With over 27,000 GitHub stars and an ecosystem of 1,900+ plugins covering every major cloud provider, database, and SaaS platform, Kestra is the orchestration engine that brings Infrastructure as Code principles to workflow automation — defining complex multi-step pipelines in readable YAML that execute across any language, runtime, or infrastructure boundary. The built-in VS Code-style editor provides syntax highlighting, auto-completion, real-time validation, and an AI Copilot that generates workflow YAML from natural language descriptions. Tasks execute in Python, Node.js, Go, R, Shell, SQL, or any Docker container, with event-driven triggers listening for file arrivals on SFTP and cloud storage, messages from Kafka, Redis, Pulsar, AMQP, MQTT, NATS, AWS SQS, Google Pub/Sub, and Azure Event Hubs in real time. The topology view visualizes workflow DAGs with execution state, duration, and output artifacts for each task node. Namespaces organize workflows into isolated environments with configurable secrets, while subflows enable modular composition with inputs, outputs, and conditional branching. Retry policies, timeouts, error handlers, and automatic backfills for missed schedules ensure reliability across production workloads. Git integration pushes workflows directly to branches from the UI with CI/CD pipeline support for automated deployment. The REST API enables programmatic workflow management, execution triggering, and resource provisioning. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Backed by 8,700+ GitHub stars and designed as a modern alternative to Apache Airflow, Mage delivers the open-source data pipeline platform that combines the interactive flexibility of notebooks with production-grade orchestration in a single self-hosted environment accessible at port 6789. The modular block architecture lets data engineers compose pipelines from Python, SQL, and R code blocks with instant data previews, live execution logs, and visual debugging at each step. Over 100 prebuilt integrations connect sources and destinations including PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, S3, Kafka, MongoDB, Amplitude, Salesforce, and Stripe with parallel stream synchronization for high-throughput data movement. Batch pipelines run on cron schedules or event triggers while streaming pipelines process real-time data from Kafka, Kinesis, and RabbitMQ with stream mode reducing memory usage by approximately 90 percent compared to batch processing. Native dbt integration builds, tests, and runs dbt models directly inside the pipeline editor alongside custom transformation blocks. Spark, Snowpark, and Databricks runtimes handle large-scale distributed processing. AI-assisted development generates code, fixes errors, and optimizes queries within the notebook interface. Monitoring dashboards track pipeline health with integrations to Datadog, Prometheus, New Relic, and OpenTelemetry. Terraform templates deploy production environments to AWS, GCP, or Azure with two commands, while Helm charts support Kubernetes clusters. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.