High Level

𝗦𝘁𝗲𝗽 𝟭: 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀

• Networking Basics (HTTP, TCP/IP, Load Balancing)
• API Design (REST vs GraphQL, Rate Limiting, Authentication)
• Database Basics (SQL vs NoSQL, Indexing, Partitioning)
• Caching Concepts (Strategies, In-Memory Caching, CDN)

𝗦𝘁𝗲𝗽 𝟮: 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲

• Vertical vs Horizontal Scaling
• Load Balancing Techniques
• Database Replication & Sharding
• Asynchronous Processing & Messaging Queues

𝗦𝘁𝗲𝗽 𝟯: 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀

• Monolithic vs Microservices Architecture
• Event-Driven vs Request-Response Model
• CQRS and Event Sourcing
• Fault Tolerance & High Availability

𝗦𝘁𝗲𝗽 𝟰: 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁

• CAP Theorem & Consistency Models
• Storage Systems (Relational, NoSQL, Distributed)
• Data Partitioning & Replication
• Indexing & Query Optimization

𝗦𝘁𝗲𝗽 𝟱: 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲

• Design and document real-world systems
• Explain trade-offs in your design decisions
• Practice mock system design interviews
• Get feedback and refine your approach

➤ 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗳𝗼𝗹𝗹𝗼𝘄:

• 𝗬𝗼𝘂𝘁𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀

1. Shrayansh Jain: https://lnkd.in/dUABhnbT
2. Arpit Bhayani: https://lnkd.in/du8RyS7R
3. Hussein Nasser: https://lnkd.in/dubTqr22
4. Gaurav Sen: https://lnkd.in/dAPhQyBp

• loosely coupled architecture
• strong consistency & eventual consistency
• observability, logging & monitoring
• troubleshooting

• database tunings & optimization
• normalization & denormalization
• db availability & resiliency

• Business Objective / Problem Statement
• Use case Requirements - How are trying to resolve the problem / Business Impact
• SaaS Requirements - Multi Tenancy
• Security Requirements - IDP Requirements / Encryption needs /
• Capacity Requirements - Reliability/Availability, Operational Capability
• Consumers / Target Audience / User Base
• Mode of consumption - Mobile App/Mobile View/Tablet View/Desktop View/Integrations
• Transformation Needs / Application Migration / Data Migration
• Delivery Timelines / Go Live
• Budget Expectations
• Deployment Preferences - OnPrem/OnCloud/Hybrid
• Database Stack - Db Storage Requirements, Data Analytics / Reporting Capability
• App Stack - VM Instances / Container / Functions

The dual write problem occurs when your service needs to write to two external systems in an atomic fashion. A common example would be writing state to a database and publishing an event to Apache Kafka. The separate systems prevent you from using a transaction, and as a result, if one write fails it can leave the other in an inconsistent state. This is an easy trap to fall into.

Thankfully, there are 3 ways to avoid this mess!

However, we have to be careful to avoid solutions that seem valid on the surface but just move the problem.

• Emitting Events
• The Dual Write Problem
• Invalid Solution: Emit the Event First
• Invalid Solution: Use a Transaction
• Change Data Capture (CDC)
• The Transactional Outbox Pattern
• Event Sourcing
• The Listen to Yourself Pattern

• Eliminating the Double Write Problem in Apache Kafka Using the Outbox Pattern
• Dual Writes - The Unknown Cause of Data Inconsistencies
• The Challenges of Event-Driven Architecture: Dealing with the Dual Write Anti-Pattern

Contemporary applications employ Event-Driven Microservices to harness the benefits of autonomous deployment and scalability offered by Domain services while maintaining loose coupling between these services.

If your application adopts a Microservices Architecture, with each Domain service managing its own data in dedicated Datastores and communicating with other services through asynchronous means, often by emitting Domain events for activities like participating in a Saga operation (such as a long-running business transaction) or data replication across services, there is a significant likelihood that you have implemented this communication approach using the Dual Write Anti-Pattern.

When considering this pattern, whether it's something to be concerned about or not, here's a brief response for situations when you should not be concerned.

If it’s OK for your application to sometimes lose the Business domain events, causing data inconsistencies across services then you can absolutely ignore this, but if this is not the case, then you need to understand this anti-pattern well and fix it.

The Dual Write Anti-Pattern refers to a scenario in which a domain service needs to perform write operations on two distinct systems, such as data storage and event brokers, within a single logical business transaction. The goal is to achieve eventual data consistency across various services. However, there is no assurance that both data systems will always be updated successfully, or conversely, that neither will be updated during this process.

Yeah, you are thinking absolutely right — we want to achieve something like Database ACID transaction, but across 2 different kind of systems. And we cannot leverage Distributed Transaction implementation because either it is not feasible or it cannot be implemented because of inherent Scalability issues with Distributed Transaction frameworks.

Let’s understand this better with a simple use case

In the provided scenario, the business objective is quite straightforward: whenever a user publishes a Feed post, it's essential to have the Content Moderation services examine the post. If any concerns are detected, the user should receive a notification, prompting them to either delete or edit the post. The Feed Microservice is responsible for managing Feed post requests from the User Interface. It not only stores the feed post data in the Database but also triggers the publication of a FeedPosted Domain event on the Event Broker. This event serves as a signal for the Content Moderation Services to take appropriate actions.

Moreover, the developer has taken meticulous steps to ensure that this entire process appears as a unified and cohesive business transaction. The pseudocode snippet below illustrates this approach:

(Original pseudocode or implementation details can be provided if needed.)

