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Do you know Apache Airflow™? Airflow™ is a platform created by the community to programmatically author, schedule and monitor workflows. It easily scales through its modular architecture and use of a message queue to orchestrate an arbitrary number of workers. It integrates out-of-the box with our data warehouse automation tool of choice, VaultSpeed through their workflow management FMC (flow management control) add-on module and generates workflow schedules this way. It ensures that all data pipelines are executed at the right time, in the right order. Whether you’re running your data warehouse in batch mode or using micro-batches and CDC tools. Do you want to know more about Apache Airflow™ and how it can help you boost you business? We would love to tell you more about it! #apache #datawarehouse #automation #workflow

Apache Airflow for Workflow Orchestration! In today's data-centric world, orchestrating complex workflows seamlessly is critical. Apache Airflow stands out as a powerful tool to programmatically author, schedule, and monitor workflows. Here’s a quick look at some key functions that make Apache Airflow an indispensable asset for data engineers and analysts: 🔹 Dynamic Task Scheduling: Define your workflows as Directed Acyclic Graphs (DAGs) and set custom schedules using cron expressions or Airflow’s intuitive time-based syntax. This flexibility ensures your tasks run precisely when needed. 🔹 Task Dependencies Management: Airflow excels in managing task dependencies, ensuring that tasks execute in the correct order. By setting upstream and downstream relationships, you can create complex workflows with ease. 🔹 Extensible Operator Ecosystem: Airflow offers a wide range of built-in operators to interact with various services like databases, cloud providers, and data processing frameworks. Additionally, you can create custom operators to fit your unique needs. 🔹 Scalable and Distributed Execution: With Airflow’s ability to scale horizontally, you can distribute task execution across multiple workers, ensuring high performance and reliability even for large workflows. 🔹 Monitoring and Alerts: Stay informed with real-time monitoring and logging. Airflow's user interface provides a detailed view of your workflow's status, and you can set up email or Slack alerts for task failures or other critical events. 🔹 Templating with Jinja: Simplify your DAGs and tasks using Jinja templating. This allows you to create dynamic workflows by injecting variables and context-specific information directly into your task parameters. 🔹 Integration with CI/CD Pipelines: Seamlessly integrate Airflow with your CI/CD pipelines to automate testing, deployment, and monitoring of your workflows, ensuring a streamlined development process. #ApacheAirflow #WorkflowOrchestration #DataEngineering #DataWorkflow #Automation #BigData #DataAnalytics #TechInnovation

I've been meaning to write about this for the longest time, and I finally got around to it. This is my first public write-up focusing on how to use Airflow to replace an enterprise solution. #ApacheAirflow

Experience the future of Ephemeral cluster management with Weave GitOps Enterprise. You can effortlessly spin up and down Ephemeral clusters in minutes, providing the flexibility you need when and where you need it. Watch this demo demonstrating how Weave GitOps Enterprise streamlines this process. You'll see a pipeline for visualizing and promoting applications through different environments, including Ephemeral ones. 👀 https://lnkd.in/eJctAwGF

Want to get the best out of your Apache Airflow Environment? Control-M integration enables users to incorporate Airflow DAG execution into Control-M workflows. Provinding an end-to-end view of business application workflows that span traditional applications with newer applications using a modern data stack. This ensures positive observability and increased business service management across teams in Data, Operations and Dev. To learn more, please use the link below #Control-M #DataWorkflow #ApacheAirflow #Integration #DataOps #BMC

Integration spotlight: Airflow & Neptune ⬇️ Keep track of parameters, metrics, artifacts and any other metadata when training or testing models in a workflow. Benefit from the out-of-the-box way to: - Track the workflow/DAG config - Have metadata from different tasks in one place - Visualize the workflow for monitoring and debugging - Compare model results Here’s how it works: https://buff.ly/48eX74E #ml #mlops #integration

Building a data platform on Kubernetes? Looking for a leg up on automating the process? Josh Lee and I will be showing how Terraform, Helm, and Argo CD can help you automate setup of analytic stacks based on ClickHouse. Join us on July 23 to find out more! https://lnkd.in/grRrF32j #opensource #clickhouse #kubernetes #terraform #opentofu #helm #argocd #analytics

REMINDER: The next Dr. Jaspersoft monthly webinar will be held on Thursday, June 13th and will feature an overview of JaperReports IO, our embeddable microservice reporting engine. Sign-up now to learn how JasperReports IO enables effortless, powerful reporting and simplifies deployment via Docker containers - great for enhancing DevOps pipelines. https://lnkd.in/exfGfweG

Learn what Apache Airflow is, why monitoring it matters, and how using OpenTelemetry to do so can help improve the reliability and performance of your Airflow workflows.