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You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
Cloud Data Fusion:
Cloud Data Fusion is a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user-friendly GUI for pipeline creation and management.
Custom Encryption Keys:
Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
Centralizing Data:
Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
Set Up Cloud Data Fusion:
Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
Create ETL Pipelines:
Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage. Configure the pipelines to use your custom encryption keys.
Run and Monitor Pipelines:
Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Cloud Data Fusion Documentation
Using Customer-Managed Encryption Keys (CMEK)
You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?
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.
Data retention with time travel and fail-safe | BigQuery | Google Cloud
BigQuery Time Travel: How to access Historical Data? | Easy Steps
You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?
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:
Dataform is 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.
Dataform Documentation
BigQuery Documentation
Managing ELT Pipelines with Dataform
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?