In the provided pseudocode, the following scenarios ensure expected behavior:

1. When both the Database and Event Broker are functioning correctly, data is successfully written to both systems.
2. In the event of an error occurring during the write operation to the Event Broker, causing the catch() block to be executed, data is not written to either of the systems.

Only in the edge case, when the Database transaction commit fails (and it can very well fail by the way), the requirement is not met. Event is written to Event Broker but Data is not saved in the Database.

And this could very well lead to a User Experience or Reliability issue where the user was prompted with an error on the User Interface that Feed Post could not be saved successfully and an email was sent to the user asking to delete the post or update the post because Content Moderation service did not find the feed post appropriate.

So, what do we do now to handle the situation? One of the solution we ruled out was — leveraging Distributed Transaction. So, what next? Here are some of the possible options

In this scenario, after data has been successfully written to the database, the service tries to also write it to the Event Broker. Ideally, this works smoothly, but if it fails due to any reason, you can store the event in a persistent storage, which might even be the same database. Then, you can set up a scheduled task (like a Cron Job) to periodically retry publishing the event to the Event Broker. While this approach seems logical, it does have some drawbacks.

This approach could potentially lead to problems with the sequencing of domain event publication. For instance, if the publishing of a "Create Feed Post" event fails, but a user successfully deletes the same feed post, causing it to be sent to downstream systems, you'll encounter a scenario where a "FeedDeleted" event is dispatched first, followed by a "FeedCreated" event, which might be sent by a Cron Job at a later time. Such a scenario has the potential to create data consistency problems. Therefore, if maintaining a specific order of events is a crucial requirement for your system, this approach may not be suitable. If an event that is supposed to be published at a later time cannot be stored in durable storage due to certain issues, there is a risk of losing those events. Another approach is to keep a marker in the business record within the database table to indicate whether the event has been synchronized. However, this approach essentially ties your event publishing requirements to the primary business entity, which may not be ideal.

One of the recommended strategies for managing the Dual Write Anti-Pattern involves a two-step process. In this approach, a service first stores the business data in the database within a single database transaction. Simultaneously, it also records the event that needs to be published in a separate table known as the Outbox Table. This approach capitalizes on the ACID properties of the database, ensuring that the business data is saved in the database as part of a unified transaction.

However, the event intended for publication to the Event Store is not immediately published at this point. Instead, an external process is responsible for reading the records from the Outbox Table. Subsequently, it publishes the event to the Event Store. This process ultimately leads to the achievement of eventual data consistency and effectively addresses issues associated with the Dual Write problem.

With this approach -

• There is a guarantee that events will be published eventually to the Event Store
• Will never be lost, even if Event Store is not available at the time of publishing the event
• Ordering of the events can be ensured

But these benefits does not come for free

• You need to put additional efforts to write this external processor which reads the data from Outbox Table and publishes to Event store
• This external component also becomes the Single Point of Failure, hence needs great monitoring and automated corrective measures to handle the failures should something goes wrong

Here is a pictorial representation of this approach

There are different ways by which we can implement the Outbox pattern and some of the design level issues which one needs to think thru in terms of

• If a service happens to publish multiple domain level entities, then would I need one Outbox table per Domain entity or one Outbox table per service.
• How would I perform clean up of the Outbox table else it will grow infinite

• https://www.linkedin.com/pulse/event-driven-architecture-complex-aspects-dual-write-momen-negm/

Legacy systems may have the session state stored in the application server itself, it cause the single point of failure when that system is crashed. This SPoF can be mitigated by providing additional web server to handle the load should any of the server is crashed.

ELB can be employed to manage the additional web servers of the system

But the application itself not developed to handle this scalability as the user session is still stored on the web server itself, therefore request should be routed to the respective web server where the user's session is stored.

ELB can be configured to remember where the user's session is stored. so that it can route the request accordingly. This is called as** Sticky Session.**

But still this is not an optimal solution, should any server crash, the session information on that server also lost.

Make the application as stateless as possible, store the session state externally (i.e outside the web server)

Store the session state outside the web server (such as DynamoDB) and web servers will use this storage to handle user session.

Message queue is being used to pass messages between components as event triggers.

workflow orchestration mechanism is about configuring a list of actions based on trigger point (criteria to launch the workflow action). The actions are performed asynchronously and the last state of the action is persisted in centralized storage.

Autoscaling (linkedin.com) Setting up Auto Scaling: Part 1 (linkedin.com) Setting up Auto Scaling: Part 2 (linkedin.com)

Testing the Auto Scaling (linkedin.com)

• Infra automation (creation of infra resources), helm chart/terraform
• Schema changes deployment automation
• Release rollout automation (Blue/Green Deployment) - https://github.com/FullstackCodingGuy/Developer-Fundamentals/wiki/Deployment-Strategies

Relational database design focuses on the normalization process without regard to data access patterns. However, designing NoSQL data schemas starts with the list of questions the application must answer. It’s important to develop a list of data access patterns before building the schema, since NoSQL databases offer less dynamic query flexibility than their SQL equivalents.

To determine data access patterns in new applications, user stories and use-cases can help identify the types of query. If you are migrating an existing application, use the query logs to identify the typical queries used.

While it’s possible to implement the design with multiple NoSQLDb tables, it’s unnecessary and inefficient. A key goal in querying NoSQLDb data is to retrieve all the required data in a single query request. This is one of the more difficult conceptual ideas when working with NoSQL databases but the single-table design can help simplify data management and maximize query throughput.

