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Answer: Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
The question describes a scenario requiring a low-latency solution capable of handling massive scale in terms of both data volume and request throughput for real-time analytics. Option A suggests using Bigtable, which is Google Cloud's fully managed, scalable NoSQL database service designed for low-latency and high-throughput workloads, making it ideal for the described use case. Dataproc, a managed Spark and Hadoop service, can be used with Bigtable for analysis, leveraging the Apache HBase libraries for direct access. Option B proposes Cloud SQL, which is a managed relational database service not suited for the scale and type of workload described. Option C suggests Memorystore, a managed Redis service, which is great for low-latency caching but not for storing and analyzing 8 TB of clickstream data. Option D recommends BigQuery, which is a serverless, highly scalable data warehouse optimized for analytics but not designed for the low-latency, high-throughput write requirements of real-time clickstream data analysis. Therefore, the correct answer is A.
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How can you design a low-latency solution to store and analyze 8 TB of ecommerce clickstream data in real time, supporting millions of read and write operations per second for customer traffic analysis and website personalization?
A
Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
B
Deploy a Cloud SQL environment with read replicas for improved performance. Use Datastream to export data to Cloud Storage and analyze with Dataproc and the Cloud Storage connector.
C
Use Memorystore to handle your low-latency requirements and for real-time analytics.
D
Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data.