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A data engineer needs to ingest streaming data from an IoT fleet into Amazon S3 for near-real-time analytics. The data volume is approximately 5 GB per hour, and each event is less than 1 KB. Which AWS service should be used as the ingestion endpoint?
AWS IoT Core is purpose-built for ingesting data from IoT devices, supporting MQTT, HTTP, and WebSocket protocols. It can handle millions of devices and high-throughput, small-message payloads (each event <1 KB) and integrates directly with Amazon S3 via IoT Core rules, making it…Read full explanation
A company uses AWS Glue ETL jobs to transform data from Amazon S3 to Amazon Redshift. The job reads JSON files, applies schema mapping, and writes to a Redshift table. Recently, the job started failing with memory errors. The data volume has increased tenfold. Which approach should a data engineer take to resolve this issue with minimal code changes?
Option C is correct because increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job directly addresses the memory constraint caused by a tenfold increase in data volume. Glue ETL jobs run on Apache Spark, which distributes data processing across execut…Read full explanation
A financial services company processes real-time stock trade data. They use Amazon Kinesis Data Streams with a shard count of 5, each shard receiving about 500 records per second. The consumer application uses the Kinesis Client Library (KCL) with DynamoDB for checkpointing. Lately, some records are being processed multiple times. What is the most likely cause?
The Kinesis Client Library (KCL) uses DynamoDB to track checkpoint progress for each shard. If the consumer application crashes and restarts, the KCL will resume processing from the last committed checkpoint, which may be behind the actual processing point. This causes records th…Read full explanation
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