Use Adjacency list design pattern

• https://aws.amazon.com/blogs/compute/creating-a-single-table-design-with-amazon-dynamodb/
• https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-adjacency-graphs.html

How Do I Navigate Ambiguity?

🔹 1. Break Down the Problem – Identify what’s known vs. unknown.

🔹 2. Ask the Right Questions – Clarify goals with stakeholders.

🔹 3. Use Data to Reduce Uncertainty – Leverage user insights, A/B tests, and MVPs.

🔹 4. Prioritize Quick Wins – Deliver small, testable solutions before committing to big changes.

🔹 5. Stay Flexible & Communicate – Keep teams aligned and iterate based on feedback.

🔹 Example: In a past project, the goal was to “improve user engagement,” but the problem was undefined. By analyzing heatmaps, drop-offs, and user feedback, we found that slow page loads were the main issue. Instead of a major redesign, we optimized performance first—leading to a 20% improvement in engagement.

Ambiguity is inevitable in technical work, but structured thinking and communication help teams move forward confidently.

Strategy: Break Vague Requirements into Actionable Tasks

Strategy: Use a Decision Matrix for Prioritization

Strategy: Build Prototypes & Iterate

Strategy: Use a "Tech Brief" to Align the Team

Strategy: Facilitate "Red Team" Reviews

Strategy: Define "Good Enough" Instead of Perfect

Prioritizing initiatives requires a structured approach to ensure that resources, time, and effort are allocated to the most impactful work. Here are some effective frameworks and techniques to help prioritize effectively:

Prioritization_Template.xlsx

Two types of challenges you can think about

• Technical Challenges
• Non Technical Challenges

Some of the examples given below

Unclear Requirements & Ambiguity

Challenge: Stakeholders often had vague or evolving requirements, making it hard to define scope.

Solution:

• Used discovery workshops to clarify needs.
• Created low-fidelity prototypes for quick feedback.
• Applied Agile principles to adapt as requirements changed.

🔹 Example: In a SaaS project, initial requirements were too broad (“Make the UI more user-friendly”). By conducting usability tests, we pinpointed slow navigation as the real issue and focused on optimizing that.

Scope Creep & Changing Priorities

Challenge: New feature requests kept coming in, delaying the project timeline.

Solution:

• Used a MoSCoW prioritization framework (Must-have, Should-have, Could-have, Won’t-have).
• Set clear success criteria upfront to prevent unnecessary additions.
• Implemented time-boxing to ensure features didn’t endlessly evolve.

🔹 Example: In an e-commerce redesign, stakeholders wanted AI-powered recommendations midway through development. Instead of derailing progress, we shipped a basic filtering system first, then iterated with AI enhancements later.

Technical Debt & Legacy Systems

Challenge: Balancing new feature development with maintaining old, outdated systems.

Solution:

• Introduced code refactoring as part of regular sprints.
• Used feature flags to test new implementations without breaking existing systems.
• Created migration roadmaps instead of big rewrites.

🔹 Example: A team needed to modernize a monolithic system to microservices. Instead of a full rebuild, they incrementally moved APIs to a new architecture while keeping the legacy system running.

Cross-Team Communication Gaps

Challenge: Engineers, designers, and product managers were misaligned on priorities.

Solution:

• Used regular stand-ups and shared documentation to maintain transparency.
• Created "Tech Briefs" summarizing technical trade-offs for non-tech stakeholders.
• Facilitated cross-functional workshops to align teams early in the process.

🔹 Example: A frontend team assumed a feature could be built with static JSON data, while the backend team planned a real-time API. This misalignment was caught in a pre-sprint planning session, preventing wasted effort.

Performance Bottlenecks & Scalability Issues

Challenge: A system worked well in testing but struggled under real-world load.

Solution:

• Conducted load testing before launch using tools like JMeter or k6.
• Used lazy loading, caching, and CDNs to optimize performance.
• Applied progressive enhancement to ensure a graceful fallback for lower-powered devices.

🔹 Example: A web app slowed down with high user traffic. We optimized database queries, added Redis caching, and used CDN delivery, cutting response times by 40%.

Building and fostering a positive engineering culture is critical for team morale, productivity, and long-term success. Here are key principles and actionable strategies to create a thriving engineering environment:

Why? Engineers thrive when they feel a sense of ownership over their work.

How to Implement:

✅ Encourage Decision-Making – Give engineers the freedom to design, propose, and implement solutions instead of micromanaging.

✅ Use “You Build It, You Run It” – Engineers should own their code in production, encouraging accountability & quality.

✅ Create Clear Ownership Areas – Define who owns what in the codebase and architecture.

🔹 Example: At Amazon, teams operate with a “two-pizza rule” (small, autonomous teams) that own services end-to-end, from development to maintenance.

Why? A team that feels safe to speak up, experiment, and fail is more innovative.

How to Implement:

✅ Normalize Blameless Post-Mortems – Focus on what went wrong, not who to blame after incidents.

✅ Encourage Open Dialogue – Make it safe for engineers to question decisions or propose new ideas.

✅ Lead by Example – Managers and tech leads should admit mistakes.

🔹 Example: Google’s Project Aristotle found that psychological safety was the #1 factor for high-performing teams.

Why? Engineers stay motivated when they are learning new skills and improving.

How to Implement:

✅ Budget for Learning – Offer stipends for courses, conferences, or books.

✅ Encourage Mentorship & Pair Programming – Create mentorship programs or peer coaching sessions.

