DevOps

Who is DataOps and Why It Matters DataOps (Data Operations) is a methodology and role that focuses on improving the quality, speed, and reliability of data delivery for analytics and decision-making. If DevOps optimizes software delivery, and MLOps optimizes machine learning workflows, then DataOps ensures organizations can effectively manage and deliver data. What Does a DataOps Engineer Do? A DataOps engineer builds and maintains processes and infrastructure that guarantee fast and trustworthy data pipelines. ...

Who is MLOps and Why It Matters MLOps (Machine Learning Operations) is a discipline that combines machine learning (ML), development, and operations. If DevOps automates and speeds up software delivery, MLOps does the same but for machine learning models. What Does an MLOps Engineer Do? The role of an MLOps engineer is to ensure that ML models are not only trained but also reliably deployed, monitored, and maintained in production. ...

What are Unix Domain Sockets and How They Work When we think about communication between applications, we often imagine TCP or UDP over the network. But sometimes, processes don’t need to leave the machine at all. That’s where Unix Domain Sockets (UDS) come in. What is a Unix Domain Socket? A Unix Domain Socket is a special type of inter-process communication (IPC) mechanism in Unix-like systems. Instead of using IP addresses and ports, processes talk to each other via a file path on the filesystem (e.g., /var/run/docker.sock). ...

Who is DevSecOps and Why It Matters DevSecOps is a practice that combines development (Dev), operations (Ops), and security (Sec) into a single workflow. If DevOps focuses on automation and speeding up delivery, DevSecOps adds a mandatory layer of security to the process. What Does a DevSecOps Engineer Do? The main responsibility is to embed security into every stage of the CI/CD pipeline. They ensure that code, infrastructure, and processes are secure and compliant. ...

How to Install and Use Kafka Schema Registry Kafka Schema Registry is a separate service that helps ensure message format consistency in Apache Kafka. Let’s see how to install it and use it in real-world scenarios. Installation Options Docker / Docker Compose The fastest way to try it is using Confluent’s Docker images. Example docker-compose.yml snippet: version: '3' services: zookeeper: image: confluentinc/cp-zookeeper:7.5.0 environment: ZOOKEEPER_CLIENT_PORT: 2181 kafka: image: confluentinc/cp-kafka:7.5.0 environment: KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181 KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 schema-registry: image: confluentinc/cp-schema-registry:7.5.0 environment: SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: PLAINTEXT://kafka:9092 SCHEMA_REGISTRY_LISTENERS: http://0.0.0.0:8081 ports: - "8081:8081" Run with: ...