Event-Driven Architecture: The Scalable Backbone for Training Large Language Models
Large language models (LLMs) are revolutionizing the field of artificial intelligence, demonstrating remarkable capabilities in text generation, language translation, and creative content production. However, training these behemoths necessitates ingesting and processing massive amounts of data, posing a significant logistical challenge. Event-driven architecture (EDA) emerges as a powerful solution, streamlining the LLM training process for optimal efficiency and scalability.
Unveiling Scalability's Potential:
- Microservice Mastery: EDA advocates for decomposing the LLM training pipeline into independent, task-specific microservices. Data ingestion, pre-processing, and model training each become distinct services, enabling independent scaling. Resource bottlenecks become a thing of the past. A surge in data can be met by scaling up data ingestion microservices, while lagging training can be addressed through additional training microservices.
- Elasticity on Demand: The event-driven nature of EDA fosters automatic scaling based on real-time data flow. When new datasets arrive, events automatically trigger additional processing and training, ensuring efficient resource allocation. Imagine a system that seamlessly adapts to new data streams, keeping your LLM constantly learning and evolving.
Breaking Down Data Silos:
- Real-Time Data Smorgasbord: Traditional architectures often struggle with integrating data from diverse sources. EDA overcomes this limitation by promoting real-time data ingestion through events. New data triggers processing and training, ensuring your LLM is constantly learning from the latest information. Imagine incorporating real-time social media data to train your LLM for a nuanced understanding of current trends and cultural shifts.
- Production Insights Fuel Innovation: The power of EDA extends beyond traditional data sources. Consider re-using events generated during your LLM's production phase to further refine its capabilities. User interactions, query logs, and feedback events can be streamed back into the training pipeline, providing valuable insights into real-world usage. Imagine a chatbot trained on customer support interactions, continuously learning and improving its responses based on real-time user feedback. This closed-loop system fosters continuous improvement and allows your LLM to stay relevant and effective in the ever-changing digital landscape.
- Data Democratization: EDA simplifies the integration of external data sources. Social media sentiment data, for instance, can be seamlessly incorporated to enhance your LLM's ability to grasp current public opinion. This fosters continuous improvement and adaptability, allowing your LLM to stay relevant in the ever-changing digital landscape.
By incorporating production data as events, you create a dynamic training environment where your LLM constantly learns and evolves based on real-world interactions. This not only improves the model's performance but also fosters a more robust and adaptable LLM capable of exceeding expectations.
Efficiency at its Finest:
- Bottlenecks, Begone: The decoupled nature of microservices or micro integrations in EDA eliminates single points of failure. If one service encounters an issue, others can continue functioning, minimizing disruptions to the training process. This translates to smoother training pipelines and faster model deployment.
- Orchestration Nirvana: EDA frameworks provide built-in event routing and management functionalities. These tools automate communication between microservices, freeing up your team to focus on core tasks like model development and performance optimization. Imagine a well-oiled machine where data flows seamlessly through the pipeline, triggering the right actions at the right time.
By embracing EDA and its advanced capabilities, you can unlock the true potential of your LLM training process. Imagine a system that automatically scales, adapts to real-time data, eliminates bottlenecks, and fosters a deeper understanding of the world through continuous learning. EDA is the key to unleashing the power of LLMs and propelling them to even greater heights of intelligence and capability. EDA allows us to think about a future where we can push events back from the LLM to trigger some other behaviours, an example might be based on a mixture of weather patterns and historical sales recommend the logistics teams to move products in the supply chain closer to where they are needed.
The Unsung Hero of AgTech: Clean Data for Powerful AI Decisions in Agriculture
The burgeoning field of agricultural technology (AgTech) is abuzz with the potential of artificial intelligence (AI) to revolutionize farming practices. From optimizing irrigation to predicting and mitigating crop disease outbreaks, AI promises a future of unprecedented efficiency and abundance. However, before these futuristic visions can become reality, there's a critical, yet often overlooked, step: data cleaning. Think of your agricultural data as a vast, information-rich field. While it holds immense potential, it's likely riddled with inconsistencies, missing entries, and inaccuracies. Just as you wouldn't sow seeds in an unprepared field, you can't expect reliable AI insights from unclean data.
Why is it important?