✅ Host Internal Tech Talks & Hackathons – Let engineers share knowledge & explore new ideas.

🔹 Example: Spotify’s “Guilds & Chapters” model allows engineers to join cross-team learning groups focused on specific technologies.

Why? Removing friction in development workflows leads to happier and more productive engineers.

How to Implement:

✅ Reduce Build & Deploy Time – Aim for fast CI/CD pipelines and quick feedback loops.

✅ Automate Repetitive Tasks – Minimize manual deployments, testing, and infrastructure setup.

✅ Invest in Documentation – Keep APIs, services, and onboarding guides up to date.

🔹 Example: Netflix invests in developer tooling (e.g., Spinnaker for deployments) to make shipping code fast & stress-free.

Why? Public recognition keeps engineers motivated and reinforces good behavior.

How to Implement:

✅ Shout-Outs in Team Meetings – Acknowledge great work in stand-ups or retros.

✅ Developer Spotlights – Feature engineers in company newsletters or tech blogs.

✅ Reward Non-Code Contributions – Recognize efforts like mentorship, documentation, and process improvements.

🔹 Example: Google’s “Peer Bonus” system allows employees to nominate colleagues for small monetary rewards.

Why? Engineering teams often struggle with trade-offs between shipping fast and building maintainable systems.

How to Implement:

✅ Set Clear “Definition of Done” – Code isn’t “done” until it’s tested, documented, and reviewed.

✅ Use Feature Flags for Iterative Releases – Ship in small, safe increments instead of big, risky launches.

✅ Encourage Refactoring – Allocate time in sprints for tech debt reduction.

🔹 Example: Atlassian dedicates 20% of engineering time to “innovation & tech debt reduction” sprints.

Why? Engineers are more engaged when they trust leadership and feel valued.

How to Implement:

✅ Be Transparent About Company Decisions – Share roadmap changes and business challenges openly.

✅ Actively Listen to Engineers – Regularly check in through 1:1s, surveys, and feedback sessions.

✅ Make Decisions with Input from Engineers – Include them in roadmap planning and technical trade-off discussions.

🔹 Example: At Stripe, leaders hold weekly Q&A sessions where any engineer can ask questions about company direction.

✅ Ownership & Autonomy – Engineers should feel in control of their work.

✅ Psychological Safety – Foster a blameless, open environment.

✅ Continuous Learning – Support mentorship, tech talks, and upskilling.

✅ Developer Experience – Optimize tooling, CI/CD, and documentation.

✅ Recognition & Collaboration – Celebrate achievements and break silos.

✅ Speed vs. Quality Balance – Ship iteratively with feature flags.

✅ Empathy & Transparency – Keep communication open and honest.

A well-designed Industrial IoT (IIoT) telemetry data pipeline must handle high-frequency sensor data, ensure low latency, and support scalability for real-time and historical analysis. Below is a high-level architecture:

Purpose: Captures raw telemetry data from industrial devices and sends it to the cloud or on-prem systems.

🔹 Components:

• Industrial Sensors & Devices – PLCs, SCADA systems, and smart meters.
• Edge Gateway – Aggregates data, applies basic preprocessing (filtering, compression).
• Edge Compute (Optional) – Runs lightweight ML models for anomaly detection before sending data.

Connectivity:

• Wired: OPC-UA, Modbus, Ethernet/IP
• Wireless: LoRaWAN, MQTT, 5G, Zigbee

🔹 Example:

A factory has temperature, vibration, and pressure sensors sending data to an edge gateway, which preprocesses it before sending it to the cloud.

Purpose: Ensures reliable, scalable, and real-time data ingestion.

🔹 Components:

• MQTT Broker / Kafka / AMQP – Handles real-time data streaming from edge devices.
• Message Queue / Buffering – Prevents data loss (Apache Kafka, RabbitMQ, AWS IoT Core).
• Edge-to-Cloud Sync – Secure, low-latency transport via TLS-encrypted APIs, AWS IoT Greengrass, or Azure IoT Hub.

🔹 Example:

A factory gateway pushes sensor data to an MQTT broker. Kafka then queues messages for real-time processing & storage.

Purpose: Processes streaming data for real-time monitoring, anomaly detection, and alerts.

🔹 Components:

• Stream Processing Engine – Apache Flink, Spark Streaming, or AWS Kinesis.
• Anomaly Detection Engine – Uses ML models for predictive maintenance.
• Event Rules & Alerts – Triggers notifications in case of threshold breaches.
• Data Transformation – Cleans and normalizes data before storage.

🔹 Example:

A vibration sensor detects a sudden spike in readings. A real-time anomaly detection model triggers an alert to the factory dashboard.

Purpose: Efficiently store both real-time and historical data for analysis.

🔹 Components:

• Time-Series Database (InfluxDB, TimescaleDB) – Stores high-frequency sensor data.
• Data Lake (Cold Storage) – S3, Azure Data Lake for long-term storage.
• Relational Databases (PostgreSQL, Snowflake) – For structured data querying.

🔹 Example:

Sensor readings are stored in InfluxDB for real-time dashboards. Older data is moved to AWS S3 for historical trend analysis.

Purpose: Extracts insights from collected data to optimize operations.

🔹 Components:

• BI Dashboards (Grafana, Power BI, Tableau) – Visualize real-time & historical data.
• Predictive Analytics – Machine learning models for fault prediction, energy optimization.
• Digital Twin Models – Simulates industrial processes for scenario analysis.

🔹 Example:

AI predicts motor failure 3 days before it happens, triggering preventive maintenance.

