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Google Cloud Exams
GCP
Associate Data Practitioner
Extract and load data into appropriate Google Cloud storage systems
Dataflow ETL: Stage, Transform, Load to BigQuery

Diagram

Dataflow ETL: Stage, Transform, Load to BigQuery

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A five-step left-to-right process flow from existing warehouse to on-premises staging, Cloud Storage bucket, Dataflow Beam transformation, and load into BigQuery.

In the Domain

Dataflow for Custom Batch and Streaming Transformation

A common extraction and load pattern moves data through an ordered sequence:

Stage in Cloud Storage: Extracted files are copied from temporary on-premises storage to a Cloud Storage staging bucket.
Process in Dataflow: Data flows from the staging bucket into the Dataflow pipeline, where Beam transforms and enriches each record.
Load to destination: Data flows from Dataflow to the destination, such as directly to BigQuery through the BigQuery I/O connector.

The pipeline depends on Beam I/O connectors for sources and destinations, including BigQueryIO, BigtableIO, AvroIO, and ParquetIO. Google-provided templates cover both modes, with streaming templates such as Bigtable change streams to BigQuery and batch templates such as Cloud Storage Avro to Bigtable and Cloud Storage Parquet to Bigtable. Choose Dataflow when data must be trimmed, joined, enriched, or reshaped, especially when the same logic must serve both one-time bulk loads and ongoing arrivals.

Read it in the Domain summary

Domain 2 of 2

Extract and load data into appropriate Google Cloud storage systems

Read the Domain summary

Practice this Domain

145 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 2 of 2

Extract and load data into appropriate Google Cloud storage systems

Read the Domain summary

Practice this Domain

145 practice questions

Practice this Domain

Practice questions in this domain

1.A data analytics team is preparing to ingest historical transaction data stored in Cloud Storage into BigQuery. The team requires an open-source, columnar storage format that is natively supported for automated data loading using the BigQuery Data Transfer Service. Which file format should the team use?Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables)
2.A data analytics team is ingesting e-commerce transaction records from Cloud Storage into BigQuery. Each transaction record contains complex hierarchical information, including nested customer contact details and a repeated list of purchased product line items. Which file format should the team use to load this data while natively preserving its nested and repeated data structure?Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables)
3.A data engineering team is designing a data ingestion and storage pipeline in Google Cloud. The architecture must support two primary use cases: Use Case 1: High-throughput streaming ingestion of individual, write-heavy event records where messages require schema evolution and fast row-level serialization. Use Case 2: Large-scale analytical processing where business intelligence queries scan and aggregate only 3 to 4 specific columns across billions of historical records. Which combination of data formats best aligns with the architectural requirements of these two use cases?Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables)
4.A company wants to ingest data regularly from several external Software-as-a-Service (SaaS) applications, such as Salesforce and Google Ads, directly into BigQuery for reporting. The analytics team wants a fully managed, scheduled solution that requires no custom code or complex pipeline development. Which Google Cloud service should the team select to meet these requirements?Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion)
5.A data analytics team needs to ingest reporting data on a recurring daily schedule from SaaS platforms, including Salesforce and Google Ads, directly into BigQuery for analysis. The team has the following requirements: The solution must be fully managed and serverless. Data movement must occur automatically on a regular, predefined schedule. The setup should require no coding or custom pipeline development. Which Google Cloud service should the team use?Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion)

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Home
Google Cloud Exams
GCP
Associate Data Practitioner
Extract and load data into appropriate Google Cloud storage systems
Dataflow ETL: Stage, Transform, Load to BigQuery

Diagram

Dataflow ETL: Stage, Transform, Load to BigQuery

X LinkedIn Facebook Reddit

In the Domain

Dataflow for Custom Batch and Streaming Transformation

A common extraction and load pattern moves data through an ordered sequence:

Stage in Cloud Storage: Extracted files are copied from temporary on-premises storage to a Cloud Storage staging bucket.
Process in Dataflow: Data flows from the staging bucket into the Dataflow pipeline, where Beam transforms and enriches each record.
Load to destination: Data flows from Dataflow to the destination, such as directly to BigQuery through the BigQuery I/O connector.

The pipeline depends on Beam I/O connectors for sources and destinations, including BigQueryIO, BigtableIO, AvroIO, and ParquetIO. Google-provided templates cover both modes, with streaming templates such as Bigtable change streams to BigQuery and batch templates such as Cloud Storage Avro to Bigtable and Cloud Storage Parquet to Bigtable. Choose Dataflow when data must be trimmed, joined, enriched, or reshaped, especially when the same logic must serve both one-time bulk loads and ongoing arrivals.

Read it in the Domain summary

Domain 2 of 2

Extract and load data into appropriate Google Cloud storage systems

Read the Domain summary

Practice this Domain

145 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 2 of 2

Extract and load data into appropriate Google Cloud storage systems

Read the Domain summary

Practice this Domain

145 practice questions

Practice this Domain

Practice questions in this domain

1.A data analytics team is preparing to ingest historical transaction data stored in Cloud Storage into BigQuery. The team requires an open-source, columnar storage format that is natively supported for automated data loading using the BigQuery Data Transfer Service. Which file format should the team use?Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables)
2.A data analytics team is ingesting e-commerce transaction records from Cloud Storage into BigQuery. Each transaction record contains complex hierarchical information, including nested customer contact details and a repeated list of purchased product line items. Which file format should the team use to load this data while natively preserving its nested and repeated data structure?Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables)
3.A data engineering team is designing a data ingestion and storage pipeline in Google Cloud. The architecture must support two primary use cases: Use Case 1: High-throughput streaming ingestion of individual, write-heavy event records where messages require schema evolution and fast row-level serialization. Use Case 2: Large-scale analytical processing where business intelligence queries scan and aggregate only 3 to 4 specific columns across billions of historical records. Which combination of data formats best aligns with the architectural requirements of these two use cases?Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables)
4.A company wants to ingest data regularly from several external Software-as-a-Service (SaaS) applications, such as Salesforce and Google Ads, directly into BigQuery for reporting. The analytics team wants a fully managed, scheduled solution that requires no custom code or complex pipeline development. Which Google Cloud service should the team select to meet these requirements?Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion)
5.A data analytics team needs to ingest reporting data on a recurring daily schedule from SaaS platforms, including Salesforce and Google Ads, directly into BigQuery for analysis. The team has the following requirements: The solution must be fully managed and serverless. Data movement must occur automatically on a regular, predefined schedule. The setup should require no coding or custom pipeline development. Which Google Cloud service should the team use?Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion)