The latest articles on DEV Community by M harshitha Reddy (@m_harshithareddy_4fbae98). https://dev.to/m_harshithareddy_4fbae98 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3442777%2F187ee1b4-f678-4914-bc0a-47ac4ff30024.png https://dev.to/m_harshithareddy_4fbae98 en M harshitha Reddy Mon, 18 Aug 2025 13:50:37 +0000 https://dev.to/m_harshithareddy_4fbae98/scaling-applications-with-kubernetes-clusters-252b https://dev.to/m_harshithareddy_4fbae98/scaling-applications-with-kubernetes-clusters-252b <p>Kubernetes is a container orchestration platform that automates deployment, scaling, and management of containerized applications. It enables teams to run microservices reliably at scale, distributing workloads across nodes and ensuring high availability.</p> <p>Key components include Pods, Deployments, Services, and Ingress. Using Kubernetes, developers can implement rolling updates with zero downtime, monitor resource usage, and quickly recover from failures. Security best practices involve setting RBAC policies, network segmentation, and regular cluster patching to mitigate vulnerabilities.</p> kubernetes cloudnative scalability security M harshitha Reddy Mon, 18 Aug 2025 13:49:52 +0000 https://dev.to/m_harshithareddy_4fbae98/how-etl-pipelines-power-modern-data-analytics-12k1 https://dev.to/m_harshithareddy_4fbae98/how-etl-pipelines-power-modern-data-analytics-12k1 <p>ETL (Extract, Transform, Load) pipelines are critical for moving data from diverse sources into a central warehouse for analysis. Modern ETL tools automate the extraction of structured and unstructured data, apply transformations like cleaning, validation, and enrichment, and load it efficiently into cloud platforms such as AWS Redshift, Google BigQuery, or Snowflake.</p> <p>By implementing robust ETL pipelines, organizations can ensure data quality, reduce processing time, and enable real-time analytics. For example, applying schema validation during the transformation stage prevents downstream errors in dashboards, while incremental loading improves pipeline performance.</p> dataengineering analytics cloud softwareengineering