Which Google Cloud services should a team choose to create a real-time pipeline that ingests events at scale, buffers them, and then performs streaming transformations before loading the results into BigQuery?
BigQuery Data Transfer Service for ingestion and Cloud Composer to transform data
Cloud Storage for ingestion and Dataproc for streaming transformations
Pub/Sub for ingestion and buffering, and Dataflow for streaming transformations
Firestore for ingestion and Looker Studio for data processing
Pub/Sub is designed for high-throughput, low-latency ingestion of event data and acts as a durable buffer between producers and downstream systems. Dataflow is Google Cloud's fully managed service for executing Apache Beam pipelines and supports stateful, exactly-once stream processing, making it ideal for applying real-time transformations before writing the enriched data to BigQuery. The other options either focus on batch loading, orchestration, or analytic visualization and do not combine scalable event ingestion with managed streaming transformations.
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What is Google Cloud Pub/Sub used for?
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How does BigQuery integrate with Dataflow in real-time pipelines?
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What is Pub/Sub and how does it handle high-throughput event ingestion?
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How does Dataflow enable streaming transformations?
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Why is BigQuery a common destination for transformed data?
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