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This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. References:

Exporting table data | BigQuery | Google Cloud

Storage classes | Cloud Storage | Google Cloud

Retention policies and retention periods | Cloud Storage | Google Cloud

Federated queries | BigQuery | Google Cloud

To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here’s why option B is the best choice:

Dual-Region Bucket:

A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures.

The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States.

Turbo Replication:

Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes.

This minimizes data loss in case of a regional failure.

Running Dataproc Cluster:

Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations.

In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss.

Steps to Implement:

Create a Dual-Region Bucket:

Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions.

Enable turbo replication to ensure rapid data replication between the regions.

Deploy Dataproc Cluster:

Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance.

Set Up Failover Plan:

Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us-central1 region.

Ensure that the failover process is well-documented and tested to minimize downtime and data loss.

Reference Links:

Google Cloud Storage Dual-Region

Turbo Replication in Google Cloud Storage

Dataproc Documentation

Watermarks are a way of tracking the progress of time in a streaming pipeline. They are used to determine when a window can be closed and the results emitted. Watermarks can be either event-time based or processing-time based. Event-time watermarks track the progress of time based on the timestamps of the data elements, while processing-time watermarks track the progress of time based on the system clock. Event-time watermarks are more accurate, but they require the data source to provide reliable timestamps. Processing-time watermarks are simpler, but they can be affected by system delays or backlogs.

By using watermarks, you can define the expected data arrival window for each windowing function. You can also specify how to handle late data, which is data that arrives after the watermark has passed. You can either discard late data, or allow late data and update the results as new data arrives. Allowing late data requires you to use triggers to control when the results are emitted.

In this case, using watermarks and allowing late data is the best solution to capture the late data in the appropriate window. Changing the windowing function to session windows or tumbling windows will not solve the problem of late data, as they still rely on watermarks to determine when to close the windows. Expanding the hopping window might reduce the amount of late data, but it will also change the semantics of the windowing function and the results.

For a farming company with a sensor data table updated every 30 seconds, the goal is to minimize costs while facilitating weekly analytical queries. The best data model will effectively manage data storage, update frequency, and query performance.

Partitioned Metrics Table:

Creating a metrics table partitioned by timestamp optimizes query performance and storage costs.

Partitioning by timestamp allows for efficient querying, especially for time-based analyses.

Sensor ID Reference:

Including a sensor_id column in the metrics table that points to the id column in the sensors table ensures data normalization.

This structure avoids redundancy and maintains a clear relationship between sensors and their metrics.

Using INSERT Statements:

Using INSERT statements to append new metrics every 30 seconds is efficient and cost-effective.

INSERT operations are more suitable than UPDATE operations for adding new data entries, especially at high frequencies.

Joining Tables for Analysis:

When running analytical queries, joining the partitioned metrics table with the sensors table as needed provides a comprehensive view of the data.

This approach leverages BigQuery's powerful JOIN capabilities while keeping the data model normalized and efficient.

Google Data Engineer References:

BigQuery Partitioned Tables

BigQuery Best Practices

Efficient Data Partitioning

BigQuery Data Modeling

Using this data model, the farming company can manage its sensor data effectively, minimize costs, and perform weekly analytical queries with high efficiency.

A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired. Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. References:

Materialized views, ML models in data warehouse - Google Cloud

Data Engineering with Google Cloud Platform - Packt Subscription

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