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Top 100 BigQuery Interview Questions and Answers

Top 100 BigQuery Interview Questions and Answers
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Question 1: What is BigQuery and why is it used?

Answer:
Google BigQuery is a serverless, highly scalable data warehouse that is used for analytics. It’s particularly suited for handling large datasets and running complex queries.

Official Reference: BigQuery Overview


Question 2: How can you create a new dataset in BigQuery using SQL?

Answer:

CREATE DATASET `your_project_id.your_dataset_id`;

Explanation:
This SQL command creates a new dataset in your BigQuery project.

Official Reference: Creating Datasets


Question 3: How do you import data into BigQuery from a CSV file?

Answer:

bq load --source_format=CSV dataset.table gs://your_bucket/your_file.csv

Explanation:
This command uses the BigQuery command-line tool to load data from a CSV file into a specified table.

Official Reference: Loading Data from CSV


Question 4: What is a partitioned table in BigQuery?

Answer:
A partitioned table is one that is divided into segments based on the values in one or more columns. This can significantly improve query performance.

Official Reference: Partitioned Tables


Question 5: How can you query nested data in BigQuery?

Answer:

SELECT name, address.city 
FROM `your_project_id.your_dataset_id.your_table_id`;

Explanation:
This SQL query retrieves the name and city from a table that contains nested data.

Official Reference: Querying Nested and Repeated Data


Question 6: What is a UDF in BigQuery?

Answer:
A User-Defined Function (UDF) is a custom function that you can define and use in your BigQuery queries.

Official Reference: User-Defined Functions


Question 7: How can you schedule queries in BigQuery?

Answer:
You can use BigQuery’s scheduling feature or use a tool like Cloud Scheduler to automate the execution of your queries.

Official Reference: Scheduling Queries


Question 8: What is a streaming insert in BigQuery?

Answer:
A streaming insert allows you to append new rows to a table in real-time. It’s useful for handling continuous streams of data.

Official Reference: Streaming Data Into BigQuery


Question 9: How can you export data from BigQuery to a Google Sheets?

Answer:
You can use the BigQuery connector for Google Sheets or use the BigQuery API to export data directly.

Official Reference: Exporting Data to Google Sheets


Question 10: How do you optimize queries in BigQuery?

Answer:
Optimization techniques include using partitioned tables, clustering, using the appropriate join types, and minimizing unnecessary columns in the SELECT statement.

Official Reference: Query Optimization


Certainly, here are more BigQuery interview questions:

Question 11: What is the purpose of clustering in BigQuery?

Answer:
Clustering is used to organize data within a table based on the values in one or more columns. This can significantly improve query performance by reducing the amount of data that needs to be scanned.

Official Reference: Clustering


Question 12: How do you handle data ingestion failures in streaming inserts?

Answer:
You can use error handling functions like INSERT IGNORE or INSERT DML statements with a condition to handle data that doesn’t conform to the schema.

Official Reference: Error Handling in Streaming Inserts


Question 13: What are some best practices for managing costs in BigQuery?

Answer:

  • Use cost controls and budgets in the Cloud Console.
  • Monitor query performance to identify and optimize costly queries.
  • Set expiration times for tables and partitions to manage storage costs.

Official Reference: Controlling Costs


Question 14: What is a materialized view in BigQuery?

Answer:
A materialized view is a precomputed result set stored as a table. It’s useful for improving query performance by reducing the need to compute expensive operations repeatedly.

Official Reference: Materialized Views


Question 15: How do you handle access control in BigQuery?

Answer:
Access control in BigQuery is managed through IAM roles. Assign roles to users or groups to grant permissions for specific tasks like querying, modifying, or administering resources.

Official Reference: Access Control


Question 16: What is the purpose of the BigQuery Data Transfer Service?

Answer:
The Data Transfer Service allows you to automate the movement of data from external sources like Google Ads, YouTube, and more into BigQuery for analysis.

Official Reference: Data Transfer Service


Question 17: How do you optimize data loading performance in BigQuery?

Answer:

  • Use streaming inserts for real-time data.
  • Load data in parallel using load jobs.
  • Use partitioned and clustered tables to reduce the amount of data scanned.

Official Reference: Loading Data into BigQuery


Question 18: What is the difference between a view and a materialized view in BigQuery?

Answer:
A view is a virtual table defined by a query, while a materialized view is a precomputed table. Materialized views store the results of the query, making them faster for repeated queries but potentially slower for data modification operations.

