Pass the Google Cloud Certified Professional-Data-Engineer Questions and answers with Dumpstech

BigLake is a Google Cloud service that allows you to query structured data in external data stores such as Cloud Storage, Amazon S3, and Azure Blob Storage with access delegation and governance. BigLake tables extend the capabilities of BigQuery to data lakes and enable a flexible, open lakehouse architecture. By upgrading an external table to a BigLake table, you can improve the performance of your queries by leveraging the BigQuery storage API, which supports data format conversion, predicate pushdown, column projection, and metadata caching. Metadata caching reduces the number of requests to the external data store and speeds up query execution. To upgrade an external table to a BigLake table, you can use the ALTER TABLE statement with the SET OPTIONS clause and specify the enable_metadata_caching option as true. For example:

SQL

ALTERTABLEhive_partitioned_data

SETOPTIONS (

enable_metadata_caching=true

);

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https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml

To resolve issues related to increased memory usage and worker pod evictions in your Cloud Composer 2 environment, the following steps are recommended:

Increase Memory Available to Airflow Workers:

By increasing the memory allocated to Airflow workers, you can handle more memory-intensive tasks, reducing the likelihood of pod evictions due to memory limits.

Increase Maximum Number of Workers and Reduce Worker Concurrency:

Increasing the number of workers allows the workload to be distributed across more pods, preventing any single pod from becoming overwhelmed.

Reducing worker concurrency limits the number of tasks that each worker can handle simultaneously, thereby lowering the memory consumption per worker.

Steps to Implement:

Increase Worker Memory:

Modify the configuration settings in Cloud Composer to allocate more memory to Airflow workers. This can be done through the environment configuration settings.

Adjust Worker and Concurrency Settings:

Increase the maximum number of workers in the Cloud Composer environment settings.

Reduce the concurrency setting for Airflow workers to ensure that each worker handles fewer tasks at a time, thus consuming less memory per worker.

Reference Links:

Cloud Composer Worker Configuration

Scaling Airflow Workers

To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here’s why option A is the best choice:

Materialized Views:

Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.

They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.

Efficiency and Cost Reduction:

By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.

Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.

Minimized Maintenance:

Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.

Steps to Implement:

Identify Aggregation Queries:

Analyze the existing queries to identify common aggregation patterns at the day and month levels.

Create Materialized Views:

Create materialized views in BigQuery for the identified aggregation patterns. For example

CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS

SELECT

DATE(transaction_time) AS day,

SUM(amount) AS total_sales

FROM

project.dataset.sales

GROUP BY

day;

CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS

SELECT

EXTRACT(YEAR FROM transaction_time) AS year,

EXTRACT(MONTH FROM transaction_time) AS month,

SUM(amount) AS total_sales

FROM

project.dataset.sales

GROUP BY

year, month;

Query Using Materialized Views:

Update existing queries to use the materialized views instead of directly querying the base table.

Reference Links:

BigQuery Materialized Views

Optimizing Query Performance

 Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency. BigQuery leverages precomputed results from materialized views and whenever possible reads only changes from the base tables to compute up-to-date results. Materialized views can significantly improve the performance of workloads that have the characteristic of common and repeated queries. Materialized views can also optimize queries with high computation cost and small dataset results, such as filtering and aggregating large tables. Materialized views are refreshed automatically when the base tables change, so they always return fresh data. Materialized views can also be used by the BigQuery optimizer to process queries to the base tables, if any part of the query can be resolved by querying the materialized view. References:

Introduction to materialized views

Create materialized views

BigQuery Materialized View Simplified: Steps to Createand 3 Best Practices

Materialized view in Bigquery

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 Fusionis 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.

Reference Links:

Cloud Data Fusion Documentation

Using Customer-Managed Encryption Keys (CMEK)