Category: Data Pipelines

  • How to Generate Python Code from Protobuf

    Protocol Buffers, which are protobuf for short, is much compact than XML and JSON and hence a great choice for designing efficient inter-service communication. In this post we will see how to generate Python code from protobuf code.

  • Application Log Processing and Visualization with Grafana and Prometheus

    Integrating Prometheus and Grafana provides a powerful solution for application log processing and visualization. By following this technical guide, you can establish a robust monitoring and observability pipeline, allowing you to gain deep insights into your application’s performance and respond proactively to issues. Adjust configurations based on your application’s needs and continuously optimize for a…

  • Understanding Data Serialization in Apache Kafka

    In the realm of distributed streaming and messaging systems, effective data serialization is a cornerstone for ensuring the seamless and efficient exchange of data. Apache Kafka’s support for various serialization formats, with a notable mention to Avro, empowers organizations to build robust, scalable, and future-proof data pipelines. By understanding the significance of data serialization in…

  • Optimizing Apache Airflow Performance: Configuring Worker Count for Maximum Efficiency

    Airflow workers are responsible for executing the tasks defined in Directed Acyclic Graphs (DAGs). Each worker can handle one task at a time. The number of workers directly impacts the system’s ability to parallelize task execution, thus influencing overall workflow performance.

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