There are many reasons why you should be prioritizing data cleaning, here are some of my thoughts :
- Ensuring Data Integrity: AI models are powerful tools, but they rely on the quality of the data they ingest. Feeding them messy data leads to unreliable and potentially misleading results. Inaccuracies in sensor readings or yield figures can lead to skewed predictions, jeopardizing resource allocation and harvest forecasts. Imagine an AI system trained on inaccurate soil moisture data recommending excessive irrigation, leading to wasted water resources and damaged crops.
- Unlocking Actionable Insights: Clean data reveals valuable patterns and trends that might be invisible to traditional analysis. By identifying anomalies and outliers, you can uncover previously unknown factors impacting crop health, soil conditions, or livestock performance. Clean data might reveal a correlation between specific weather patterns over a specific time period, and a surge in a particular pest. Armed with this knowledge, farmers can take preventative measures and safeguard their crops, optimizing resource allocation and minimizing losses.
- Sharpening AI's Focus: Clean data allows you to train AI models on the most relevant information, significantly improving the accuracy of AI predictions for tasks like disease detection, irrigation scheduling, or yield estimation. Clean data on historical crop yields, weather patterns, and pest outbreaks allows AI to identify subtle connections that would be missed by traditional methods. This leads to more precise yield forecasts and targeted interventions for optimal crop health and performance.
How do you cultivate a clean data crop for your agricultural operations? Here are a few key strategies:
- Standardization is Paramount: Ensure consistent data formats across all sources, be it sensors, weather stations, or manual recordings. This eliminates confusion and streamlines data analysis. Imagine a scenario where some yield data is recorded in kilograms while others are in tons. This inconsistency would make it difficult for AI to analyze the data effectively. Standardization ensures all the information speaks the same language.
- Embrace Automation Tools: Utilize data cleaning tools to automate repetitive tasks like identifying missing values or correcting inconsistencies. These tools can significantly improve efficiency and free up your team's time for more strategic tasks, such as developing and deploying AI models. I was recently in a workshop where Microsoft were discussing benefits of using co-pilot to help clean data, this is a fabulous use of the technology.
- Human Expertise is Indispensable: Don't underestimate the power of human oversight. Train your team to identify and address data errors, in a previous role where we were using Machine Learning to clean data we actually embedded agromonists within the data science team to help aid understanding. This ensured the overall quality and reliability of the information used for AI-driven decision-making. Human expertise is crucial for identifying and correcting errors that might be missed by automated tools.
By prioritizing data cleaning, you're not just preparing data for AI – you're laying the foundation for a future of data-driven agricultural practices. Clean data is the fertile ground from which intelligent and sustainable farming practices can truly flourish. In essence, clean data is the unsung hero of the AI revolution in agriculture, empowering farmers to make data-driven decisions that optimize yields, conserve resources, and ensure the long-term health of our planet.
AI-Powered Price Optimization - The Key to Maximizing Rebate Revenue in Ag Retail
Building on the foundation of real-time data and automated rule application provided by Event-Driven Architecture (EDA) (explored in a previous blog post), this blog post delves into the transformative power of Artificial Intelligence (AI) in ag retail. We'll explore how AI can analyze sales data and suggest price adjustments, propelling your business towards becoming a rebate-generating powerhouse.
AI - Your Rebate Revenue Catalyst
Imagine a sophisticated system that analyzes historical sales data, current inventory levels, competitor pricing, and complex rebate structures – all in real-time. This is the transformative power of AI in ag retail rebate management. By leveraging advanced algorithms, AI can:
- Uncover Hidden Sales Opportunities:AI can identify products with high rebate potential but lagging sales within your vast sales data. This allows you to strategically focus efforts on these products, maximizing your rebate capture and overall revenue.
- Predict Customer Behavior with Precision:AI can analyze past customer purchases and agronomy recommendations across similar conditions to predict future buying habits with greater accuracy. This enables you to tailor pricing strategies to specific customer segments, potentially offering targeted discounts that incentivize purchases of high-rebate products.
- Optimize Pricing for Maximum Rebate Capture:This is where AI truly shines. By considering all the factors mentioned above, AI can recommend dynamic pricing strategies that not only increase sales volume but also optimize your rebate capture. For instance, AI might suggest a temporary price adjustment on a specific herbicide product to reach a sales threshold that unlocks a significant rebate.
