A transportation company wants to track vehicle movements by capturing geolocation records.

Problem Analysis:

The company ingests geolocation records (10 bytes each) at 10,000 records per second intoKinesis Data Streams.

Data transmission delays are acceptable, but the solution must maximize throughput efficiency.

Key Considerations:

TheKinesis Producer Library (KPL)batches records and uses aggregation to optimize shard throughput.

Efficiently handles high-throughput scenarios with minimal operational overhead.

Solution Analysis:

Option A: Kinesis Agent

Designed for file-based ingestion; not optimized for geolocation records.

Option B: KPL

Aggregates records into larger payloads, significantly improving shard throughput.

Suitable for applications generating small, high-frequency records.

Option C: Kinesis Firehose

Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.

Option D: Kinesis SDK

The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.

Final Recommendation:

UseKinesis Producer Library (KPL)for its built-in aggregation and batching capabilities.