Purpose: Provides interfaces for monitoring, control, and analytics.

🔹 Components:

• Web & Mobile Dashboards – Industrial operators monitor real-time metrics.
• APIs for Integration – REST/GraphQL APIs allow external apps to query telemetry data.
• Role-Based Access Control (RBAC) – Ensures secure access to IIoT data.

🔹 Example:

A factory manager gets real-time energy consumption alerts on a mobile app.

1️⃣ Sensors → Edge Gateway (MQTT, OPC-UA, Modbus)

2️⃣ Gateway → Cloud (MQTT/Kafka, API Gateway)

3️⃣ Stream Processing (Flink, Kinesis, Spark Streaming)

4️⃣ Storage (Time-Series DB, Data Lake)

5️⃣ Analytics & AI (Dashboards, Predictive Models)

6️⃣ End-User Apps (Web, API, Mobile)

✅ Latency Optimization – Use Edge AI for real-time processing before cloud transmission.

✅ Scalability – Use serverless ingestion (AWS Lambda, Azure Functions) for event-driven workflows.

✅ Reliability – Design for fault tolerance & failover with redundant brokers and queues.

✅ Security – Use TLS encryption, device authentication, role-based access controls.

✅ Interoperability – Support multiple protocols (MQTT, OPC-UA, HTTP APIs).

✅ Data Retention Policy – Move hot data to cold storage after a defined period.

Edge devices in Industrial IoT (IIoT) are often deployed in unsecured environments, making them vulnerable to cyber threats like data breaches, unauthorized access, malware, and physical tampering. Securing these devices requires a multi-layered security approach that spans device authentication, secure communication, runtime protection, and continuous monitoring.

🔴 Unsecured Physical Access – Edge devices are often deployed in remote locations (factories, pipelines) and can be tampered with.

🔴 Weak Authentication – Default credentials or weak passwords can expose devices to attacks.

🔴 Unencrypted Communication – Data sent from edge to cloud can be intercepted.

🔴 Software Vulnerabilities – Unpatched firmware and insecure code increase the attack surface.

🔴 Malware & Botnets – Attackers can compromise edge devices to launch DDoS attacks (e.g., Mirai Botnet).

To protect IIoT edge devices, we need a layered security architecture covering:

1️⃣ Device Identity & Authentication (Zero Trust)

Use Unique Device Identities – Every edge device must have a unique cryptographic identity to prevent impersonation.

• Hardware-Based Security – Use TPM (Trusted Platform Module) or HSM (Hardware Security Module) to securely store encryption keys.
• Mutual Authentication – Devices should authenticate both to the network and to the cloud using certificates (X.509), OAuth2, or JWT tokens.
• No Default Credentials – Require password rotation, multi-factor authentication (MFA), or secure bootstrapping methods.

🔹 Example: AWS IoT Core enforces X.509 certificates for mutual authentication between edge devices and cloud services.

• TLS/SSL Encryption – All data in transit should be encrypted using TLS 1.3 with strong cipher suites.
• End-to-End Encryption (E2EE) – Encrypt sensor data before transmission so it remains secure even if intercepted.
• MQTT Security Enhancements – Use MQTT over TLS (port 8883) and require authenticated access for message brokers.
• Edge-to-Cloud VPNs – Secure device communication using IPsec or WireGuard VPNs.
• Integrity Checks (HMAC, AES-GCM) – Ensure message integrity to detect tampering.

🔹 Example: A temperature sensor in an oil refinery encrypts its readings using AES-256 before sending it over an MQTT-TLS channel to a secure cloud API.

• Secure Boot – Ensure devices boot only trusted, signed firmware using cryptographic verification.
• Code Signing for Updates – All firmware updates must be digitally signed to prevent tampered updates.
• Firmware Over-the-Air (FOTA) Security –
  • ✅ Encrypt firmware packages
  • ✅ Validate updates with cryptographic signatures
  • ✅ Implement rollback mechanisms in case of failure

• Firmware Integrity Checks – Use SHA-256 hashing & attestation to detect unauthorized modifications.

🔹 Example: A smart PLC (Programmable Logic Controller) in a factory uses Secure Boot and only accepts firmware updates signed by a trusted manufacturer key.

• Zero Trust Networking – Apply least-privilege access (e.g., devices should talk only to necessary endpoints).
• Sandboxing & Process Isolation – Use containerized environments (Docker, Firecracker VMs) to prevent malware from affecting the entire system.
• Real-Time Anomaly Detection – Deploy AI-based Intrusion Detection Systems (IDS) that analyze behavior and flag suspicious activities.
• Secure Data Storage – Use AES-256 encryption for locally stored data and prevent unauthorized USB access.

🔹 Example: A factory edge gateway runs runtime behavioral analysis to detect anomalies in data transmission rates, preventing potential exfiltration attacks.

• Tamper-Resistant Hardware – Use epoxy coating, secure enclosures, and sensors to detect tampering.
• Geofencing & Remote Locking – Disable edge devices if they are moved out of authorized locations.
• Self-Destruct Mechanisms (Data Wipe) – In case of unauthorized access, devices can erase sensitive data.
• Hardware Watchdogs – Reset devices if they detect malicious firmware injection or system failure.

🔹 Example: A remote IoT gateway in a wind farm detects unauthorized physical tampering and sends an alert while wiping stored encryption keys.

