Airflow

Used for scheduling ETL jobs Airflow

Manage the calculation pipeline and data distribution procedures. Airflow

I use Airflow because it's the gold standard for scheduling batch data jobs. It comes with a bit of a learning curve given the extensive UI and working with different connectors. However, it has a lot of great retry features, and the visual DAGS help with a lot of troubleshooting.

I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

1. Trigger Matillion ETL loads
2. Trigger Attunity Replication tasks that have downstream ETL loads
3. Trigger Golden gate Replication Tasks
4. Shell scripts, wrappers, file watchers
5. Event-driven schedules

I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

I need to implement a Node.js cron scheduler like Airflow. Is it possible to implement it without working on Python? Till now, all my jobs are running on my server only via internal script calling another job scripts. Any alternative or better way to implement?

I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

1. Trigger Matillion ETL loads
2. Trigger Attunity Replication tasks that have downstream ETL loads
3. Trigger Golden gate Replication Tasks
4. Shell scripts, wrappers, file watchers
5. Event-driven schedules

I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

• Web: a mix of JavaScript/ES6 and React.
• Desktop: And Electron to ship it as a desktop application.
• Android: a mix of Java and Kotlin.
• iOS: written in a mix of Objective C and Swift.

• The core application and the API written in PHP/Hack that runs on HHVM.
• The data is stored in MySQL using Vitess.
• Caching is done using Memcached and MCRouter.
• The search service takes help from SolrCloud, with various Java services.
• The messaging system uses WebSockets with many services in Java and Go.
• Load balancing is done using HAproxy with Consul for configuration.
• Most services talk to each other over gRPC,
• Some Thrift and JSON-over-HTTP
• Voice and video calling service was built in Elixir.

• Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.

• For server configuration and management we use Terraform, Chef and Kubernetes.
• We use Prometheus for time series metrics and ELK for logging.

I use Airflow because it's the gold standard for scheduling batch data jobs. It comes with a bit of a learning curve given the extensive UI and working with different connectors. However, it has a lot of great retry features, and the visual DAGS help with a lot of troubleshooting.

I need to implement a Node.js cron scheduler like Airflow. Is it possible to implement it without working on Python? Till now, all my jobs are running on my server only via internal script calling another job scripts. Any alternative or better way to implement?