Walkthrough a complex pipeline I shall, worked on many pipelines, I have put one particular intricate it is, consider a pipeline for pooling, transforming, enhancing and pushing data to elastic search. This pipeline running on AWS ECS, pools data at interval, pushing 500 articles to Kinesis. From Kinesis, the lambda function process and transform. This data different formats, the data must be transformed into a source provided varied formats. First segregate useful data, we do validate and map fields transforming data that is needed then enhance the data, a second lambda triggered by events, adds publication, license and ownership detail. Pull additional data such as author details from DynamoDB we do, finally push the enhanced data to elastic search, we do, parallel this data undergoes calculation, generating queries from articles. Either complex pipeline involves Apache, Beam, Kafka, source data pushed to a Kafka topic is consumed by parallel executors. Data is processed in batches, transformed and enhanced within the pipeline. Successful data pushed to a different Kafka topic, it is failures, sent to a dead letter queue. They are an application using Apache, Bing and BIM, pulls this process data and indexes it in elastic search, ensuring its scalability and maintainability. These pipelines are by using batching, parallel processing, robust handling, thus complex and efficient our data pipelines are.