• Centralized Log Aggregation – Send edge device logs to a SIEM (Security Information & Event Management) system (e.g., Splunk, AWS Security Hub).
• Automated Threat Detection – Use machine learning to detect anomalies in device behavior.
• Regular Security Audits – Continuously test for vulnerabilities with penetration testing & red team exercises.
• Automatic Patch Deployment – Regularly update device firmware & security policies via over-the-air (OTA) updates.

🔹 Example: A smart factory integrates all edge logs into a Splunk SIEM, which uses AI to detect suspicious access attempts and malware activity.

• https://readmedium.com/en/https:/blog.devgenius.io/why-distributed-locks-are-important-17dfef01a6db
• https://redis.io/docs/latest/develop/use/patterns/distributed-locks/ -https://github.com/coinbase/master_lock

Establishing an independent API Gateway for web and mobile users can be a good idea, depending on your use case and architecture needs. Here’s a detailed breakdown of the pros and cons, along with alternative approaches.

In Amazon’s microservices event-driven architecture, order processing follows a structured approach before passing it to downstream services like Payment, Inventory, and Shipping. The system ensures validation, enrichment, deduplication, and consistency before triggering the next steps.

When a user places an order, it goes through pre-processing stages before being handled by downstream services.

• A user submits an order request through Amazon's web or mobile app.
• The request hits the API Gateway, which routes it to the Order Service.
• Validation Layer:
  • Ensures user authentication (JWT, OAuth, etc.).
  • Checks request payload (valid items, address, payment details).
  • Rate-limits abusive users.

✅ Ensures only valid orders proceed further.

Before sending the order downstream, it is pre-processed:

🔹 Order Enrichment:

• Fetch latest product details (e.g., price, discounts).
• Validate inventory availability in real-time.
• Assign order priority (Prime vs. Standard Delivery).

🔹 Fraud Detection System:

• Uses ML-based fraud detection (analyzes user behavior, location, payment history).
• If flagged as fraud → Order is halted or sent for manual review.

✅ Ensures only valid & enriched orders are processed downstream.

Amazon ensures no duplicate orders are processed by mistake.

• Uses Idempotency Tokens (each order has a unique request ID).
• If a duplicate request is detected → Reject it without reprocessing.
• Deduplication at the Kafka/Event Broker level (prevents duplicate event handling).

✅ Prevents accidental duplicate orders & unnecessary processing.

Once an order is validated & enriched, it is persisted in the database.

• Stored in Order DB (PostgreSQL, DynamoDB, or Aurora).
• Uses Outbox Pattern:
  • The OrderCreated event is stored in a DB transaction.
  • A separate event publisher picks up the event and sends it to Kafka/SQS.
  • Ensures event reliability & atomicity.

✅ Guarantees the event is never lost & prevents inconsistent states.

Once order storage is successful, an OrderCreated event is published to an Event Bus (e.g., Kafka, AWS SNS/SQS, RabbitMQ).

👉 Downstream services consume the event asynchronously:

• Payment Service → Starts payment processing.
• Inventory Service → Reserves stock.
• Shipping Service → Prepares for shipment.

✅ Ensures decoupling & scalable order processing.

🔍 Explanation

• 1️⃣ User places an order, which goes through the API Gateway to the Order Service.
• 2️⃣ Validation Layer checks for missing details, authentication, etc.
• 3️⃣ If valid, the system enriches the order (fetching stock, pricing, priority).
• 4️⃣ Fraud detection runs, flagging suspicious transactions.
• 5️⃣ If approved, the order is persisted in the database.
• 6️⃣ The system publishes an OrderCreated event via Kafka/SNS/SQS.
• 7️⃣ Downstream services (Payment, Inventory, Shipping) consume the event asynchronously.
• 8️⃣ Once all services confirm, the order is ready for fulfillment.

🚀 Why Use This Approach?

• ✅ Ensures data consistency before passing to downstream services.
• ✅ Prevents duplicate or fraudulent orders.
• ✅ Decouples services for scalability & reliability.

Before sending an order downstream, Amazon ensures: ✅ Order is Valid & Complete (No missing or incorrect data).
✅ No Duplicate Orders (Idempotency & deduplication mechanisms).
✅ Order is Persisted (Stored safely before triggering payment & shipping).
✅ Event-Driven Processing (Asynchronous, scalable handling of orders).

graph TD;
  A[User Places Order] -->|API Gateway| B[Order Service]
  
  B -->|Validate Order| C{Validation Layer}
  C -->|Valid Order| D[Order Enrichment]
  C -->|Invalid Order| E[Reject Order]

  D -->|Fetch Product & Stock| F[Inventory Check]
  D -->|Apply Discounts| G[Price & Discount Calculation]
  D -->|Assign Priority| H[Order Priority Handling]
  
  F & G & H --> I[Fraud Detection System]
  I -->|Fraudulent Order| J[Manual Review]
  I -->|Valid Order| K[Persist in Order DB]

  K -->|Outbox Pattern| L[Emit OrderCreated Event]
  L -->|Kafka/SNS/SQS| M[Event Bus]

  M -->|Notify Payment Service| N[Payment Processing]
  M -->|Notify Inventory Service| O[Stock Reservation]
  M -->|Notify Shipping Service| P[Prepare Shipment]

  E -.->|Send Failure Response| A
  J -.->|Approve or Reject| K
  N & O & P -->|Order Processed Successfully| Q[Order Ready for Fulfillment]

Here’s an enhanced architecture diagram for Amazon's Order Processing System in a Microservices Event-Driven Architecture with data consistency, retries, and rollback mechanisms.

graph TD;