Official Reference: Views vs. Materialized Views


Question 19: How do you troubleshoot slow queries in BigQuery?

Answer:

  • Check for inefficient query patterns.
  • Review query execution details.
  • Use the Query Execution History in the Cloud Console.
  • Analyze the query plan.

Official Reference: Troubleshooting Slow Queries


Question 20: Can you perform machine learning tasks in BigQuery?

Answer:
Yes, BigQuery ML allows you to build and deploy machine learning models directly within BigQuery using SQL.

Official Reference: BigQuery ML


Certainly, here are more BigQuery interview questions:

Question 21: What are the advantages of using partitioned tables in BigQuery?

Answer:
Partitioned tables improve query performance by allowing you to restrict the amount of data scanned to only the partitions needed. This can significantly reduce costs and speed up query execution times.

Official Reference: Partitioned Tables


Question 22: How can you optimize a query in BigQuery?

Answer:
Optimizing a query in BigQuery involves several steps:

  • Use appropriate WHERE clauses to filter data.
  • Avoid using SELECT *; specify only required columns.
  • Use clustering to organize data efficiently.
  • Monitor query execution times and use query plan explanations to identify bottlenecks.

Official Reference: Query Optimization


Question 23: What is the difference between a table and a dataset in BigQuery?

Answer:
A table is where data is stored, while a dataset is a container for one or more tables. Datasets can be used to organize and control access to your tables.

Official Reference: Datasets


Question 24: How does BigQuery handle schema evolution?

Answer:
BigQuery allows for relaxed schema enforcement, which means you can add new columns to a table without affecting existing data. However, you cannot remove or change the type of existing columns.

Official Reference: Schema Evolution


Question 25: What is the purpose of the BigQuery Data Catalog?

Answer:
The Data Catalog is a service that allows you to discover, manage, and understand all your data assets across different Google Cloud services, including BigQuery.

Official Reference: Data Catalog


Question 26: Can you perform JOIN operations in BigQuery?

Answer:
Yes, BigQuery supports various types of joins, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN, allowing you to combine data from multiple tables.

Official Reference: Joining Tables


Question 27: How can you export data from BigQuery?

Answer:
You can export data from BigQuery to various formats, including CSV, JSON, Avro, and more. This can be done using the bq extract command, the Cloud Console, or programmatically using APIs.

Official Reference: Exporting Data


Question 28: What is the purpose of a wildcard table in BigQuery?

Answer:
A wildcard table is a special type of table that allows you to query multiple tables that match a specific pattern using a single query.

Official Reference: Wildcard Tables


Question 29: How do you handle data privacy and compliance in BigQuery?

Answer:
You can use features like Data Loss Prevention (DLP) and Access Controls in BigQuery to ensure data privacy and compliance with regulations like GDPR.

Official Reference: Data Privacy in BigQuery


Question 30: What is the purpose of a streaming buffer in BigQuery?

Answer:
The streaming buffer is a section of a table where recently inserted data is temporarily stored before it’s organized into the table’s structure. This is used for real-time streaming data.

Official Reference: Streaming Buffer


Certainly, let’s continue with more BigQuery interview questions:

Question 31: What is the difference between a clustered table and a partitioned table in BigQuery?

Answer:
Both clustered and partitioned tables improve query performance, but they work differently. Partitioned tables organize data by date or range, while clustered tables use a hierarchical structure based on the values in one or more columns. You can use both features together for optimal performance.

Official Reference: Clustered Tables


Question 32: How can you schedule and automate queries in BigQuery?

Answer:
You can use BigQuery’s Scheduled Queries feature to automate and schedule queries. These scheduled queries can be used for regular data updates, report generation, and more.

Official Reference: Scheduled Queries


Question 33: Explain what a Google BigQuery slot is and how it affects query execution.

Answer:
A slot represents the computational capacity of BigQuery. Query execution consumes slots, and the number of slots available determines how quickly a query can be executed. BigQuery allocates slots dynamically based on your project’s slot capacity.

Official Reference: Query Slots


Question 34: What is the purpose of the BigQuery Reservations feature?

Answer:
Reservations allow you to allocate dedicated resources (slots) to your queries, ensuring consistent and predictable query performance. This feature is useful for enterprises with high query workloads.

Official Reference: Query Reservations


Question 35: How can you secure data in BigQuery?