AI in Action: A Practical Example
Let's consider a real-world scenario: Your store sells a popular brand of fungicide with a tiered rebate structure. AI analyzes historical sales data and notices a decline in purchases as customers approach the next rebate tier. Here's how AI can help:
- Identify the Rebate Opportunity:
- Analyze Market Dynamics:
- Recommend Strategic Price Adjustment:
The beauty of AI is its ability to continuously learn and adapt. As you gather more data and implement AI recommendations, the system becomes progressively more adept at suggesting optimal pricing strategies that maximize both sales and rebate capture.
The Path to Rebate Domination: Implementing AI for Ag Retailers
The good news is that AI-powered pricing solutions are becoming increasingly accessible for ag retail businesses of all sizes. Here's how to get started on your journey to rebate dominance:
- Needs Assessment:Begin by meticulously identifying your specific challenges and goals with rebate management, this could be keeping track of simultaneous programs on offer.
- Explore AI Solutions:Research AI-powered pricing platforms specifically designed for the ag retail sector. Focus on solutions that integrate seamlessly with your existing retail systems for optimal data flow.
- Embrace a Data-Driven Culture:AI thrives on data. Ensure your sales and inventory data is clean, accurate, and up-to-date to maximize the effectiveness of your AI solution.
By embracing EDA and AI, you can transform your rebate management from a time-consuming end of season task to a strategic advantage. Imagine a future where your pricing decisions are not just informed by intuition, but driven by data-driven insights that unlock the full potential of your rebate programs. With AI as your guide, you'll be well on your way to becoming a rebate-generating powerhouse in the competitive world of ag retail.
Reaping the Rewards: How Event-Driven Architecture Streamlines Rebate Management in Ag Retail
In the competitive world of agricultural retail, maximizing profits hinges on efficiency. One area ripe for optimization is rebate management. Traditional systems often struggle to keep pace with the dynamic nature of sales and complex rebate structures. This is where Event-Driven Architecture (EDA) steps in, offering a real-time, data-driven approach to unlocking the full potential of your rebates.
What is Event-Driven Architecture (EDA)?
Imagine a symphony conductor who doesn't rely on a rigid schedule, but rather cues the musicians based on the notes being played. That's the essence of EDA. It's a software design paradigm that reacts to events as they happen, rather than relying on scheduled processes. Instead of a central database, EDA utilizes event streams – continuous flows of data generated by specific actions. In the context of ag retail, events could include:
- A recommendation being made by an agronomist.
- A sale being registered at the Point-of-Sale (POS) system.
- A specific product reaching a certain inventory level.
- A new rebate agreement being signed with an input manufacturer.
How EDA Optimizes Rebate Management
Traditional rebate management systems are often clunky and inefficient. They rely on batch processing, largely at the end of the season. This means that valuable insights are delayed and opportunities can be missed. EDA offers a paradigm shift with several key advantages:
- Real-Time Visibility: With every sale triggering an event, you gain immediate insight into your rebate progress. No more waiting for end-of-period reports to understand where you stand on rebate fulfilment. Imagine the power of knowing exactly how close you are to that lucrative crop protection rebate by the end of the week, allowing you to strategically adjust sales efforts if needed to achieve the next tier of a program.
- Automated Rule Application: Manual calculations and data entry are error-prone and time-consuming. EDA allows you to define rebate rules that automatically trigger based on specific events. For example, a rule could instantly calculate a rebate based on the quantity of a particular high-value herbicide sold. This not only saves time and reduces errors, but also ensures your team is freed up to focus on higher-value activities.
- Improved Accuracy: Manual calculations and data entry become a thing of the past. EDA eliminates human error from the equation, ensuring your rebate claims are accurate and submitted on time. This can translates into earlier payments meaning improved in-season cash flow for your business.
- Enhanced Agility: The traditional approach to rebate management often requires significant IT intervention to adapt to changing rebate structures and promotions. EDA's flexibility allows you to adapt with ease. New rules can be implemented quickly, ensuring you maximize every opportunity. Imagine a manufacturer offering a flash sale on a specific type of crop protection with an attractive rebate attached. With EDA, you can swiftly implement the new rebate rule and capitalize on the opportunity without missing a beat!
Unlocking the Full Potential: The Power of AI
In the next blog post of this series, we'll delve into how leveraging this real-time data with Artificial Intelligence can take your rebate management to the next level. Imagine AI analyzing sales trends and suggesting price adjustments to not only maximize sales but also optimize your rebate capture.
