Hello everyone. This is RA Maria, and today I'll be talking about cloud native MDM utilizing Kubernetes orchestration for enterprise data quality. At scale Master data management system is evolving from monolithic systems to cloud native architectures. This transformation leverages Kubernetes orchestration to deliver enterprise grade data quality at unprecedented scale, combining the reliability of. Traditional MDM with agility of modern cloud platforms, why master data management must evolve? Traditional MDM systems are heavyweight stateful and slow to scale in modern distributed environments with hybrid and multi-cloud deployments, it they demand flexible portable data management solutions. No, we are into microservices era. Distributed architectures require MDF systems. That can integrate seamlessly across services to meet the real time demands. Modern enterprises need immediate data quality and availability for critical business decisions. The enterprise IT landscape has fundamentally changed with containerization, making workloads, portable and dynamic cloud native MDM Rethinks data mastering principles to work seamlessly in Kubernetes ecosystems providing a single source of truth. For customers, products, suppliers, and financial accountants. Cloud native MDM represents a complete redesign of data mastering for distributed, elastic and automated environments. It's not simply ization the legacy MDM products, but fundamentally rethinking how data quality and governance work in modern infrastructure. With microservices first, design each MDM capability like matching. Merging validation. Governance operates as its own containerized service with clear boundaries and responsibilities. Kubernetes manifest provide declarative infrastructure, helm charts and GI tops, manage deployments and configuration through code-based approaches. Horizontal part autoscaler. Handles variable data loads automatically scaling resources up and down based on demand, providing elastic scaling. The backbone of orchestration pods and deployments encapsulate my MD microservices, like matching engines and data quality validators in managed scalable units. State full sets help in managing ordered stable PO pod identities and persistent data for core MDM stores that require state consistencies persistent volumes provide durable storage beyond ephemeral containers, ensuring data service PO restarts and failures with enforcing the secure, reliable interservice communication through tools like T and linker service mass integration can be easily achieved. Kubernetes transforms MDM into fragile static deployments to self-healing, dynamically scalable infrastructure config maps, and secret manage governance rules and credentials. With horizontal port auto scaling responds automatically to data processing spikes. How? How Help charge help in streamlining MDM deployment with seamless single command deployment for integrated multiple components in MDM architecture. Dynamic Parameterization for precise environment specific configuration, robust dependency orchestration for interconnected sub charts. Assurance of safe and reliable roadblocks for control updates help revolutionizes the deployment of Complex M Indian systems by encapsulating them as version ParaBit rise packages. This allows teams to consolidate all critical events from data matching and cleansing. To user interfaces and APIs into single manageable chart. The result is unparalleled consistency across development, staging, and production environments drastically minimizing configuration drift and accelerating time to value, which is hard to achieve In monolithic MDM systems. Handling stateful workloads in Kubernetes requires sophisticated patterns to ensure data integrity and consistency across the distributed environments. Stateful sets provide stable network identities and order deployment for critical MDM components. Abstract cloud pro storage providers like A-W-S-E-B-S, Azure Disk, and GCP per certain disk provide persistent volume so that data can be lost. Tools like ETCD or CockroachDB ensure consistent state across nodes and prevent data corruption, providing consensus protocols with. Cloud native MDM, it's relatively easier to scale for millions of records. 99.9% availability can be achieved even when processing millions of records per day. Organizations can run 24 7 operations with continuous data processing with automatic scaling, and up to a million plus record can be mastered at enterprise scale with this infrastructure. Kubernetes native MDM handles massive data loads through horizontal port autoscaler, sharded data stores, and even driven architecture using Kafka and pulse resources quota scale prevent service starvation while maintaining optimal performance. This architecture provides a self-healing and resilient system. Likeness and readiness probes automatically restart unhealthy pots, ensuring continuous operation and monitor health. Continuously circuit breakers prevent cascading failures between independent services through intelligent failure isolation. Stateful sets smooth workloads to healthy nodes with persistent storage intact during failures, providing automated failover mechanism tools like chaos Mash. Validate that MDM survives unexpected disruptions and maintains data integrity. Data quality without visibility is a black box, which is one of the major problems in existing MDM solutions, but with cloud native MDM, which integrates deep observability to transform MDM from an opaque system into an actionable data operations platform. Prometheus exporters publish matrices for data validation, throughput matching latency, and merge accuracy. Grafana dashboards provide at a glance view of MDM Health and Performance with customizable visualizations. Jacker or Open Telemetry can be used for distributed tracing to follow data records across microservices. Intelligent alerting can be placed with notify, which notify DevOps teams to f fulfill data loads. Still governance rules or degraded SS managing configuration manually error prone and lacks audit auditability, tops brings discipline, traceability and agility to MDM, which is a critical requirement in regulated industries. With version control rules, every change in update to data matching survivorship or validation policies is tracked and get with full history. Automated deployments using CSCD pipelines apply Kubernetes manifest date, relatively reducing human error and deployment time. We can revert to known good configurations if governance updates cause issues, ensuring system stability and providing safe rollbacks. I would like to talk about enterprise system integration. In this way, we can integrate with API gateways using Kong A PG index, which expose master data to application securely with rate limiting and authentication Streaming platforms like Kafka and Puler can ingest raw data and broadcast master updates in real time across in the enterprise. With for the legacy systems, integration can be achieved using adapters and bridge services, which can bring mainframe or ERP data into cloud native fabric seamlessly. DBT Airflow and Blue Feed data warehouses and analytics platform with high quality master data can be leveraged using ETL and ELT pipeline. By using standard APIs and event driven patterns, MDM becomes an integrated hub rather than an isolated silo, enabling comprehensive data governance across the enterprise ecosystems. Data mastering often involves sensitive and regulated information. Cloud native MDM enforces comprehensive security measures while maintaining cloud agnostic compliance capabilities. We can ensure security with zero trust. Networking where mutual tail is between services via server mash ensures encrypted communication and identity verification. Kubernetes secrets integrated with vault systems like HashiCorp Vault or secure for secured credential handling can help in secured management. End-to-end encryption can be achieved and data can be encrypted in transit with TLS and rest using cloud provider KMS or CSI Drivers fine. Green access controls and comprehensive audit logs to meet the G-D-P-I-C-C-P and other industry requirements can be achieved with RPAC and audit. In one of the studies, the our global retailer. Was face facing the a huge roadblock with when they had, like with millions of products and customer records with daily ingestion peaks During seasonal sales, legacy MDM downtime was affecting order fulfillment and customer experience to get away with this problem, they deployed MDMS microservice with he state full sets backed by cloud block storage. Kafka for ingestion and still were secure interservice communication. As a result, they achieved 99.9% uptime, including Black Fridays in the peak load, 30% cost reduction using elastic scaling during off peak periods, and the rule deployments were easier like never before with new governance rules via GI tops being deployed in minutes. With this. In this slide, I would like to talk about some common best practices and pitfalls. Best practices include start small. Begin with one domain, like customer data before expanding to other entities. Choose databases and consensus strategies upfront to avoid costly refactoring later. Automate everything using C-A-C-I-C-D for both application and data governance. Configurations, observe relentlessly build comprehensive dashboards before going live. To ensure visibility, the most common pitfalls which we have observed are stateless assumptions. Treating MDM like a stateless app leads to data loss on PO restarts storage latency, ignoring storage performance flows, matching and merging under load network complexities. Underestimating service match. Operational overhead and configuration complexity may be a nightmare on later stages. Missing observability, skipping monitoring, making debugging makes debugging impossible, but data quality degrades. So all these things should be in place before we go live. Cloud native MDM continues evolving as enterprises modernize their data infrastructure. The future promises even greater automation, intelligence and integration capabilities. Machine learning will improve identity resolution, matching accuracy and survival survivorship decisions automatically. With serverless extensions, we can offload specific data transformations to fast platforms for cost effective event driven processing. MDMs deployment with multicast deployment. Nearer to closer to the data regions. Through edge computing and distributed cluster architecture can be achieved. We can build self tuning systems with automated data Pipelines with anomaly detection will minimize human intervention requirements. Kubernetes and cloud native design have redefined how data mastering can scale, heal, and integrate in modern enterprise. By embracing these technologies, organizations achieve resilient, observable, and scalable MDM platforms capable of supporting the next decade of data driven innovation. Thank you for your time.