  %% User Request & API Gateway
  A[User Places Order] -->|REST / GraphQL| B[API Gateway]
  B -->|Routes Request| C[Order Service]
  
  %% Order Processing
  C -->|Validate Order| D{Validation Layer}
  D -->|Invalid Order| E[Reject & Notify User]
  D -->|Valid Order| F[Order Enrichment]

  %% Fraud & Inventory Check
  F -->|Check Inventory| G[Inventory Service]
  F -->|Price & Discounts| H[Pricing Service]
  F -->|Fraud Detection| I[Fraud Detection System]

  I -->|Fraudulent| J[Manual Review]
  J -->|Approved| K[Store Order in Database]
  J -->|Rejected| E

  %% Storing Order and Event Emission
  K -->|Outbox Pattern| L[Event Store DynamoDB, PostgreSQL]
  L -->|Emit OrderCreated Event| M[Event Bus Kafka, SNS, SQS]

  %% Downstream Services
  M -->|Trigger Payment| N[Payment Service]
  M -->|Reserve Stock| O[Inventory Service]
  M -->|Schedule Shipment| P[Shipping Service]

  %% Success & Failure Handling
  N -->|Success| Q[Payment Confirmed]
  N -->|Failure| R[Trigger Order Rollback]
  
  O -->|Success| S[Stock Reserved]
  O -->|Failure| R

  P -->|Success| T[Shipping Confirmed]
  P -->|Failure| R

  R -.->|Emit OrderFailed Event| U[Cancel Order & Notify User]
  R -.->|Release Stock| V[Undo Inventory Reservation]
  R -.->|Refund Payment| W[Trigger Refund]

  Q & S & T -->|Order Fully Processed| X[Order Ready for Fulfillment]

  %% Observability
  X -.->|Logs & Alerts| Y[Monitoring & Observability Datadog, Prometheus, AWS CloudWatch]

• Uses AWS API Gateway / GraphQL Gateway for request routing.
• Implements JWT authentication and DDOS protection.
• Enables rate limiting to prevent abuse.

• Uses Validation Rules (missing fields, invalid products).
• Runs AI/ML Fraud Detection (past user behavior, location, payment risk).
• Manual Review Queue for suspicious orders.

• Ensures reliability by storing events before publishing.
• Prevents lost messages by using DynamoDB, PostgreSQL, or Aurora.
• Uses Kafka/SNS/SQS for event-driven messaging to scale horizontally.

• Each microservice listens for events asynchronously.
• Services are idempotent, ensuring retries don't duplicate orders.
• Rollback Mechanism handles failures at any step.

• If payment fails, the system:
  ✅ Cancels order
  ✅ Releases reserved inventory
  ✅ Initiates refund
  ✅ Notifies user

• Logs errors, latency, and failures using Datadog, Prometheus, or AWS CloudWatch.
• Enables alerts & automated scaling for high traffic periods.

✅ Decouples services for scalability (Each microservice can scale independently).
✅ Ensures data consistency (Outbox pattern, Event-driven model).
✅ Fault-tolerant (Retry policies, rollback strategies, monitoring).
✅ Handles high traffic efficiently (Amazon scales to millions of orders per day).

Would you like code snippets for event-driven order processing? 🚀

In AWS, requests are routed to nearby Availability Zones (AZs) using multiple routing mechanisms to ensure high availability, low latency, and fault tolerance. Here’s how AWS routes requests to the nearest AZ:

• Geolocation Routing: Directs users to the nearest AWS region or AZ based on their geographic location.
• Latency-Based Routing: Routes requests to the AWS region that provides the lowest network latency to the user.
• Weighted Routing: Can distribute traffic across multiple AZs within a region.

• Application Load Balancer (ALB) and Network Load Balancer (NLB) distribute traffic across healthy targets in multiple AZs within a region.
• By default, cross-zone load balancing distributes requests evenly across all available instances.
• If an AZ fails, the ELB automatically reroutes requests to healthy instances in other AZs.

• Routes traffic through the AWS Global Network instead of the public internet.
• Uses Anycast IPs to direct traffic to the closest AWS edge location, which then forwards it to the optimal region and AZ.

• Can route API requests to multiple backend services deployed across different AZs.
• Works with Route 53, ALB, and AWS Lambda for fault-tolerant architectures.

• Auto Scaling Groups (ASG) ensure enough instances are available across multiple AZs.
• Multi-AZ RDS & DynamoDB automatically failover to a healthy AZ if an outage occurs.

• AWS spreads instances across multiple AZs to balance the traffic within a VPC.
• Using private and public subnets, traffic can be routed efficiently between AZs using NAT Gateways or VPC Peering.

• Route 53 ensures users reach the nearest region or AZ based on latency.
• ELB distributes traffic evenly across instances in multiple AZs.
• Global Accelerator provides low-latency routing using AWS’s private network.
• Multi-AZ deployments ensure high availability with failover mechanisms.

Here's a Mermaid diagram to illustrate how AWS routes requests to nearby Availability Zones (AZs).

graph TD;
    A[User Request] -->|DNS Resolution| B[Route 53];
    B -->|Latency-Based or Geolocation Routing| C[AWS Global Accelerator];
    C -->|Optimal AWS Region| D[Elastic Load Balancer ALB/NLB];

    subgraph AWS Region
        D -->|Distributes Traffic| E[Availability Zone 1];
        D -->|Distributes Traffic| F[Availability Zone 2];
        
        subgraph Availability Zone 1
            E1[EC2 Instances] -->|Processes Request| E2[RDS Multi-AZ DB];
            E1 -->|Scales| E3[Auto Scaling Group];
        end

        subgraph Availability Zone 2
            F1[EC2 Instances] -->|Processes Request| F2[RDS Multi-AZ DB];
            F1 -->|Scales| F3[Auto Scaling Group];
        end
    end

    E2 -.->|Failover| F2;
    F2 -.->|Failover| E2;

1. User Request → Route 53

   • Route 53 resolves the request using latency-based or geolocation routing.

