and fosters a thriving developer ecosystem.

Microservices architectures offer a scalable and resilient approach to building applications, but their complexity can make them challenging to comprehend, especially for those new to the concept. 

To overcome this hurdle, diagrams and visualizations can be employed to illustrate how microservices interact and collaborate, making the architecture more tangible and accessible.

Another effective way to simplify microservices is through the use of analogies. 

For instance, a microservices architecture can be likened to a city, where each building represents a microservice, and the roads connecting them symbolize the APIs that facilitate communication between the services.

𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Consider the diagram below, which showcases a microservices system for an online shopping cart. The system comprises the following microservices:

1. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 microservice: Responsible for managing customer accounts and checkout orders.
2. 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗖𝗮𝗿𝘁 microservice: Handles the storage of items that a customer has added to their shopping cart.
3. 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 microservice: Monitors the inventory levels of products.
4. 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 microservice: Facilitates payment processing.
5. 𝗦𝗵𝗶𝗽𝗽𝗶𝗻𝗴 microservice: Manages the shipping of orders to customers.
6. 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 microservice: Generates reports on sales and other relevant metrics.

In this scenario, the Customer microservice is tasked with handling customer requests to add items to their shopping cart, complete their order, and manage their account information. 

The Shopping Cart microservice is responsible for storing the items in a customer's cart and granting access to these items to other microservices. 

The Inventory microservice ensures that products are in stock and prevents the sale of out-of-stock items. 

The Payment microservice processes customer payments, while the Shipping microservice oversees the delivery of orders to customers. 

Lastly, the Reporting microservice generates reports on sales and other pertinent metrics.

These microservices communicate with each other through well-defined APIs and Pub/Sub protocols. 

For example, the Customer microservice must call the Shopping Cart API to add items to a customer's cart and to finalize an order. 

The Shopping Cart API, in turn, must call the Inventory API to verify that a product is in stock before adding it to the customer's cart. 

Similarly, the Payment API must call the Shipping API to initiate the shipping process once a payment has been successfully processed.

By leveraging diagrams and analogies, microservices architectures can be made more comprehensible and easier to visualize, enabling developers and stakeholders to better understand and work with these complex systems.

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Our careers are important, but they shouldn't come at the expense of our mental well-being.

If you're constantly stressed or burnt out, it might be time to re-evaluate your priorities.

Your health and happiness are worth more than any job.

#mentalhealth #wellbeing #worklifebalance

Navigating the Path to Becoming a Software Developer in 2024 and Beyond

As someone who has been on the software development journey for several years, I'm always eager to help guide others who are starting out or looking to build new skills. 

This roadmap infographic provides an excellent high-level overview of the landscape and key areas to focus on.

Some key takeaways and thoughts:

- Web development is a great place to start. Fundamental skills like HTML, CSS and JavaScript open up tons of opportunities, and frameworks like React and Angular help you build interactive applications.

- Do not neglect databases and SQL! Data is central to most applications and understanding how to efficiently store and query it is critical. Comfort with a few popular databases is very valuable.

- Get familiar with version control (Git), as it's an essential collaboration and code management tool. Also understand methodologies like Agile and Scrum for working effectively on teams.

- Continual learning is a must in this field. Pick a language or two to gain proficiency in and build projects, but expect to keep expanding your skillset. Just-in-time learning when you need a new tool for a job is also key.

What recommendations would you make for developers starting their journey? 

Do you agree with the high-priority skills emphasized here? 

Let me know your thoughts!

Choosing the right structure for your project is like laying down a strong foundation for a building. 

It's really important because it affects a lot of things later on, like how easy it is to make your project bigger, keep it running smoothly, make it fast, and keep it safe.

Here's a quick look at six common structures:

1. 𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵𝗶𝗰: This is the simplest form, where everything is in one big piece. It's easy to get but can get tricky to handle as it gets bigger.
   
2. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿 - 𝗪𝗼𝗿𝗸𝗲𝗿: Think of this as a small twist on the monolithic structure. There's a main server that handles all the adding and changing, and then there are helpers that just handle viewing. This is good for projects where a lot of people are just looking at stuff.
   
3. 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: These are like small, independent pieces that talk to each other. This makes it easy to change parts without messing with everything else, but it can get complicated.
   
4. 𝗠𝗩𝗖 (𝗠𝗼𝗱𝗲𝗹-𝗩𝗶𝗲𝘄-𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿): This is a popular way to organize web projects. It splits things up into three parts: the data part, the display part, and the part that handles the logic.
   
5. 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻: In this setup, different parts of the project react to signals. It's good for making parts work independently and can speed things up, but it might be hard to keep track of what's happening.
   
6. 𝗟𝗮𝘆𝗲𝗿𝗲𝗱: This method stacks different parts of the project based on their role, like layers in a cake. It's great for keeping things organized, but it might slow things down a bit.

Choosing the right one depends on what you need for your project, like how big it might get, how easy it needs to be to change things, how fast it needs to run, and how safe it needs to be.

There's no one-size-fits-all answer; the best choice really depends on what your project needs.

I'm curious to know what you think about these structures. 

Have you used any of them? 

What was your experience like?

Let me know in the comments!

Stay tuned by following me here - Brij kishore Pandey

Secure Your API with These 16 Practices

As someone deeply involved in API security, I've compiled a list of 16 best practices that I've found effective in safeguarding APIs:

 1. Authentication 🕵️‍♀️ - Verifies user identities accessing APIs.
    
 2. Authorization 🚦 - Sets permissions for authenticated users.
    
 3. Data Redaction 🖍️ - Hides sensitive data for enhanced protection.
    
 4. Encryption 🔒 - Secures data so only authorized entities can access it.
    
 5. Error Handling ❌ - Manages faulty responses without exposing sensitive information.
    
 6. Input Validation & Data Sanitization 🧹 - Ensures the integrity of input data and removes malicious elements.
    
 7. Intrusion Detection Systems 👀 - Vigilantly monitors networks for unusual activities.
    
 8. IP Whitelisting 📝 - Allows API access exclusively from trusted sources.
    
 9. Logging and Monitoring 🖥️ - Maintains comprehensive logs and consistently oversees API activities.
    
10. Rate Limiting ⏱️ - Controls user requests to prevent system overload.
    
11. Secure Dependencies 📦 - Regularly checks third-party code for vulnerabilities.
    
12. Security Headers 📋 - Protects against specific attack types like XSS.
    
13. Token Expiry ⏳ - Frequently renews tokens to block unauthorized access.
    
14. Use of Security Standards and Frameworks 📘 - Provides a structured approach to API security.
    
15. Web Application Firewall 🔥 - Defends against HTTP-based threats.
    
16. API Versioning 🔄 - Manages different API versions for smooth transitions and updates.

👉 Follow my journey and insights here - Brij kishore Pandey 

Note: Apologies for the typo in "Intrusion Detection Systems" earlier mentioned as "Intrution." Thanks for understanding!

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