Exam Professional Cloud Database Engineer topic 1 question 14 discussion

A. Cloud SQL could not handle the load, so B is wrong. Memorystore can scale up to 300 GB. The question mentions needing 8 TB, so C must be wrong. BigQuery could not handle the latency requirements of the question, which leaves A. Bigtable could handle the volume of writes at the speeds required.

A: Write your data into Bigtable ***** and use Dataproc and the Apache Hbase libraries for analysis.

Bigtable is ideal for clickstream and IOT use cases, also it can process high performance read and writes globally.

This option uses BigQuery, that has a low latency and is a big data

A is not correct because Bigtable is not designed for real-time analytics. It is a good choice for storing and retrieving small amounts of data quickly, but it is not as efficient for analyzing large volumes of data. B is not correct because it cannot support Million of Read and Write C is not correct because of storage limitation D is correct

I would opt for A.

The correct answer is D. Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data.. A is used for NOSQL

At a high level, Bigtable is a NoSQL wide-column database. It's optimized for low latency, large numbers of reads and writes, and maintaining performance at scale. Bigtable use cases are of a certain scale or throughput with strict latency requirements, such as IoT, AdTech, FinTech, and so on. If high throughput and low latency at scale are not priorities for you, then another NoSQL database like Firestore might be a better fit.

B. Normalize the data model. D. Promote high-cardinality attributes in multi-attribute primary keys. When designing a schema for Cloud Spanner, it is important to follow best practices to avoid hotspots and ensure optimal performance. Hotspots occur when too many requests are targeted at a single node or group of nodes, causing them to become overloaded and potentially impacting performance.

D. Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data. BigQuery is a fully managed, serverless data warehouse that allows you to store and analyze large datasets using SQL-like queries. It is designed to handle petabyte-scale data and is optimized for fast query performance. By streaming your clickstream data into BigQuery, you can store and process large amounts of data in real-time. Dataproc, on the other hand, is a fully-managed cloud service for running Apache Hadoop and Spark clusters. It provides a managed, easy-to-use environment for data processing, which can be used to analyze the data stored in BigQuery. The BigQuery Storage API allows you to directly access data stored in BigQuery from external applications, including Dataproc, which enables you to run advanced analytics on large volumes of data with low latency. This approach provides a scalable, low-latency solution for storing and analyzing large volumes of data, making it a good fit for your requirements.

A. Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis. Most Voted

A looks like best option.

A is correct answer, C wouldn't be handled 8TB data Scalable: Start with the lowest tier and smallest size and then grow your instance as needed. Memorystore provides automated scaling using APIs, and optimized node placement across zones for redundancy. Memorystore for Memcached can support clusters as large as 5 TB, enabling millions of QPS at very low latency

Answer is A. Click stream and time series data and the size is 8TB. Read low latency with reads and writes. Correct answer is A to use BigTable for storage and use either CBT or Hbase API to interact with data.

B couldn't handle this volume of writes and read, D wouldn't be able to handle the writing and C wouldn't be suited for this.

must be A