2. Route 53 → AWS Global Accelerator

   • AWS Global Accelerator routes requests through AWS’s private global network to the nearest region.

3. AWS Global Accelerator → Elastic Load Balancer (ALB/NLB)

   • The Load Balancer distributes traffic across multiple Availability Zones (AZs).

4. Elastic Load Balancer → Availability Zones

   • Requests are sent to the EC2 instances in different AZs, ensuring high availability.

5. Multi-AZ Database Failover

   • If an AZ fails, RDS automatically fails over to a healthy AZ.

• Implement Latency-Based Routing (LBR) or Geolocation Routing to direct users to the closest AWS region.
• Configure health checks to detect failures and route traffic accordingly.

🔹 AWS Service: Route 53 (Global Traffic Routing)

• Deploy EC2 instances, ECS services, Lambda functions, and other compute resources in multiple AWS regions.
• Use AWS Auto Scaling to maintain performance across regions.

🔹 AWS Service: EC2, ECS, Lambda, Auto Scaling

• Use AWS Global Accelerator to direct users to the nearest available region.
• Deploy Elastic Load Balancer (ALB/NLB) in each region for regional traffic distribution.

🔹 AWS Service: AWS Global Accelerator, ALB/NLB

• Amazon Aurora Global Database (Recommended) → Provides fast cross-region replication (~1 second latency).
• DynamoDB Global Tables → Multi-region replication for NoSQL databases.
• Amazon S3 Cross-Region Replication (CRR) → Syncs objects between regions.
• Amazon RDS Multi-Region Read Replicas → Improves read performance.

🔹 AWS Service: Aurora Global, DynamoDB Global Tables, RDS Read Replicas, S3 CRR

• Amazon CloudFront (CDN) → Caches content at edge locations worldwide.
• Amazon ElastiCache Global Datastore → Synchronizes cache across regions.

🔹 AWS Service: CloudFront, ElastiCache

• Amazon SNS & SQS (Cross-Region Messaging) → Ensures asynchronous processing.
• Amazon EventBridge → Routes events across regions.

🔹 AWS Service: SNS, SQS, EventBridge

• Amazon CloudWatch (Multi-Region Monitoring)
• AWS X-Ray (Distributed Tracing)
• AWS Shield & WAF (Security & DDoS Protection)

🔹 AWS Service: CloudWatch, X-Ray, AWS Shield

graph TD;
    A[User Request] -->|Route 53 Latency-Based Routing| B1[AWS Region 1];
    A[User Request] -->|Route 53 Latency-Based Routing| B2[AWS Region 2];

    subgraph AWS Region 1
        B1 -->|Global Accelerator| C1[Application Load Balancer ALB];
        C1 -->|Traffic Distribution| D1[EC2/ECS/Lambda Services];
        D1 -->|Read/Write| E1[Aurora Global Database];
        D1 -->|Read/Write| F1[DynamoDB Global Table];
        E1 -->|Sync| E2[Aurora Global Database Region 2];
        F1 -->|Sync| F2[DynamoDB Global Table Region 2];
    end

    subgraph AWS Region 2
        B2 -->|Global Accelerator| C2[Application Load Balancer ALB];
        C2 -->|Traffic Distribution| D2[EC2/ECS/Lambda Services];
        D2 -->|Read/Write| E2;
        D2 -->|Read/Write| F2;
    end

    D1 -->|Cache Sync| G1[ElastiCache];
    D2 -->|Cache Sync| G2[ElastiCache];

    H1[S3] -->|Cross-Region Replication| H2[S3 Region 2];

    D1 -->|SNS/SQS| I1[SNS Event Processing];
    D2 -->|SNS/SQS| I2[SNS Event Processing];

✔ Improved Performance: Users are routed to the nearest region.
✔ Fault Tolerance: Traffic automatically reroutes during failures.
✔ Disaster Recovery: Zero downtime if a region goes down.
✔ Scalability: Auto-scaling handles traffic spikes.

• Multi-region active-active architectures add significant complexity to your stack – think carefully about your services and whether they need to be multi-region at all.

• Consider whether less complex multi-region architectures, such as multi-region backup, pilot light, or warm standby can meet your needs.

• Design to avoid race conditions, by using read local/write global or partitioned write.

• Use storage tools to keep data synchronized, including S3 cross region replication and EBS snapshot cross-region copies.

• Keeping data consistent across regions is challenging. Use DynamoDB Global Tables or Read Replicas with RDS and Aurora.

• VPC peering allows consistent security across regions.

• Route 53 can perform failover checks, and most operations can be automated – but the service still needs to be monitored. Create relevant metrics and set alarms on them.

• Plan to manage the environment! Use AWS CloudFormation StackSets, AWS Config rules, AWS System Manager, and other DevOps tools.

• If you don’t test, it won’t work in a crisis. Test a lot

• Reference: https://www.slideshare.net/slideshow/architecture-patterns-for-multiregion-activeactive-applications-arc209r2-aws-reinvent-2018/124318684#4

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