Deploying Grafana on Kubernetes using Deployment and NodePort Service

The Kubernetes community has rolled out alpha support for a changed block tracking mechanism, enhancing storage efficiency. I found it interesting that this feature could significantly streamline operations for cloud-native applications. What are your thoughts on the impact of improved block tracking on data management strategies in Kubernetes?

🔥 Meet Confluent for VS Code 2.0 We're proud to announce the newest release of Confluent for VS Code, a major milestone that streamlines the development of Flink UDFs inside VS Code: ➡️ Full Flink UDF lifecycle support: scaffold, develop, test, deploy, register, and use UDFs, all from within VS Code. ➡️ Built-in project templates to jumpstart Flink UDF development. ➡️ Automatic UDF discovery: the extension now parses .jar artifacts and auto-populates available class names for registration. ➡️ Rich metadata for registered UDFs, see parameters and signatures at a glance just by hovering on the UDF. ➡️ Enhanced Flink SQL authoring: IntelliSense support for referencing UDFs, plus Copilot code completion. Check out the extension's source code on GitHub (https://lnkd.in/ebnYmPuV) or install it from the marketplace (https://lnkd.in/euv2fPsC).

Exciting news from the Kubernetes team! They have announced alpha support for a changed block tracking mechanism, which enhances efficiency in data management. What stood out to me was the potential impact this could have on optimizing storage solutions within Kubernetes environments. How do you think this feature might change data management strategies in cloud-native applications?

The recent announcement of alpha support for a changed block tracking mechanism in Kubernetes is a significant development for enhancing data management efficiency. I found it interesting that this feature aims to optimize performance for workloads that require rapid data access. As Kubernetes continues to evolve, what capabilities do you think will be most impactful for developers in the next few years?

In this post, solution architect Mohammad Shoeb describes how shifting the integration plumbing — things like HTTP client factories, retry logic, Kafka producers, JSON serialization, error handlers, and health-check utilities — out of service code and into infrastructure (via a single declarative config file + a Dapr side-car) substantially reduced boilerplate. By injecting one side-car alongside eight .NET services he achieved a 48% reduction in messaging and service-invocation glue code — and eliminated the need for custom brokers and brittle HTTP wrappers. 

Exciting news in the Kubernetes community! The announcement of alpha support for a changed block tracking mechanism is a significant step forward. I found it interesting that this feature aims to improve efficiency in data management, which is critical for modern cloud-native applications. How do you see this impacting your workflows or projects? Read more here: https://lnkd.in/drHMVCUj

🟢 What Is a ConfigMap in kubernetes? ✔️ ConfigMaps in Kubernetes are used to store non-sensitive configuration data as key-value pairs, allowing you to decouple configuration from application code. ✔️ A ConfigMap is a Kubernetes object that holds configuration data such as: 👍 Environment variables 👍Command-line arguments 👍Configuration files 🟢 This allows your containers to be portable and environment-agnostic, since the config is injected at runtime rather than baked into the image. ⚠️ Note: ConfigMaps are not secure. For sensitive data, use a Secret instead. 🟣 How to Create a ConfigMap? 🔻 From literal values 🔻From file 🔻From directory 🔻from yaml manifest. 📥 How to Use a ConfigMap? 🔹 As Environment Variables. 🔹 As Volume Mounts. 🔹 As Command-Line Arguments. 🔹 As configMapKeyRef. #LearnKubernetes #learnLinux #LearnDevOps #shareKnowledge #likeFollowShare #completeDocument_@https://lnkd.in/d6_FyCXf

Automate & Govern: Create Iceberg S3 Tables with GitLab CI/CD & YAML Automate and govern your S3 Iceberg tables with GitLab CI/CD! In this video, I show how to define tables via YAML—once a YAML file is created or updated, it gets reviewed, merged, and automatically provisions an Iceberg table with its schema. Delete the YAML and push changes? The corresponding table is automatically removed. A fully automated, central, and auditable process for managing your S3 tables efficiently. Code https://lnkd.in/eDVB_kM9 #AWS #DataEngineering #S3 #IcebergTables #GitLabCI #CICD #Automation #DataGovernance #BigData #YAML #DataPipeline #Serverless #CloudData #ETL #DataOps

GitHub - jvanbuel/flowrs: Flowrs is a TUI application for Apache Airflow that allows you to monitor, inspect and trigger Airflow DAGs from the comforts of your terminal., https://lnkd.in/eMsp4HAP IA Summary: Flowrs transforms your Apache Airflow experience by letting you monitor, inspect, and trigger DAGs directly from your terminal. Discover how this powerful TUI application brings unparalleled convenience and control to your workflow orchestration. #apache #airflow #TUI #flowrs

🚀 Excited to share my latest Rust-based 🦀 open-source project — Kafka2i! A terminal UI for Kafka that makes debugging and message inspection a whole lot easier. If you’ve ever struggled to fetch messages from Kafka using the standard CLI tools or kcat, you know the pain! OAuth based setup makes it even more painful. Kafka2i simplifies this: - Explore Kafka metadata easily - Fetch messages by offset or timestamp - Seamlessly handle OAuth authentication & refresh tokens If you work with Kafka, I’d love your feedback! https://lnkd.in/dDCJ3BBN #rust #ratatui #rdkafka #TUI #opensource