Google Updated Professional-Data-Engineer Exam Questions and Answers by iman

BigQuery Omni enables you to run BigQuery analytics directly on data stored in AWS S3 buckets without having to move or copy the data. This provides several benefits:

Reduced Data Movement Costs:Eliminates the need to egress data from AWS, potentially saving significant costs.

Real-Time Analytics:Allows you to query data in AWS S3 in real-time, providing up-to-date insights.

Simplified Architecture:Reduces the complexity of managing data pipelines and ETL processes.

Here's a breakdown of the steps involved in using BigQuery Omni:

Set up a BigQuery Omni connection:This involves configuring the connection between your Google Cloud project and your AWS S3 bucket. This connection establishes the secure link for BigQuery to access the data in AWS S3.

Create external tables:BigQuery external tables are a way to query data residing in external storage systems, such as AWS S3, without having to import the data into BigQuery. This enables you to directly query the data in its original location.

Query the data using BigQuery:Once the external tables are created, you can use standard SQL queries to analyze the data stored in both Cloud Storage and AWS S3, just as if it were native BigQuery data.

Why other options are not suitable:

Option A:BigLake tables are designed for storing large volumes of structured data within BigQuery itself, not for directly querying data in external storage systems.

Option C and D:While the Storage Transfer Service is useful for moving data between cloud providers, it introduces unnecessary data movement and latency if the goal is to simply query the data in its original location.

Key Points:

BigQuery Omni extends BigQuery's capabilities to analyze data stored in other cloud providers, such as AWS.

External tables provide a way to query data in external storage systems without having to import it into BigQuery.

By leveraging BigQuery Omni and external tables, you can efficiently and cost-effectively query data stored in multiple cloud environments using a single tool, BigQuery.

Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.

To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here’s why:

ELT Development Technique:

ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.

BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.

Dataform:

Dataformis a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.

It provides tools for managing SQL as code, including version control and collaborative development.

Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.

Intuitive Coding Environment:

Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.

It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.

Managing SQL as Code:

Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.

This allows for better collaboration, code reviews, and version tracking.

Reference Links:

Dataform Documentation

BigQuery Documentation

Managing ELT Pipelines with Dataform