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Answer: Isolate the write and batch workloads by adding a second cluster to the Bigtable instance and create two app profiles, one for write traffic and one for batch jobs.
To optimize Bigtable's performance by isolating batch analytics jobs from other operations, Google Cloud recommends using two clusters within a single instance and employing app profiles to direct operations to the appropriate cluster. Alternatives like Cloud Spanner and Cloud Firestore are not suited for data warehousing purposes. Adding a second set of tables does not alleviate the workload on the cluster's nodes as they are managed by the same set of nodes. For more details, refer to [Google Cloud's Bigtable documentation](https://cloud.google.com/bigtable/docs/performance).
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A financial services company utilizes a single Bigtable cluster for storing equity price data, experiencing high write operations during trading hours alongside analytic batch jobs running throughout the day. As a Google Professional Data Engineer, what optimization strategy would you recommend for Bigtable's performance?
A
Continue to write data to Bigtable but create a Cloud Dataflow job to copy data to a Cloud Firestore data warehouse for batch operations.
B
Isolate the write and batch workloads by adding a second cluster to the Bigtable instance and create two app profiles, one for write traffic and one for batch jobs.
C
Continue to write data to Bigtable but create a Cloud Dataflow job to copy data to a Cloud Spanner data warehouse for batch operations.
D
Isolate the write and batch workloads by adding a second set of tables to the Bigtable instance and write the data needed by batch jobs to the second set of tables.