Your company plans to migrate a multi-petabyte data set to the cloud.
The data set must be available 24hrs a day.
Your business analysts have experience only with using a SQL interface.
How should you store the data to optimize it for ease of analysis?
Click on the arrows to vote for the correct answer
A. B. C. D.A.
BigQuery is Google's serverless, highly scalable, low cost enterprise data warehouse designed to make all your data analysts productive.
Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL and you don't need a database administrator.
BigQuery enables you to analyze all your data by creating a logical data warehouse over managed, columnar storage as well as data from object storage, and spreadsheets.
https://cloud.google.com/bigquery/Given the scenario, where the company needs to migrate a multi-petabyte dataset to the cloud, which should be available 24x7 and the business analysts have experience only with a SQL interface, we need to choose the best option to store data that optimizes ease of analysis.
Option A: Load data into Google BigQuery Google BigQuery is a fully-managed, serverless data warehouse that provides a scalable, high-performance, and cost-effective solution to store and analyze large datasets. BigQuery supports standard SQL queries, which makes it an ideal choice for business analysts with SQL experience. With BigQuery, you can load data from various sources, including CSV, JSON, Avro, and Parquet files, into tables, which can be queried using standard SQL. BigQuery is designed to handle large datasets, and the storage and processing are decoupled, making it easy to scale storage and compute independently. Moreover, BigQuery is highly available and durable, with built-in redundancy and disaster recovery options.
Option B: Insert data into Google Cloud SQL Google Cloud SQL is a fully-managed relational database service that provides a MySQL or PostgreSQL database in the cloud. It is a good choice if your data is already in a structured format and you want to use SQL to interact with it. However, since the data is stored in a relational database, it may not be the most efficient solution for large datasets. Cloud SQL can scale up to a certain extent, but it may not be able to handle multi-petabyte datasets efficiently. Additionally, Cloud SQL may not be the most cost-effective solution for storing large datasets.
Option C: Put flat files into Google Cloud Storage Google Cloud Storage is a fully-managed object storage service that provides a scalable and cost-effective way to store and access large datasets. You can store flat files, such as CSV or JSON, in Cloud Storage, which can then be analyzed using SQL or other tools. However, Cloud Storage is not a relational database and does not provide built-in query capabilities like BigQuery or Cloud SQL. As a result, it may not be the most efficient solution for business analysts with SQL experience.
Option D: Stream data into Google Cloud Datastore Google Cloud Datastore is a NoSQL document database service that provides a highly scalable, fully-managed database for storing non-relational data. It is a good choice if your data is unstructured, such as JSON or XML, and you need to access it quickly. However, it may not be the most efficient solution for business analysts who are used to working with SQL.
Therefore, among the options given, Option A - Load data into Google BigQuery, would be the best option for storing the data to optimize it for ease of analysis, given the scenario.