Your ecommerce website captures user clickstream data to analyze customer traffic patterns in real time and support personalization features on your website. You plan to analyze this data using big…

Question

Options

• AWrite your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
• BDeploy a Cloud SQL environment with read replicas for improved performance. Use Datastream
• CUse Memorystore to handle your low-latency requirements and for real-time analytics.
• DStream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large

• A
  77% (40)
• B
  13% (7)
• C
  2% (1)
• D
  8% (4)

Explanation

Bigtable is purpose-built for exactly this workload: low-latency (single-digit milliseconds), massive write/read throughput (millions of requests per second), and petabyte-scale storage. Its row-key-based design is ideal for clickstream data keyed by user or session ID. Dataproc with Apache HBase libraries provides the big data analysis capability on top of Bigtable. Option B (Cloud SQL with read replicas) cannot scale to millions of requests per second. Option C (Memorystore/Redis) is an in-memory cache, not a durable storage solution for 8 TB of data. Option D (BigQuery) is optimized for analytical queries, not for low-latency real-time ingestion at millions of writes per second.

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