Stay tuned for part two, where we explore how AI can transform your ag retail business into a rebate-generating powerhouse!
Unleash the Power of Events: Why Event-Based Messaging is Transforming Businesses
In today's fast-paced world, businesses need to be agile and responsive. Traditional, request-based systems just don't cut it anymore. That's where event-based messaging enters the scene. It's not just a fancy buzzword; it's a game-changer, offering a dynamic and efficient way for businesses to react to the constant churn of events that drive their operations.
But what exactly is event-based messaging, and why should you care? Let's break it down. Imagine your business as a bustling city, with information zipping around like cars and trucks. In a request-based system, it's like every street requires a specific traffic light, slowing things down. Event-based messaging is like a series of interconnected highways, where events act as triggers, sending information to relevant destinations only when something happens. This creates a reactive, real-time flow of data, delivering the right information to the right place at the right time.
So, what are the juicy benefits of adopting this paradigm shift?
Boost your Agility and Scalability
Forget about systems bogged down by constant requests. Event-based messaging decouples systems, making them loosely coupled and independent. This translates to lightning-fast responses, effortless scaling, and the ability to adapt to changing needs like a chameleon. No more traffic jams, just smooth information flow.
Real-Time Insights and Actions
Events are like whispers carrying the news of what's happening. With event-based messaging, you can react to them instantly, triggering workflows, updating information, and even informing customers in real-time. Imagine a delivery app instantly notifying you about a shipment change, or a stock market system reacting to price fluctuations with lightning speed.
Enhanced Customer Experience
Tired of customers waiting on hold or receiving irrelevant information? Event-based messaging allows you to deliver personalized, contextually relevant messages based on specific customer actions. Imagine a travel app informing you about gate changes or airport delays, or a retail store sending targeted discounts based on your browsing history. It's all about making your customers feel like the VIPs they truly are.
Improved Operational Efficiency
Forget about manually chasing down updates and chasing data through siloed systems. Event-based messaging automates communication, eliminating inefficiencies and streamlining processes. This means less time spent on redundant tasks and more time focusing on what truly matters.
Unlocking the Power of Data
Events are like breadcrumbs of information, and event-based messaging gathers them all. This rich data stream can be analyzed to uncover hidden trends, predict future events, and make data-driven decisions. Imagine optimizing warehouse logistics based on shipping updates or personalizing marketing campaigns based on customer interactions.
Ready to join the event-based revolution?
Start by analyzing your business processes and identifying areas where real-time information flow can add value. Remember, this shift is not just about technology; it's about embracing a new way of thinking, where agility, responsiveness, and data-driven decisions become the cornerstones of your success.
So, don't get stuck in the request-based traffic jam. Unleash the power of events and let your business zoom ahead! Get in touch and let us help you with your requirements.
Streamlining Rebate Processes for Enhanced Partner Satisfaction
Effective rebate management hinges on efficient and streamlined processes. By simplifying the rebate lifecycle, manufacturers can reduce administrative burdens for both themselves and their channel partners, leading to improved partner satisfaction and overall collaboration.
Streamlining Data Capture and Validation
The foundation of efficient rebate management lies in accurate and timely data capture. Manufacturers should implement robust data collection systems to capture sales information, purchase records, and any other relevant data points. This data should be validated and reconciled promptly to ensure data integrity and eliminate discrepancies.
Automating Calculation and Distribution
Manual rebate calculations and distributions can be time-consuming and prone to errors. Implementing automated rebate processing systems can significantly reduce the administrative burden and ensure accurate payments are made on time. These systems should integrate with partner systems to streamline information exchange and automate payment processing.
Providing Real-time Insights and Reporting
Channel partners should have access to real-time insights into their rebate performance. Manufacturers can provide online portals or dashboards that allow partners to track their progress, view payment schedules, and receive detailed reports on their rebate activity. This transparency fosters trust and accountability among partners.
Addressing Partner Concerns and Disputes
Rebate disputes can strain relationships and hinder collaboration. Manufacturers should establish clear dispute resolution processes and appoint dedicated teams to handle inquiries promptly and professionally. This ensures that partner concerns are addressed effectively and that potential conflicts are resolved amicably.
