Home>Data Ingestion Tools>Apache Kafka>Start Automating your Data pipelines with Apache Airflow
Apache Kafka Articles Data Pipeline Data Pipelines Data Structuring Kafka Kafka Architecture Real Time Streaming

Start Automating your Data pipelines with Apache Airflow

About Apache Kafka & Apache Airflow

Kafka is a popular messaging platform based on pub-sub mechanism. It is a highly available, fault tolerant & distributed system. Most organisations are using Kafka in different use-cases, but the best part of Kafka is it’s service oriented architecture, it makes it language agnostic, and gives it wide usability. One of the Kafka use-case involves integration with Python. Existing Python based applications can communicate with each other using Kafka message queue. Or Kafka can be used as an Enterprise Service Bus (ESB) where all data transfers happen via Kafka. This is a standard example of Enterprise Streaming Data Pipelines With Apache Airflow.

Apache Airflow is a workflow manager to schedule, orchestrate & monitor workflows. It is defined as ‘A platform to programmatically author, schedule and monitor data pipelines, by Airbnb’. It is built on the principle of ‘Configuration as a code’, it simplifies increasingly complicated enterprise workflows. Airflow utilizes directed acyclic graphs(DAG) to manage workflow orchestration. Tasks and dependencies are detailed in Python and then Airflow manages the orchestration and execution.

 

Data Pipelines Architectures with Apache Airflow

Airflow data pipeline example

Best Data pipeline example of Apache Airflow is for Machine Learning (ML) workloads, where we can create a preliminary ML model. This model would be reinforced with a streaming platform i.e. Apache Kafka platform, Users ML usage and feeds would be streamed into Kafka (Simply produced or published to Kafka). The data from this streaming platform would be updated real-time (That’s why it’s called Streaming). Our ML model would periodically poll corresponding Kafka topics  for the latest data set and update itself. The model improvises each time with the latest datasets.

All this would be orchestrated with Apache Airflow. All runtime metadata can again be logged into a Kafka topic or an external database for monitoring. But rather we can utilize the already available Apache Airflow for the same.

Similarly, one can easily use Kafka for Integration, message queue, feeds & logs aggregation and Big data workloads. All these use cases can be built and orchestrated using Apache Airflow.

Apache Kafka and airflow combined bring a lot of resilience to the table. Airflow is extensible, elegant, dynamic and highly configurable, on the other hand kafka is a low latency, high throughput, distributed and highly available platform. Both technologies are production ready, and can even be used for mission critical workloads.

 

Lets start automating your data pipelines with Apache Airflow

Apache Kafka vs Apache Airflow – What are the differences? What are they?

A comprehensive overview of both technologies and their integration use cases.

Keeping your ML model in shape with Kafka, Airflow and MLFlow

How to incrementally update your ML model in an automated way as new training data becomes available.

Leave a Reply

Your email address will not be published. Required fields are marked *