Apache Airflow Reviews

I am using Apache Airflow to orchestrate my jobs, projects, and batch ETL jobs. It manages tasks, orchestrates jobs, and helps in handling batch processes effectively, incorporating integrations like DBT and Great Expectations.

Apache Airflow is easy to scale and its UI improves with each release. Reliability is good, and when integrated with Kubernetes, it performs better compared to on-premises environments. It also facilitates the construction of data pipelines by various teams, including those with limited technical knowledge.

We are using Apache Airflow, which has a modular architecture, integrated with our pipeline and with the code generation using Python and the code pipeline. We are running it in Airflow. It is easy for us to use to build the machine learning models, transport data, and manage our infrastructure.

Apache Airflow is easy for us to use to build machine learning models, transport data, and manage our infrastructure. It generates data using the pipeline, and we use Terraform code to run the airflow engine. The solution is easy for us to install and maintain and is very scalable. Apache Airflow is an open-source platform that allows easy integration with AWS, Azure, and Google Cloud Platform.

Apache Airflow is an open-source tool. Apache Airflow can be integrated or used to run multiple files. The tool is helpful in the orchestration process, and it also allows our company to provide some notifications to teams via email. The main valuable feature of the tool is that it is an open-source product and that it can be integrated with any cloud environment.

I have not come across any challenges associated with the product.

The scripts that we use in our company refer to the package dependencies in Python, but those are lost when Apache Airflow starts running for a particular test.

The in-built package dependencies in Python have some issues in Apache Airflow, making it an area that needs improvement.

The primary use case for us is ETL pipelines. We write some pipelines to ingest the data. That is the primary use. And, secondly, we use it to run some scheduling and orchestration. We need to run some automation jobs every day. So, we just write an Airflow task and pipeline that runs every day or every hour or however we need. Those are the two things we use it for.

Since integrating Airflow, we are efficiently able to build pipelines around it in days. If there is a requirement within days or at the end of the week, we can create a pipeline for it.

Apache Airflow is like a freeway. Just as a freeway allows cars to travel quickly and efficiently from one point to another, Apache Airflow allows data engineers to orchestrate their workflows in a similarly efficient way.

There are a lot of scheduling tools in the market, but Apache Airflow has taken over everything. With the help of airflow operators, any task required for day-to-day data engineering work becomes possible. It manages the entire lifecycle of data engineering workflows.

So, for example, let's say you want to connect to multiple sources, extract data, run your pipeline, and trigger your pipeline through other integrated services. You also want to do this at a specific time.

You have a number of operators that can help you with this. For example, you can use the External Sensor operator to take the dependency of workflows. This means that you can wait for one workflow to complete before triggering another workflow. There are also good operators like the Python operator and the Bash operator. These operators allow you to run your scripts without having to change your code. This is great for traditional projects that are already running on batch.

So, let's say you have a scheduled alert called Informatica. You can use Airflow to trigger your VTech scripts through the Informatica jobs. This way, you don't need to use Informatica as a scheduling engine. That's a good way to decouple your data pipelines from Informatica. Airflow is a powerful tool that can help you to automate your workflows and improve your efficiency.

We use the tool to schedule data pipelines. We also use Apache Airflow to orchestrate dbt, another data processing tool. Airflow helps manage dbt processes, which, in our case, load data from our data lake.

To increase efficiency, it's quite simple to add dbt tasks to an Apache Airflow pipeline or orchestration file. With the tool, you can specify dependencies.