Yoda Response: Develop data pipelines, I do. Pull data from various APIs using a Python script on Amazon ECS, we do. Poller runs, hits the API, retrieves data, and pushes it to Amazon Kinesis. Trigger Lambda, we do, to transform and store data in Elasticsearch. 

Challenges faced, optimizing performance, we did. Initially, processing 25 articles took 300-350 milliseconds. Adding Kafka push, increased to 2.5 seconds, it did. Not acceptable for large-scale data, this was. 

Optimize, we did, by batching logs. Instead of pushing logs one by one, collect them in a list, we did. Push all logs in a batch, we do. Reduced processing time to 400 milliseconds, we have. Reliable and efficient, our pipeline is now.

New pipeline with Kafka and Apache Beam, building we are. Source directly posts data to Kafka topic, it does. Consumer fetches data, transforms, and pushes to Elasticsearch. Faster and with less network latency, it will be. Maintain and improve our pipelines, we do.