Answer:
You can secure data in BigQuery using Identity and Access Management (IAM) policies, row-level security, encryption, and data masking. IAM policies control who can access resources, while encryption and data masking protect data at rest and in transit.

Official Reference: Data Security in BigQuery


Question 36: What is BigQuery ML, and how can it be used?

Answer:
BigQuery ML is a machine learning (ML) service that allows you to build and train ML models using SQL queries. You can use it for tasks like classification, regression, forecasting, and more, directly within BigQuery.

Official Reference: BigQuery ML


Question 37: Explain the concept of clustering keys in BigQuery tables.

Answer:
Clustering keys determine how data is physically stored in a clustered table. Rows with similar values in the clustering key columns are stored together, improving query performance by reducing the amount of data that needs to be scanned.

Official Reference: Clustering Tables


Question 38: What is the purpose of the BigQuery GIS (Geographic Information Systems) feature?

Answer:
BigQuery GIS allows you to perform geospatial analysis on geographic data. It supports various functions and types for working with geographic data, making it useful for location-based analysis.

Official Reference: BigQuery GIS


Question 39: How does BigQuery handle data ingestion from external sources?

Answer:
BigQuery supports data ingestion from various external sources, including Cloud Storage, Google Sheets, and streaming data via Pub/Sub. You can use tools like Dataflow and Dataprep to prepare and ingest data.

Official Reference: Loading Data


Question 40: What is the purpose of the BigQuery Data Transfer Service?

Answer:
The Data Transfer Service simplifies data movement from other Google services, third-party sources, and on-premises databases into BigQuery. It provides predefined data connectors for common sources.

Official Reference: Data Transfer Service


Certainly, let’s continue with more BigQuery interview questions:

Question 41: How does BigQuery handle data export?

Answer:
You can export data from BigQuery to various formats, including CSV, JSON, Avro, and more. It also supports exporting data to Google Cloud Storage, which can be used for backup, sharing, or further processing.

Official Reference: Exporting Data


Question 42: What is the purpose of streaming data into BigQuery?

Answer:
Streaming allows you to ingest real-time data into BigQuery. This is useful for scenarios where you need to analyze data as it’s generated, such as IoT applications, live monitoring, or social media analytics.

Official Reference: Streaming Data


Question 43: Can you explain the difference between a standard SQL query and an legacy SQL query in BigQuery?

Answer:
Standard SQL is the recommended query language for BigQuery as it is more powerful and aligns with ANSI SQL standards. Legacy SQL, while still supported, lacks some features and functions available in standard SQL.

Official Reference: SQL Reference


Question 44: What is the purpose of a Data Catalog in BigQuery?

Answer:
The Data Catalog in BigQuery serves as a centralized metadata repository. It allows users to discover, understand, and manage data assets across the organization. It provides a unified view of all your data resources.

Official Reference: Data Catalog Overview


Question 45: How does BigQuery handle nested and repeated data structures?

Answer:
BigQuery supports nested and repeated data, allowing you to model complex data structures. This is useful for scenarios like JSON data where elements can be nested or repeated.

Official Reference: Nested and Repeated Data


Question 46: What is the role of a Data Studio in conjunction with BigQuery?

Answer:
Google Data Studio is a powerful reporting and visualization tool that can be integrated with BigQuery. It allows you to create interactive and shareable dashboards based on data from BigQuery.

Official Reference: Using BigQuery with Data Studio


Question 47: Explain the concept of Materialized Views in BigQuery.

Answer:
Materialized Views in BigQuery are precomputed views of a query. They allow you to store the results of a query and incrementally update it, reducing the need to recompute the same result.

Official Reference: Materialized Views


Question 48: What is the purpose of the BigQuery BI Engine?

Answer:
The BigQuery BI Engine is an in-memory analysis service that allows for sub-second SQL queries on large datasets. It is particularly useful for interactive dashboards and reporting.

Official Reference: BigQuery BI Engine


Question 49: How can you optimize query performance in BigQuery?

Answer:
You can optimize query performance in BigQuery by partitioning tables, clustering data, using Materialized Views, and choosing the appropriate data types. Additionally, writing efficient SQL queries is crucial.

Official Reference: Query Optimization


Question 50: What is the purpose of the BigQuery Data QnA feature?

Answer:
The Data QnA feature in BigQuery allows users to ask questions in natural language and get instant answers from the data. It leverages machine learning to understand and process queries.

Official Reference: Data QnA


Certainly, let’s continue with more BigQuery interview questions:

Question 51: What are the benefits of using partitioned tables in BigQuery?

