Hello everyone. I'm Satish Pangan. I'm working as a platform administrator in Zurich North America, USA. I have overall of 15 years of experience in data platform engineering with a specialization in enterprise data integration on automation. Today I'm going to present the topic, a driven data integration at scale, real time compliant, and cloud native. This topic, A driven data integration, transforms enterprise data workflow across hybrid cloud environments through this AEA power engine to perform orchestration and automation. That's, let's see. The evolution of cloud native data architecture and the challenges, the traditional method, which we face now. So as the legacy systems struggle with the real time operations, manual processes create several bottlenecks. And on top of it, the compliance requirements add complexity to the existing data and workflows, which challenging the process time. Kubernetes reshapes the cloud NATO architecture. Which having a, which mounting pressure to modernize their data. So this shift to containerized environments demands a new approaches to data orchestration, governance, and scalability. The traditional integration platforms cannot scale and provide the required output. Let me introduce Claire, the A engine behind intelligence data management. So Claire is provided by the Informatica service provider, which represents the next generation of data integration intelligence, which powers the intelligence data management cloud IDMC, which is a cloud native solution provided by Informatica. This sophisticated platform transforms how enterprise approaches data workflows across hybrid environments, including AWS Azure, Google Cloud, and on-premises infrastructure. The SCL a driven approach fundamentally changes the data integration, which instead of reactive maintenance to a proactive optimization and intelligent automations that add access to the business request. Now let's see how multi-cloud integration can be seamlessly integrated with the cloud. Our supports AWS Amazon Web Service of Azure on this, which supports native integrations with AWS services, including S3 relational databases, Redshift and Lambda. And it also leverages AWS Native Security and Compliance Framework while maintaining data governance acceler Azure entire cloud stack. So coming to Microsoft Azure, the IT integrates with Azure Data Factory for all the integration needs and Azure Data Lake storage for all the storage needs and synapsis for analytics. WS. It also supports Azure SQL database and it also seamlessly connects with the existing Microsoft ecosystem while extending capabilities through a powered automations. Now let's talk about the on-premises ra. So it builds the legacy between the on-premises and the cloud, modern cloud native infrastructure, and it provides real time synchronization. It also seamlessly integrated our data all across the hybrid environments. Now about the data integration on the task automation, revolutionize traditional data integration by automating the most time consuming and error pro task through machine learning algorithms. Pattern recognition. The platform identifies optimal integration path, such as the transformation logic, automatic and automatically generates mapping and workflow recommendations so that it'll be easy for the developers to see where they need to look into the logic, transformation or mappings. This intelligent automation extends beyond simple task execution. To include, prevent predictive maintenance, anomaly detection, and self-healing data pipelines that adapts to ever changing business requirements, and also without a manual inter. Metadata discovery and data classification. So Class A power metadata discovery engine automatically scans and classifies data across all your entire ecosystem, enterprise ecosystem. Using that ones the patent recognition, semantic analysis. It identifies sensitive data personally, personal information like PIA, and regulate. Content with unprecedented present. The system continuously inter learns from the user. Feedback through a continuous monitoring and regulate updates, ensuring that classification accuracy improves over time while adapting to the new data types and sources as they imagine your organization. Now let's see how the real-time integration and intelligent routing takes place. So when stream data ingestion the process, it process high velocity data streams from several iot, internet of things, devices, applications, and external APAs with minimal latency and to intelligent parts selection. AI algorithm automatically selects optimal route parts based on the network condition, data sensitivity and performance requirement while enting the data. It also takes the real time decision making, so enabling instant insights and rapid response to the changing business conditions through continuous data processing. Let's see how these algorithms self-learn and performs the anomaly detection. Claire has a self-learning capabilities continuously monitor data quality patterns, identify any deviations before any of the downstream is getting impacted. Are the processes getting impacted? The system builds comprehensive baselines of normal data behavior and uses statistical models to flag any potential issues when anomalies are detected. The A engine not only alerts administrator, but also statist the character actions based on the historical resolution patterns. As I mentioned earlier, it has a continuous. Monitoring and pattern identification methods. So it keeps, it is very easy to check the historical resolution. This proactive approach to data quality ensures consistent, reliable data across all integration points. Now, let's see how Kubernetes native orchestration for containerized data services. So container orchestration native. Kubernetes integrations enables automatic scaling, loading, BA load balancing, and resource optimization for data processing workloads. So when considering the data processing workloads, it performs like PO level scaling based on the data volume, and it allocates the resources and optimizes it. And if there is any there of failures it performs the failure recovery automation. And when it comes to micro microservices architecture. So the data services that can be independently deployed, scaled and maintained within container services can be performed with service mesh integration, a PA gateway management and secure breaker patterns. And it also has a cloud NATO security integrated security controls that leverage Kubernetes native features for comprehensive data protection, like network policies and segmentation secret management. It also supports a role-based access control. Now let's see how the A engine dynamic auto optimizes the workload and cost management, the predictive resource allocations. So Clare has a predictive algorithm which analyzes the torical usage patterns, seasonal trends, and business cycles to optimizes resource allocation automatically, the system anticipates demands. Spikes and scales infrastructure proactively. This intelligent approach to workload management ensures optimal performance during peak load, while minimizes the cost during when there is less much of a demand. Through it, it'll auto scale and de provisioning the resources. Now, compliance automation for regulated industries in highly regulated industries. Claire automates the complex processes of governance, policy enforcement, and audit trial generation. The platform maintains comprehensive lineage tracking automatically generates compliance reports, and ensures that the data handling practices aligns with industry regulations. Through intelligent policy enforcements, class monitors, data access patterns, flags, potential violations in real time and automatically applies the remediation measures. This proactive approach to compliance management significantly reduces the risk of regulatory violations while streamlining audit pro processes. Now let's see the real world successes in the regulated industry. Let's take, let's talk about financial s services, transformation like a banking sector where. Major investment bankings implemented class to automate a risk data aggregation across global trading systems, achieving real-time regulatory records and reporting capabilities. So Clay Class A engine uses machine learning to analyze millions of transactions across accounts rapidly. Spotting unusual. Spendings suspicious transfers or deviations from typical customer behavior. This enables banks to flag the potential of fraud transactions in real time, sending instant alerts to investigation, and even trigger automated interventions like temporarily placing a hold on the accounts. So for complaints, cloud continuously scans activities. For regulatory breaches adapts to policies and generate audit ready reports all in a real time in all in real time keeping institution compliant without manual efforts. Now let's talk about the healthcare in the large healthcare system change data. Integration to create unified electronic health records while maintaining HIPAA or CG or CPDA. Compliance now come to the insurance industry. So in the insurance industry, they are leveraging CLA to automate cleaning. The A can automatically ingest, classify, and route claims based on complexity, urgency, and risk. Enabling foster settlements and tries on high priority cases. So this a powered engine analysis, submitted documents, extract required information and cross reference it against the policy historical claims to validate each submission, reducing manual intervention and review time. So this a detects claims anomalies such as language pattern associated with the fraud. Flagging suspicious cases for a deeper inspection, which result in cost savings and reduce the payout on fraudulent camps. So these are some real world success stories, which is already implemented in the industry using the cla. Now, let's talk about how DevOps and data architecture can use this class A and how it can be leveraged for the future. Class A driven streaming pipeline. Unpredictable planning capabilities, empower DevOps teams and data architects to build scalable, secure solutions that adapts to changing business learning approach ensures that your data integration strategy evolves with the organization, providing the foundation for sustain the digital transformation in a containerized cloud Native neurons. Thank you so much for your time and. Have a good day. Thank you.