Sure Pass Exam Professional-Data-Engineer PDF

 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 deploy a batch pipeline in Dataflow that adheres to the organizational constraint of using only internal IP addresses, ensuring Private Google Access is the most effective solution. Here’s why option D is the best choice:

Private Google Access:

Private Google Accessallows resources in a VPC network that do not have external IP addresses to access Google APIs and services through internal IP addresses.

This ensures compliance with the organizational constraint of using only internal IPs while allowing Dataflow to access Cloud Storage and BigQuery.

Dataflow with Internal IPs:

Dataflow can be configured to use only internal IP addresses for its worker nodes, ensuring that no external IP addresses are assigned.

This configuration ensures secure and compliant communication between Dataflow, Cloud Storage, and BigQuery.

Firewall and Network Configuration:

Enabling Private Google Access requires ensuring the correct firewall rules and network configurations to allow internal traffic to Google Cloud services.

Steps to Implement:

Enable Private Google Access:

Enable Private Google Access on the subnetwork used by the Dataflow pipeline

gcloud compute networks subnets update [SUBNET_NAME] \

--region [REGION] \

--enable-private-ip-google-access

Configure Dataflow:

Configure the Dataflow job to use only internal IP addresses

gcloud dataflow jobs run [JOB_NAME] \

--region [REGION] \

--network [VPC_NETWORK] \

--subnetwork [SUBNETWORK] \

--no-use-public-ips

Verify Access:

Ensure that firewall rules allow the necessary traffic from the Dataflow workers to Cloud Storage and BigQuery using internal IPs.

Reference Links:

Private Google Access Documentation

Configuring Dataflow to Use Internal IPs

VPC Firewall Rules