Answer:
Partitioned tables in BigQuery improve query performance and reduce costs. They allow for more efficient querying by restricting the amount of data scanned, resulting in faster responses. This is particularly useful for time-based data.

Official Reference: Partitioned Tables


Question 52: Explain the purpose of clustering in BigQuery.

Answer:
Clustering in BigQuery helps organize data within partitioned tables based on the values in one or more columns. This improves query performance as it reduces the amount of data scanned for a given query.

Official Reference: Clustering Tables


Question 53: How can you handle and analyze JSON data in BigQuery?

Answer:
BigQuery supports JSON data types, allowing you to directly ingest and query JSON documents. You can use functions like JSON_EXTRACT and JSON_QUERY to work with JSON data.

Official Reference: Working with JSON Data


Question 54: Can you explain the concept of federated queries in BigQuery?

Answer:
Federated queries allow you to query data that is stored in external sources, like Cloud SQL, Cloud Spanner, and more. This extends the capabilities of BigQuery to analyze data beyond its own storage.

Official Reference: Federated Queries


Question 55: What is the purpose of BigQuery ML?

Answer:
BigQuery ML is a machine learning service integrated with BigQuery. It enables data analysts and data scientists to build and operationalize ML models using SQL queries.

Official Reference: BigQuery ML Overview


Question 56: How does BigQuery handle access control and security?

Answer:
BigQuery uses Identity and Access Management (IAM) for access control. You can define roles and permissions to control who can view, query, or modify data and resources within BigQuery.

Official Reference: BigQuery Access Control


Question 57: Explain the concept of slots in BigQuery.

Answer:
In BigQuery, slots represent the computational resources required to execute a query. Every query consumes a certain number of slots, and BigQuery’s pricing is based on slot usage.

Official Reference: Query Pricing


Question 58: What is the role of Dataflow in conjunction with BigQuery?

Answer:
Google Cloud Dataflow is a data processing service that can be integrated with BigQuery. It allows for real-time stream and batch data processing before it is loaded into BigQuery.

Official Reference: Using Dataflow with BigQuery


Question 59: How does BigQuery handle data encryption?

Answer:
BigQuery encrypts data at rest by default. Additionally, you can use Customer-Managed Encryption Keys (CMEK) for an extra layer of security.

Official Reference: Data Encryption


Question 60: Can you explain the concept of billing and cost management in BigQuery?

Answer:
BigQuery offers flexible pricing options based on storage, streaming, and query costs. You can also set budget alerts and analyze usage to manage costs effectively.

Official Reference: BigQuery Pricing


Certainly, let’s continue with more BigQuery interview questions:

Question 61: What is the purpose of a view in BigQuery?

Answer:
A view in BigQuery is a saved SQL query that behaves like a table. It doesn’t store data itself but provides a way to organize and simplify complex queries, making them easier to manage and reuse.

Official Reference: Views


Question 62: Explain the difference between streaming and batch processing in BigQuery.

Answer:
Streaming processing in BigQuery allows for real-time data ingestion, where data is immediately available for querying. Batch processing, on the other hand, involves loading data in bulk at scheduled intervals.

Official Reference: Streaming Data into BigQuery


Question 63: How can you optimize queries for cost and performance in BigQuery?

Answer:
You can optimize queries by using partitioned tables, clustering, and using the appropriate table decorators for time-based queries. Additionally, you should avoid unnecessary column selection and reduce the amount of data scanned.

Official Reference: Query Optimization


Question 64: What is the purpose of a correlated subquery in BigQuery?

Answer:
A correlated subquery is a subquery that depends on the outer query. It can reference columns from the outer query and is evaluated for each row processed by the outer query.

Official Reference: Correlated Subqueries


Question 65: How does BigQuery handle nested and repeated fields?

Answer:
BigQuery supports nested and repeated fields, allowing you to work with semi-structured data. You can use functions like ARRAY and STRUCT to manipulate and query these fields.

Official Reference: Working with Nested and Repeated Data


Question 66: Explain the purpose of a user-defined function (UDF) in BigQuery.

Answer:
A UDF in BigQuery allows you to define custom functions using SQL or JavaScript. These functions can be reused across queries, providing a way to encapsulate complex logic.

Official Reference: User-Defined Functions


Question 67: How can you export data from BigQuery to external storage or services?

Answer:
You can export data from BigQuery to various external storage options like Cloud Storage, Sheets, or Data Studio. This can be done using the bq extract command or through the UI.