Conclusion: A Seamless Rebate Experience for Partner Success
By streamlining rebate processes, manufacturers can enhance partner satisfaction, foster a sense of ownership, and ultimately drive stronger channel relationships. By eliminating administrative hurdles, providing real-time insights, and addressing concerns promptly, manufacturers can create a seamless rebate experience that promotes collaboration and mutual success.
Amazon AWS Launches new B2B Offering
AWS Have launched a new B2B EDI data exchange offering, I think that this will be great for businesses who struggle to migrate their legacy EDI workflows to the cloud, and also offer a more cost effective solution than the legacy EDI VANs out there. It will be interesting to see how the stack up functionality-wise, and also with industry and domain expertise.
Calculating and Leveraging Numbers in Rebate Strategies
In the intricate web of modern commerce, supply chain visibility stands as a beacon guiding successful rebate management. The orchestration of rebate strategies doesn't happen in isolation; it’s intricately woven into the fabric of supply chain dynamics. This blog post sheds light on the paramount significance of supply chain visibility in steering effective rebate management strategies.
Crafting profitable rebate strategies is more than a stroke of luck; it’s a meticulous game of numbers. In this blog post, we explore the critical role that numerical calculations play in the development and success of rebate strategies. Understanding these figures becomes the bedrock for crafting strategies that not only benefit stakeholders but also drive sustainable growth.
Comprehensive Understanding of Variables
A profitable rebate strategy starts with a deep dive into the data pool. Understanding demand trends, potential margins, and a plethora of other variables becomes crucial. These insights aren’t just numbers on a spreadsheet; they’re the driving force behind well-informed decisions, guiding rebate strategies toward profitability.
Advantages of Rebates Over Discounts
Rebates hold a unique advantage over simple discounts. Their capacity to account for a wider range of variables makes them a sophisticated tool in the arsenal of trading partnerships. However, with complexity comes the need for precision. Manual processes might falter where a nuanced approach is required, emphasizing the importance of robust analytical tools in leveraging rebate strategies effectively.
Conclusion
Numbers form the backbone of profitable rebate strategies. They provide the roadmap toward aligning objectives, understanding market dynamics, and optimizing partnerships. In our upcoming blog post, we’ll explore effective management strategies that transform these numerical insights into actionable and mutually beneficial rebate programs.
Microsoft Warns of Hackers Exploiting OAuth for Cryptocurrency Mining and Phishing
Check out this article on the Hacker News about hackers using OAuth applications to deploy virtual machines for crypto mining and phishing - see Hacker News
The Role of Supply Chain Visibility in Effective Rebate Management
In the intricate web of modern commerce, supply chain visibility stands as a beacon guiding successful rebate management. The orchestration of rebate strategies doesn't happen in isolation; it’s intricately woven into the fabric of supply chain dynamics. This blog post sheds light on the paramount significance of supply chain visibility in steering effective rebate management strategies.
Imagine a roadmap where every turn, every junction is illuminated. That's what supply chain visibility offers in the realm of rebate management. It provides a comprehensive view of the entire journey, from raw materials to end consumers. Dashboards offering end-to-end insights play a pivotal role in this landscape, enabling stakeholders to make informed decisions.
Integration of Data and Analytical Tools
Visibility is not merely about seeing; it's about understanding. Understanding demand trends, market fluctuations, and potential margins - these insights drive profitable rebate strategies. Analytical tools aid in deciphering this labyrinth of data, allowing for informed decision-making. Integration of these tools into existing processes becomes imperative for the successful deployment of rebate programs.
Supply chain visibility isn’t just a buzzword; it’s the cornerstone of effective rebate management. It empowers stakeholders with insights, allowing for agile adaptations and informed strategies. The integration of data and analytical tools within this framework ensures that rebate strategies aren’t just shots in the dark but well-calculated moves grounded in market realities. Stay tuned for our next blog post, which will delve into the realm of calculating and leveraging numbers in crafting profitable rebate strategies.
The Power of Rebates in Driving Trading Partner Cooperation
Trading partnerships thrive on collaboration and mutual benefit. The concept of a rebate, where a channel partner receives a financial incentive based on a defined program is a great way to drive collaboration. Rebates, often underestimated in their influence, stand as powerful tools fostering cooperation among trading partners. In particular, within the crop protection industry, rebates constitute a substantial 30% of goods sold, exemplifying their pivotal role in stimulating partnerships and driving growth.
