Hello everyone and good day. I'm Srijan Reddy Anugu, a technology manager with 20 years of experience in emerging technologies. My expertise spans across SAP, cloud computing, and integrations, and I currently work for ADM in the Cincinnati area. Thank you for joining this conference on cloud based data integration. Today, I will be sharing the best practices, discussing the challenges, and unveiling innovative strategies for modern data driven ecosystem. Let's dive into the topic. Cloud based data integration is a game changer for companies that work with different kinds of data in today's digital world. In this talk, we will cover how these systems have developed, what benefits they offer, and the best ways to use them. We will also see how cloud platforms have improved older methods by making data management more scalable, easier to access, and more cost effective. So let's talk about the evolution of the cloud data integration. So initially it was a traditional on prem data integration systems. In the early days, companies used data systems installed on their own hardware. The systems were reliable, but limited by physical resources, needed a lot of memory money upfront, and couldn't easily grow with increasing data needs. So the example for on prem are, the SAP data services, the Informatica ETL, the Dell Boomi, StrapLogic, then, hybrid cloud strategies. Companies then started using a mix of on prem and cloud solutions. Today, about 89. 3 percent of fortune 500 companies use hybrid systems, spending an average of 12. 7 million a year. This approach combines the security of local systems with the flexibility of the cloud. So the examples for that is, again, a cloud SAP data services system, Informatica ETL, a Dell Boomi, a SnapLogic ETL system, and Apache NiFi, and AWS Glue also supports the hybrid scenarios. Then the evolution of the cloud native solutions. Now the modern cloud native platforms are best in terms of speed and reliability. They can process data in just 2.3 milliseconds on average, and maintain a 99.997% reliability rate across different data types. This makes it easier for companies to handle complex data tasks quickly and accurately. And again, the example for this of the AWS glue. The AWS Data Factory, Google Cloud Data Flow, SAP Data sphere, and the application integration tool, S-A-P-B-D-P integration tool. Okay. Now let's talk about the market dynamics of this cloud platforms. The cloud integration market is quickly becoming DOM dominated by three main platforms. Making up almost 80% of the total market share AWS leads with a 34.2 market share. Thanks to its early start and broad range of services. AWS started in 2005, between 2005 to 2008, and Microsoft Azure is in second place with 27.8 Azure as. Picked up a lot in, last five years, showing an impressive growth of 42 percent year over year. And Google Cloud Platform holds 16. 4 percent of the market, excelling in the artificial intelligence and machine learning. Together, these platforms handle a massive 7. 84 trillion integration transactions every day. About the same as processing all the data in the Library of Congress every 3. 2 seconds. So what are the key, benefits of using this, cloud based integrations? So the first thing that comes is, elasticity and scalability. So in a traditional on prem system, scaling up a system, scaling out or scaling up is a big task. It's a downtime and it affects the business. With the cloud systems, it is on the fly. and, we can. Set the settings up such that if the load is more the autoscale scalability happens. So scalability is one of the primary factors. And global accessibility. So there are many data centers for AWS Azure, Google, Alibaba, and Oracle Cloud. So they are omnipresent across the globe. So because of those data centers, the data accessibility. is the core component, right? So the speed increased, the reliability increased. And next, the cost optimization. Moving to cloud integration can significantly cut costs, especially for security. Organizations can reduce their security expenses by nearly 60 percent compared to traditional on premise solutions, with average security coming in at only 3. 27 per GB of data processed. Now, let's talk about What happens by following the best architectural practices for cloud native tool integrations? So it reduces the deployment failures by 72. 3 percentage Through automated testing and standardized workflows and it enhances the resolution speed by 58. 6 with better monitoring and the automated rollback and enabling in near continuous delivery with 95 percent weekly deployments to streamline the deployment pipelines. These tools leverage containerization. orchestration and automated workflows to accelerate deployment speed and maintain high reliability, allowing teams to respond more effectively for the business needs. And the another one, the modern microservices architectures transform cloud computing by dividing monolithic appliances. into independently scalable services. This approach significantly enhances deployment flexibility, system reliability, and operational efficiency, especially in large scale cloud environments where traditional architecture faces challenges with complexity and scale. Now, how is the data quality? and governance in cloud native architecture. So automated data validation, smart validation tools have reduced data issues by 87. 3 percent and boosted data consistency by 92. 1 percent across various system parts making operations smoother and analytics more reliable. Compliance management. Modern cloud systems have streamlined compliance, cutting audit operation time by almost 74 percent and increasing audit success by 41. 2 percent. This means companies can meet strict rules while reducing extra paperwork. Metadata management. Advanced tools for managing metadata have sped up data discovery by 68. 5 percent and improving data tracking accuracy by nearly 80 percent. This gives organizations a clear view of the data flow, helping them make decisions quickly and with Confidence. Real time processing capabilities. What do we get with this real time processing capabilities? So low latency. Our system process data almost instantly, taking just 8. 7 milliseconds for complex events. It reliably delivers messages with a 99. 997 success rate. So data loss is nearly eliminated. High throughput. It can handle huge amounts of data. Processing 2. 8 million events every second, a 312 percent improvement over old systems. This boost makes real time analytics possible on a large scale systems. So that is the reason why OLAP and OLTP supported systems are built now. Edge computing with edge processing. We reduce response times by over 90 percent globally. Our distributed systems maintain data accuracy with a 99. 999 percent consistency rate, ensuring reliable performance everywhere. Now, let's talk about some common challenges and the solution for those challenges. So the common challenges are data silos. Performance optimization and security and compliance. So coming to the data silos, using schema matching and mapping techniques, improves data integration accuracy by 64. 2 percent and cuts down manual work by 57. 8%. Performance optimization. Query optimization frameworks reduce data retrieval time by 47. 3 percent on average while keeping data consistency at 99. 95%. Security and compliance. Multi layer security frameworks lower security incidents. By 92. 4 percent with only a minimal increase in data access time, just 3. 7 milliseconds by using this cloud native data integration solutions. So now let's talk about the future trends in the cloud based data integration. So right now we do have cloud based data integration. What's the future of the cloud based data integration? First thing we could talk about is AI argumented integration. AI is the buzzword now. So new AI tools can speed up integration work, cutting development time by over half, and improving error detection significantly. This lets developers spend more time on important projects instead of routine troubleshooting. And second one is, advanced meta management, metadata management, right? So metadata is the heart of any company. So for that, the modern metadata systems automatically match data formats with high accuracy, greatly reducing the need for manual setup. Tasks that used to take weeks can now be done in hours. The third, that I could talk about is intelligent data quality monitoring. So real time monitoring systems are now much better at catching and fixing errors quickly, ensuring data stays reliable and useful across all platforms. So the quicker we fix the data, the quicker solution. The business gets the data immediately and the decision is made immediately based on the data. To conclude, transformative impact. Cloud based data integration represents a fundamental shift in data management, offering substantial improvements in efficiency, cost optimization, and performance. Best practices. Success is closely tied to implementing architectural best practices, particularly adopting cloud native tools and microservices based architecture. The future outlook, the emergence of AI driven integration capabilities and advanced automation tools suggest a future of increasingly intelligent and self optimizing integration process. Thank you everyone for joining today and listening in. Appreciate your time. Thank you. Have a good day.