Official Reference: Exporting Data


Question 68: Explain the concept of materialized views in BigQuery.

Answer:
Materialized views in BigQuery are precomputed queries that store the results of a query. They are useful for improving query performance by avoiding the need to recompute the same results.

Official Reference: Materialized Views


Question 69: How can you handle data ingestion and streaming in BigQuery?

Answer:
BigQuery supports real-time data ingestion through streaming. You can use tools like Dataflow, Pub/Sub, or directly stream data into BigQuery using the streaming API.

Official Reference: Streaming Data into BigQuery


Question 70: What is the purpose of the BigQuery Sandbox?

Answer:
The BigQuery Sandbox is a free environment that allows you to explore and experiment with BigQuery, with certain limitations on data size and query capacity.

Official Reference: BigQuery Sandbox


Certainly, let’s continue with more BigQuery interview questions:

Question 71: What are the benefits of using partitioned tables in BigQuery?

Answer:
Partitioned tables in BigQuery offer improved query performance and cost efficiency. They allow for faster querying of specific date ranges or partitions, and can significantly reduce the amount of data processed.

Official Reference: Partitioned Tables


Question 72: How can you schedule and automate queries in BigQuery?

Answer:
You can schedule and automate queries in BigQuery using tools like Cloud Composer, Dataflow, or by creating scheduled queries in the BigQuery UI. This allows for regular execution of queries without manual intervention.

Official Reference: Scheduling Query Execution


Question 73: Explain the concept of cost controls and budgets in BigQuery.

Answer:
Cost controls and budgets in BigQuery help you manage and monitor your spending. You can set daily, monthly, or custom budgets to prevent unexpected charges and receive alerts when thresholds are reached.

Official Reference: Managing Costs


Question 74: What is the purpose of the BigQuery Data Transfer Service?

Answer:
The BigQuery Data Transfer Service allows for automated data transfers from popular SaaS applications and platforms directly into BigQuery. It simplifies the process of importing data from external sources.

Official Reference: Data Transfer Service


Question 75: Explain the concept of slots in BigQuery.

Answer:
Slots in BigQuery represent computational resources that are required to execute a query. They are used to manage query processing capacity and can be allocated as per your needs.

Official Reference: Slots


Question 76: How does BigQuery handle data encryption and security?

Answer:
BigQuery encrypts data at rest and in transit by default. It also provides options for customer-managed encryption keys (CMEK) and supports IAM roles and permissions for access control.

Official Reference: Data Encryption


Question 77: What are the benefits of using BigQuery ML for machine learning tasks?

Answer:
BigQuery ML allows users to build and deploy machine learning models directly in BigQuery, without the need to move data to a separate ML platform. It simplifies the process of performing ML tasks on your data.

Official Reference: BigQuery ML


Question 78: How can you integrate BigQuery with other Google Cloud services?

Answer:
BigQuery integrates seamlessly with other Google Cloud services like Cloud Storage, Dataflow, Dataprep, and more. This enables a comprehensive data processing and analysis pipeline.

Official Reference: BigQuery Integration


Question 79: What is the purpose of a correlated subquery in BigQuery?

Answer:
A correlated subquery in BigQuery is a subquery that depends on the outer query. It can reference columns from the outer query and is evaluated for each row processed by the outer query.

Official Reference: Correlated Subqueries


Question 80: How can you use Google Data Studio with BigQuery for data visualization?

Answer:
You can connect Google Data Studio directly to BigQuery to create interactive and customizable dashboards for data visualization. This allows for easy sharing and exploration of insights.

Official Reference: Data Studio Integration


Certainly, let’s continue with more BigQuery interview questions:

Question 81: What is the purpose of a window function in BigQuery?

Answer:
A window function in BigQuery performs a calculation across a set of table rows related to the current row. It allows you to aggregate data while still preserving the granularity of the result set.

Official Reference: Window Functions


Question 82: How does BigQuery handle nested and repeated fields in a table?

Answer:
BigQuery supports nested and repeated fields, allowing for more complex and flexible data structures. You can query and manipulate these fields using dot notation and array functions.

Official Reference: Nested and Repeated Fields


Question 83: Explain the purpose of streaming inserts in BigQuery.

Answer:
Streaming inserts allow you to continuously add data to a table in real-time. This is useful for applications that require immediate availability of data for analysis.

Official Reference: Streaming Inserts


Question 84: How can you optimize the performance of queries in BigQuery?

Answer:
You can optimize query performance in BigQuery by using best practices like partitioning tables, clustering data, optimizing joins, and using efficient SQL queries.

Official Reference: Query Optimization


Question 85: What is the purpose of the BigQuery Reservations feature?

Answer:
BigQuery Reservations allow you to purchase dedicated resources for guaranteed processing capacity. This is beneficial for workloads that require consistent and high-performance query processing.

Official Reference: Reservations


Question 86: How does BigQuery handle data snapshots and point-in-time recovery?

Answer:
BigQuery automatically maintains a 7-day history of your data, allowing you to restore a table to any point within that timeframe. This ensures data integrity and recoverability.

Official Reference: Data Snapshots


Question 87: Explain the use of a User-Defined Function (UDF) in BigQuery.

Answer:
A UDF in BigQuery allows you to define custom SQL functions for more complex computations. It can be used to encapsulate logic and improve query readability.

Official Reference: User-Defined Functions


Question 88: What is the purpose of a Data Definition Language (DDL) statement in BigQuery?

Answer:
DDL statements in BigQuery are used to define, modify, or delete table schemas and structures. They include operations like CREATE, ALTER, and DROP.

Official Reference: DDL Statements


Question 89: How can you monitor and troubleshoot query performance in BigQuery?

Answer:
You can use tools like Query Execution Details, Query History, and Stackdriver Monitoring to monitor and troubleshoot query performance in BigQuery. These provide insights into query execution times and resource usage.

Official Reference: Query Performance


Question 90: Explain the concept of Materialized Views in BigQuery.

Answer:
Materialized Views in BigQuery are precomputed views that store the results of a query. They provide faster query response times for frequently accessed data.

Official Reference: Materialized Views


Certainly, let’s continue with more BigQuery interview questions:

Question 91: How does BigQuery handle data encryption and security?

Answer:
BigQuery encrypts data both in transit and at rest. It uses the Advanced Encryption Standard (AES) with a 256-bit key for encryption. Access controls and IAM policies provide additional security.

Official Reference: Data Encryption


Question 92: What is the purpose of the BigQuery Data Transfer Service?

Answer:
The Data Transfer Service in BigQuery allows you to automate the transfer of data from SaaS applications like Google Ads, YouTube, and more into BigQuery for analysis.

Official Reference: Data Transfer Service


Question 93: How can you handle complex data types like JSON and Avro in BigQuery?

Answer:
BigQuery natively supports complex data types like JSON and Avro. You can load, query, and export these formats directly, making it easy to work with semi-structured data.

Official Reference: Loading JSON and Avro Data


Question 94: Explain the concept of partitioned tables in BigQuery.

Answer:
Partitioned tables in BigQuery are divided into segments based on a column’s values (e.g., by date). This improves query performance and reduces costs by limiting the amount of data scanned.

Official Reference: Partitioned Tables


Question 95: How does BigQuery handle Data Lifecycle Management (DLM)?

Answer:
BigQuery supports DLM policies for managing the lifecycle of tables. You can set up rules to automatically delete or archive tables after a certain period, reducing storage costs.

Official Reference: Data Lifecycle Management


Question 96: What is the purpose of the BigQuery GIS data type?

Answer:
The GIS data type in BigQuery enables the storage and processing of geographical and spatial data. It supports operations like geospatial indexing and distance calculations.

Official Reference: GIS Data Type


Question 97: How can you export data from BigQuery to external storage or services?

Answer:
You can export data from BigQuery to various external services like Google Cloud Storage, Google Sheets, or even directly to a Cloud SQL database for further analysis or use.

Official Reference: Exporting Data


Question 98: What are the benefits of using BigQuery ML for machine learning tasks?

Answer:
BigQuery ML provides a convenient way to build and deploy machine learning models directly within BigQuery, eliminating the need to move data to a separate ML service.

Official Reference: BigQuery ML Overview


Question 99: How can you schedule queries and jobs in BigQuery?

Answer:
You can use features like scheduled queries, Cloud Scheduler, and BigQuery Data Transfer Service to automate and schedule queries and jobs in BigQuery.

Official Reference: Scheduled Queries


Question 100: What are some best practices for cost optimization in BigQuery?

Answer:
To optimize costs in BigQuery, consider practices like using partitioned tables, clustering data, using appropriate machine types, and managing DML operations efficiently.

Official Reference: Best Practices for Cost Optimization