Yoda Response: Describe a project, I will, where responsible for data ingestion and integration, I was. 

With APIs, for instance, integrating data from news sources like Times of India, we did. Every 15 seconds, hitting their API, we would, to fetch 500 articles. This data, we pushed to Kafka for further processing. Once in Kafka, Apache Beam pipelines consumed and transformed the data, enhancing it before pushing to Elasticsearch. From Elasticsearch, APIs served this data to various clients based on their queries.

For IoT devices, a similar approach we took. IoT devices, such as connected bikes or cars, pushed data directly to Kafka. Thousands of devices, each with different data types, created specific Kafka topics. Our executors processed this data based on the topic, ensuring the right transformations and enhancements were applied before storing or further processing.

Thus, a seamless integration of data from APIs and IoT devices into our downstream systems, we achieved. Efficient and scalable, our approach was.