Yoda Response: Describe a project with real-time data processing, I shall. At Onclusive, responsible for handling real-time data, I was. Challenges faced, many we did.

For instance, processing data from Kinesis in near real-time, a task it was. Ensuring timely processing and maintaining performance, crucial it was. Concurrency of AWS Lambda functions, we optimized. Batching data, we did, to enhance efficiency. For example, process 25 articles in a single Lambda, we would. This approach, reduced the overhead of spinning up multiple Lambdas, it did.

Another challenge, sending processed data to Elasticsearch and Kafka, we faced. Logs for each article, we generated—one at the start and one at the end. Batching these logs, we did, and pushed them to Kafka. By sending logs in batches of 25, efficient and timely, the process became. This method, reduced connection times and ensured successful log pushes.

For Elasticsearch, bulk pushing records, we employed. Instead of updating records one by one, in bulk, we pushed them. This approach, reduced connection times and repetitive operations, it did.

Thus, by optimizing concurrency, batching data, and bulk operations, the challenges of real-time data processing, we overcame. Efficient and reliable, our system became.