Understanding Effective Deployment
Successful deployment of rebates hinges upon more than a simple discount mechanism. It revolves around creating incentive schemes that benefit both partners. These schemes require a deep understanding of the numerical backbone behind rebate strategies. It's not just about offering rebates but ensuring that they align with the overarching objectives of both parties. A rebate strategy that doesn't resonate with mutual goals might fall short of driving the desired cooperation.
Visibility and Communication
Visibility acts as the guiding light in effective rebate strategies. It's not enough to have a strategy; partners need to see through it, understand its intricacies, and share a mutual vision. Communication becomes the conduit through which alignment on objectives is achieved. Clear, consistent communication paves the way for execution that aligns with the agreed-upon objectives.
In conclusion, deploying successful rebate strategies requires a careful orchestration of several critical elements. It's about creating incentives that benefit both sides, grounded in a deep understanding of the numerical data that drives these strategies. As we delve deeper into this realm, upcoming blog posts will explore the role of supply chain visibility and the significance of numerical calculations in crafting and optimizing rebate strategies. Stay tuned for insights that unlock the potential within these crucial aspects of effective rebate management.
API vs Event Driven Integration
When it comes to integrating different systems and applications, there are two main approaches: point-to-point API integration and event driven architecture. Both have their own advantages and disadvantages, and the best approach for a particular use case will depend on the specific requirements and constraints of the project.
Point-to-Point API Integration
Point-to-point API integration, also known as direct integration, is a simple and straightforward approach where two systems are directly connected through an API. This means that one system makes a direct call to the other system's API to retrieve or update data. This approach is best suited for small-scale projects where only a few systems need to be integrated, and the data flow between them is relatively simple.
One of the major advantages of point-to-point API integration is that it is easy to set up and maintain. There is no need for additional infrastructure or middleware, and the systems can be connected quickly. Additionally, it is easy to troubleshoot and debug issues, as the data flow is direct and easy to understand.
However, point-to-point API integration can become complex and unwieldy as the number of systems and the complexity of the data flow increases. It can also lead to tight coupling between systems, which can make it difficult to change or replace one of the systems without impacting the others. This can create a support burden and a reliance on knowledge which may not be dedicated to supporting integration. Often issues appear randomly due to some problem with calls or an API change on a 3rd party system.
Event Driven Architecture
Event driven architecture, on the other hand, is a more flexible and scalable approach to integration. In this approach, systems communicate with each other through the exchange of events. An event is a message that is sent by one system to notify other systems of something that has happened, such as a change in data or a specific action.
One of the major advantages of event driven architecture is that it is highly scalable and can handle a large number of systems and a complex data flow. It also promotes loose coupling between systems, which makes it easy to change or replace one of the systems without impacting the others.
Additionally, event driven architecture allows for real-time processing of events and can handle a high volume of events.
However, setting up and maintaining an event driven architecture can be more complex than point-to-point API integration. It requires additional infrastructure, such as an event bus, although it is generally easier to understand where issues are due to the availability ot tooling.
In conclusion, point-to-point API integration and event driven architecture are two different approaches to integration that have their own advantages and disadvantages. The best approach will depend on the specific requirements and constraints of the project, and it's important to consider factors such as scalability, flexibility, and maintainability when making a decision.
What is Event Driven Architecture
When it comes to integrating different systems and applications, there are two main approaches: point-to-point API integration and event driven architecture. Both have their own advantages and disadvantages, and the best approach for a particular use case will depend on the specific requirements and constraints of the project.
Event Driven Architecture
This architecture is best used for data and analytics in situations where real-time processing and low latency are important. For example, in financial services, EDA is used to process trades and analyze market data in near real-time. EDA can also be used in IoT, where events from sensor data need to be processed and acted on quickly. Additionally, event-driven architectures are commonly used in streaming data and microservices environments where events are used to trigger different services or actions.
Event driven architecture is more flexible and scalable approach to integration. In this approach, systems communicate with each other through the exchange of events. An event is a message that is sent by one system to notify other systems of something that has happened, such as a change in data or a specific action.
One of the major advantages of event driven architecture is that it is highly scalable and can handle a large number of systems and a complex data flow. It also promotes loose coupling between systems, which makes it easy to change or replace one of the systems without impacting the others.
Additionally, event driven architecture allows for real-time processing of events and can handle a high volume of events.
In conclusion, event driven architecture offers a scalable architecture which can be deployed across cloud environments and allows events to be captured and used for analytics independently.