Apache Airflow Scheduler Update Issue Resolved

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!!Kube-S3!! Storage is often the backbone of any application setup, and getting it right means most of the heavy lifting is already done. That’s why I’ve been working on a project that leverages S3 buckets mounted into Kubernetes pods via FUSE. This approach lets pods access S3 data as if it were on a local disk—perfect for parsing and importing large datasets without worrying about manual key management. Of course, #S3 isn’t a perfect replacement for local storage (latency in metadata ops, no atomic renames, eventual consistency), but when combined with #Kubernetes mount propagation, it becomes a powerful way to share persistent, cloud-based storage seamlessly across multiple pods. With just a few steps—building the #S3Fuse pod, deploying it, and mounting data into /var/s3—we can unlock a scalable, flexible, and cloud-native storage layer. https://lnkd.in/dcMquYcY

What if ONE pipeline could pull data from multiple servers and hundreds of tables automatically? No copy-paste. No manual configuration. Just pure automation. Instead of creating separate pipelines for each data source, I use a single metadata table in Fabric data pipeline to control everything. Here's how it works: → Metadata table stores configurations (servers, tables, watermark columns) → One pipeline reads metadata and dynamically connects to multiple SQL servers → Copy Activity loads data into the staging layer → ForEach loop handles incremental Upsert for all sources → PySpark updates watermarks after each load There are multiple ways to pull data from different servers i.e,. separate pipelines, linked services, or even manual integrations. But a metadata-driven approach is far more sustainable. It centralizes control, reduces maintenance overhead, and scales effortlessly. Once the framework is in place, you’re not building pipelines anymore, you’re just managing metadata. #MicrosoftFabric #DataEngineering #DataPipeline #ModernDataStack #DataArchitecture #ETL

Your microservices still using polling for sync? Here's what we learned switching 47 services to CDC We had 47 microservices, REST APIs everywhere, database polling every 5 seconds. Sound familiar? Then we implemented CDC with Debezium. Result: latency dropped from minutes to milliseconds, zero additional DB load, no more race conditions. The shocking part? Our entire Go consumer is just 200 lines of production code. No "last_updated" columns, no complex ETL jobs, no polling loops. Just reading the transaction log and streaming events in real-time. https://lnkd.in/ddF6dv_H

Airflow Task States – Explained Simply When working with Apache Airflow, every task you define in a DAG goes through a “life cycle” of states. Understanding these states is crucial for debugging and optimizing workflows. Here are the key ones: 1. none → Task is defined but not ready (dependencies not met). 2. scheduled → Dependencies are met, and it’s scheduled for execution. 3. queued → Waiting in line to be picked by a worker. 4. running → Actively executing on a worker. 5. success → Completed without errors. 6. failed → Task ran but ended with an error. 7. skipped → Skipped due to branching logic. 8. up_for_retry → Failed once but retrying again. 9. upstream_failed → Blocked because a dependent task failed 10. shutdown / restarting/deferred/removed → Special states for interruptions or changes. ---> Ideal Task Flow: none → scheduled → queued → running → success If your tasks follow this flow, you’re in good shape! Mastering task states is one of the first steps to becoming confident in Airflow troubleshooting. #ApacheAirflow #DataEngineering #WorkflowAutomation #BigData #ETL

**High Availability on Kubernetes: Making Database Clusters Bulletproof** Spent today fine-tuning Patroni PostgreSQL clusters across our Kubernetes environments - SIT to DR synchronization in action! Why K8s + Patroni is a game-changer for database HA: • Pod-level failover with automatic leader election • Cross-cluster replication made simple with kubectl • Built-in health checks and self-healing capabilities • Seamless scaling across multiple availability zones Running databases on Kubernetes used to be scary. Now with operators like Patroni, it’s becoming the gold standard for resilient data infrastructure. The beauty? One kubectl command can show you the entire cluster topology across environments. Infrastructure as code at its finest. Database downtime in production? Not on our watch. #Kubernetes #PostgreSQL #Patroni #DatabaseOperator #HighAvailability #CloudNative #DevOps #SRE

🚀 Building Efficient Data Pipelines with Apache Airflow & S3 🚀 Today, I explored integrating Apache Airflow with Amazon S3 to streamline data workflows. The combination is a game-changer for modern data engineering: ✨ Key Highlights: Automated Data Ingestion: Airflow DAGs can automatically pull data from S3 buckets at scheduled intervals. Seamless Integration: Using Airflow’s S3 hooks and operators, data transfer becomes efficient and reliable. Scalable Pipelines: Easily handle multiple files or datasets, triggering downstream tasks like transformations or loading into Redshift/Databases. Error Handling & Monitoring: Airflow provides retries, alerts, and logs to ensure robust workflows. 💡 Practical Use Cases: ETL pipelines for analytics Backup automation Event-driven data processing Data migration between S3 and databases Integrating Airflow with S3 simplifies pipeline orchestration, reduces manual effort, and ensures data flows reliably across systems. #ApacheAirflow #DataEngineering #S3 #ETL #CloudComputing #DataPipelines #AWS

I recently wrote a hands-on guide on setting up Apache Airflow with Helm, Keycloak, and EFS/S3 DAG syncing. Writing this helped me solidify the workflow end-to-end, and I hope it helps others tackling similar deployments. Check it out:

Breaking Down Apache Airflow Architecture If you’ve just started with Airflow, understanding its architecture is the first step to mastering it. Let’s simplify it 1. Scheduler The brain of Airflow. It determines when a task should run and instructs the Executor to initiate it. 2. Executor Think of it as the “middleman” — it takes tasks from the Scheduler and ensures they are assigned to the right workers. 3. Webserver (UI) The place where we monitor and interact with DAGs. Want to see task dependencies or check why something failed? The Web UI is your go-to. 4. Metadata Database The memory of Airflow. It stores everything — which tasks ran, which ones are queued, which failed, etc. 5. Workers The doers! Workers are the ones who actually execute the tasks defined in your DAGs. Together, these components make Airflow a powerful orchestration tool for scheduling and managing workflows at scale. #ApacheAirflow #DataEngineering #ETL #WorkflowAutomation #BigData