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When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts

For example, if you plan to store extensive historical data for a large number of remote-sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage—reads would be much more frequent in this case, and reads are much slower with HDD storage.

In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.

Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail.

It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.

Dataplex is Google Cloud's intelligent data fabric that enables organizations to centrally manage, monitor, and govern data across data lakes, data warehouses, and data marts. It is specifically designed to provide a unified interface for distributed data without moving the data.

Unified Governance: Dataplex allows you to group distributed data (in GCS and BigQuery) into logical Lakes and Zones (e.g., Raw vs. Curated). This structure enables you to apply security policies once at the Lake or Zone level, which then propagates to all underlying assets.

Metadata Discovery: Dataplex automatically scans and registers metadata from your buckets and datasets into the Search/Catalog interface, making it discoverable without manual scripts.

Data Quality & Profiling: Dataplex includes built-in, fully managed features for Data Quality (declarative rules) and Data Profiling to monitor the health of your data directly within the governance framework.

Correcting other options:

A & B: These involve significant manual overhead, custom scripts, and fragmented tools. They do not provide a "unified data governance framework" that scales naturally across both GCS and BigQuery.

C: Dataproc Metastore is primarily for Hive/Spark workloads and doesn't offer the comprehensive security and data quality governance required for a general-purpose federated framework across BigQuery and Cloud Storage.

The key requirements are:

Migrate Spark 3 batch job.

Minimally change the job (reads from GCS, writes to BQ – standard for Spark on GCP).

Job optimized for Spark (specific executor vCPU/memory).

Ability to choose similar executor settings.

Minimize installation and management effort.

Dataproc Serverless (Option A)is designed for these use cases.

Spark 3 Support:Dataproc Serverless supports various Spark runtimes, including Spark 3.

Minimal Changes:Spark jobs reading from GCS and writing to BigQuery (using the Spark-BigQuery connector) are standard. Minimal code changes are generally needed.

Customizable Resources:Dataproc Serverless allows you to specify resources for the driver and executors, including vCPU and memory. You can configure these to match your optimized on-premises settings (e.g., 8 vCPU, 16 GB memory per executor, though specific available configurations should be checked).

Minimal Installation and Management:This is the core benefit of "serverless." You don't need to provision, manage, or scale clusters. You submit your batch job, and Google Cloud handles the underlying infrastructure. This significantly reduces operational overhead.

Let's analyze why other options are less suitable:

B (Compute Engine VM):You would need to manually install Spark, configure it, manage dependencies, and manage the VM itself. This is high management effort.

C (Google Kubernetes Engine cluster):While you can run Spark on GKE (e.g., using Spark on Kubernetes operator), it involves managing the GKE cluster, Spark deployment configurations, Docker images, etc. This is also significant management effort, more than Dataproc Serverless.

D (Dataproc cluster):A standard Dataproc cluster provides more control than serverless but also requires more management (creating, scaling, and managing the cluster lifecycle). Dataproc Serverless is specifically designed to minimize this management for batch jobs. Given the "minimize installation and management effort" requirement, serverless is preferred over a managed cluster if it meets the job's needs.

Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure. The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep. References:

Dataprep overview

Dataprep features

Dataprep and BigQuery integration

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

Datastream is a serverless Change Data Capture (CDC) and replication service that allows you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud Storage, Cloud SQL, and Pub/Sub. Datastream captures and delivers database changes in real-time, with minimal impact on the source database performance. Datastream also preserves the schema and data types of the source database, and automatically creates and updates the corresponding tables in BigQuery.

By using Datastream, you can replicate the required tables from both Cloud SQL databases to BigQuery, and keep them in sync with the source databases. This way, you can reduce the load on the Cloud SQL databases, as the marketing team can run their queries on the BigQuery tables instead of the Cloud SQL tables. You can also leverage the scalability and performance of BigQuery to query the customer behavioral data from Google Analytics and the customer information from the replicated tables. You can run the queries as frequently as needed, without worrying about the impact on the Cloud SQL databases.

Option A is not a good solution, as BigQuery federated queries allow you to query external data sources such as Cloud SQL databases, but they do not reduce the load on the source databases. In fact, federated queries may increase the load on the source databases, as they need to execute the query statements on the external data sources and return the results to BigQuery. Federated queries also have some limitations, such as data type mappings, quotas, and performance issues.

Option C is not a good solution, as creating a Dataproc cluster with Trino would require more resources and management overhead than using Datastream. Trino is a distributed SQL query engine that can connect to multiple data sources, such as Cloud SQL and BigQuery, and execute queries across them. However, Trino requires a Dataproc cluster to run, which means you need to provision, configure, and monitor the cluster nodes. You also need to install and configure the Trino connector for Cloud SQL and BigQuery, and write the queries in Trino SQL dialect. Moreover, Trino does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high.

Option D is not a good solution, as creating a job on Apache Spark with Dataproc Serverless would require more coding and processing power than using Datastream. Apache Spark is a distributed data processing framework that can read and write data from various sources, such as Cloud SQL and BigQuery, and perform complex transformations and analytics on them. Dataproc Serverless is a serverless Spark service that allows you to run Spark jobs without managing clusters. However, Spark requires you to write code in Python, Scala, Java, or R, and use the Spark connector for Cloud SQL and BigQuery to access the data sources. Spark also does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high. References: Datastream overview | Datastream | Google Cloud, Datastream concepts | Datastream | Google Cloud, Datastreamquickstart | Datastream | Google Cloud, Introduction to federated queries | BigQuery | Google Cloud, Trino overview | Dataproc Documentation | Google Cloud, Dataproc Serverless overview | Dataproc Documentation | Google Cloud, Apache Spark overview | Dataproc Documentation | Google Cloud.