Chaitanya Kannali

Native and eBPF-based Kubernetes Workload Profiling for Kubernetes Clusters By capturing profiling data at regular intervals from your Kubernetes cluster or applications, you can gain valuable insights into your system’s behavior, quickly detect anomalies, and pinpoint areas that may require optimization. This proactive monitoring ensures that your system remains efficient and reliable. In this blog post, my colleague Reza Ramezonpur articulates how profiling can benefit your environment and how you can enable it using Calico’s native capabilities or for any other application by using eBPF-based probes. By incorporating profiling into your monitoring strategy, you’ll be able to optimize your system’s performance, improve its reliability, and gain valuable insights into its behavior. A link to the blog is available in the comments section. ———— Tigera - Container Security with built-in network security #CNAPP #zerotrust #cloudnative #Kubernetes #containersecurity #cloudsecurity #eBPF #Istio #OpenShift #Rancher #AWS #Azure #GCP #Fortinet #PaloAltoNetworks #Zscaler #CrowdStrike #SentinelOne #EKS #AKS #GKE #Anthos #RedHat #Devops #SRE #Containers #GenerativeAI

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Elevate your Elastic game with our webinar focused on migrating from Elastic Cloud Enterprise (ECE) to Elastic Cloud on Kubernetes (ECK). ☁️ Learn migration strategies, advantages of managed Kubernetes platforms, live demos, and post-migration best practices. https://gag.gl/bodqQv #ECE #ECK #Webinar

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Addressing the Challenges of Shared Data Access Across Multiple #K8s Clusters When deploying services across different Kubernetes (k8s) clusters, ensuring consistent and efficient access to shared datasets becomes a significant challenge. Here are some of the key issues: 1. Latency and Performance: Accessing an external DBaaS from multiple clusters often results in high latency and reduced performance due to network overhead and #database contention. 2. Consistency and Synchronization: Keeping data #consistent across clusters is difficult, especially when services need to update shared datasets. This often leads to data inconsistency and synchronization issues. 3. Caching Limitations: Implementing a #cache on top of the #DBaaS might improve read performance but falls short in ensuring data consistency and handling write operations effectively. Caches can become stale, leading to outdated information being served. 🔍 The Solution: GigaSpaces #XAP Skyline as a Backbone Shared Data Bus GigaSpaces Technologies XAP Skyline provides a robust solution by acting as a backbone shared data bus hosted within each k8s cluster. Here's how it addresses the challenges: - Data Locality: XAP Skyline instances within each cluster ensure that data is locally available, drastically reducing access latency and improving performance. - Real-time Synchronization: XAP Skyline handles bi-directional replication between XAP instances, ensuring that updates are propagated in real-time, maintaining data consistency across clusters. - Flexible Topologies: Whether it's hub-spoke, ring, or federated deployment topologies, XAP Skyline supports various configurations to suit different needs. This flexibility allows for uni or bi-directional data replication, catering to diverse data management requirements. With GigaSpaces XAP Skyline, you can achieve ultimate data locality and consistency, making it an ideal choice for complex multi-cluster environments. Embrace seamless data access and synchronization with XAP Skyline, and power your distributed applications with confidence. Nati Shalom Shai Zmigrod Stephen Zampino Vin Costello Ilana Elstein Ami Zvieli Nadine Morales Giyora Ramati #Kubernetes #DataManagement #GigaSpaces #XAPSkyline #DBaaS #CloudComputing #DataConsistency #MultiClusterDeployment #HighPerformanceComputing #RealTimeData

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