by Shanmugapriya J, on Jun 3, 2023 4:06:20 PM
1. Google BigQuery, and what are its key features?
Ans: Google BigQuery is a fully-managed, serverless data warehouse and analytics platform. Its key features include scalability, fast query execution, automatic infrastructure management, real-time analytics, and integration with other Google Cloud services.
2. How is data organized and stored in BigQuery ?
Ans: BigQuery organizes data into datasets, which can contain tables, views, and user-defined functions. Each dataset is stored in a project and can be accessed and queried using SQL-like syntax.
3. What is the difference between BigQuery and traditional relational databases?
Ans: Unlike traditional relational databases, BigQuery is designed for handling large-scale, distributed data processing. It utilizes a columnar storage format and executes queries in parallel across multiple nodes for faster query performance.
4. How do you import data into BigQuery?
Ans: Data can be imported into BigQuery using various methods such as:
1. Uploading CSV, JSON, or Avro files directly.Streaming data into BigQuery using the streaming API.
2. Loading data from Google Cloud Storage or other external data sources.
3. Using Dataflow or other ETL (Extract, Transform, Load) tools for batch data ingestion.
5. How does BigQuery handle data partitioning and clustering?
Ans: BigQuery supports partitioning tables based on a specific date or timestamp column. This helps optimize query performance by restricting data scanning to specific partitions. Clustering involves organizing data within each partition based on one or more columns, which further improves query efficiency.
6. Can you explain the concept of nested and repeated fields in BigQuery?
Ans: BigQuery supports nested and repeated fields within its tables. Nested fields allow you to have structured data within a column, while repeated fields allow you to have an array or a list of values within a column.
7. How does BigQuery handle data encryption and security?
Ans: BigQuery encrypts data at rest and in transit. It uses Google-managed encryption keys and provides options for customer-managed encryption keys (CMEK) for added security. Access controls, identity and access management (IAM), and audit logs are available to manage and monitor data access.
8. How can you optimize query performance in BigQuery?
Ans: To optimize query performance in BigQuery, you can:
1. Use partitioning and clustering to reduce the amount of data scanned.
2. Optimize the schema design and use nested and repeated fields appropriately.
3. Cache frequently accessed data using BigQuery's cache mechanism.
4. Use the EXPLAIN statement to analyze query execution plans and identify bottlenecks.
5.Use table decorators to query specific snapshots of table data.
9. What is the role of BigQuery slots, and how does slot allocation work?
Ans: BigQuery slots determine the amount of computational resources allocated to process queries. Slots are measured in slot-seconds and can be allocated in two ways: on-demand or through a dedicated reservation. Slots usage impacts query execution speed and concurrency limits.
10. How can you control costs in BigQuery?
Ans: 1. Enable query caching to avoid redundant computations.
2. Use partitioning and clustering to reduce the amount of data scanned.
3. Set up cost controls and budgets to monitor and limit spending.
4. Consider optimizing schema design and query structure to minimize data processing.
11. What is the difference between a table and a view in BigQuery?
Ans: A table in BigQuery stores actual data, while a view is a virtual table that references data from one or more tables or views. Views allow you to create customized subsets of data or apply filters without duplicating the underlying data.
12. How can you schedule and automate data transformations or ETL processes in BigQuery?
Ans: In BigQuery, you can schedule and automate data transformations or ETL processes using tools like Cloud Dataflow or Cloud Composer (Apache Airflow). These tools enable you to create pipelines and workflows for data processing and transformation tasks.
13. How does BigQuery handle data access control and permissions?
Ans: BigQuery uses Google Cloud's Identity and Access Management (IAM) to manage data access control and permissions. IAM allows you to assign roles and permissions to individual users, groups, or service accounts at the project, dataset, or table level.
14. Can you explain how BigQuery federated queries work?
Ans: BigQuery federated queries allow you to query data from external sources without loading the data into BigQuery. You can query data stored in Google Cloud Storage, Google Sheets, or other supported external data sources using standard SQL syntax.
15. What is the difference between streaming inserts and batch loading in BigQuery?
Ans: Streaming inserts allow you to insert data into BigQuery in real-time, while batch loading is a method of loading data in larger batches using jobs. Streaming inserts have a higher cost per row and are better suited for low-latency use cases, while batch loading is more efficient for larger data volumes.
16. How do you export data from BigQuery to other formats or storage systems?
Ans: BigQuery provides several options to export data, including:
1. Exporting data to Google Cloud Storage as CSV, JSON, Avro, or Parquet files.
2. Using Dataflow or other ETL tools to transform and export data to different systems.
3. Directly querying BigQuery from other applications or tools via JDBC or ODBC drivers.
17. Can you explain how BigQuery handles data consistency in case of concurrent writes or updates?
Ans: BigQuery does not provide row-level or transactional consistency guarantees. When concurrent writes or updates occur, there may be a delay before the changes become visible in query results due to the distributed nature of BigQuery's architecture.
18. How does BigQuery handle schema evolution and changes?
Ans: BigQuery supports schema evolution, allowing you to add new columns to existing tables without affecting the existing data. It also provides options to update or modify existing schemas using DDL (Data Definition Language) statements
19. What are the benefits of using BigQuery ML for machine learning tasks?
Ans: BigQuery ML enables you to build and deploy machine learning models directly within BigQuery, utilizing its processing power and scalability. It simplifies the machine learning workflow, eliminates the need for data movement, and provides SQL-based machine learning capabilities.
20. How does BigQuery integrate with other Google Cloud services?
Ans: BigQuery integrates with other Google Cloud services, such as Cloud Storage, Dataflow, Dataproc, and Pub/Sub, to enable seamless data movement, data processing, and analytics workflows. It can also integrate with external tools and platforms via APIs and connectors.
21. What are the advantages of using BigQuery for real-time analytics?
Ans: BigQuery's real-time analytics capabilities allow you to analyze data as it is ingested, enabling you to make immediate data-driven decisions. The combination of BigQuery's scalability, fast query execution, and integrations with real-time data sources makes it well-suited for real-time analytics use cases.
22. How does BigQuery handle query optimization?
Ans: BigQuery's query optimizer automatically optimizes queries for efficient execution. It considers factors such as query structure, table statistics, and available resources to determine the most efficient query plan. BigQuery also provides query plan explanations and performance troubleshooting tools.
23. Can you explain how BigQuery handles data shuffling and joins?
Ans: BigQuery uses a distributed query execution engine that performs data shuffling or redistribution across multiple nodes to optimize join operations. It intelligently distributes and parallelizes join operations to minimize data movement and improve query performance.
24. How do you monitor and troubleshoot query performance issues in BigQuery?
Ans: BigQuery provides several tools for monitoring and troubleshooting query performance issues, including:
1. Query execution statistics and metrics in the BigQuery UI.
2. Query plan explanations to understand how the query is executed.
3. Stackdriver Logging and Monitoring for system-level monitoring and alerts.
4. Query auditing and profiling to identify and optimize resource-intensive queries.
25. Have you worked on any complex or challenging projects using BigQuery? If yes, explain your experience.
Ans: The candidate should provide their personal experience and examples of working on complex or challenging projects using BigQuery. They should describe the objectives, challenges faced, their role in the project, and the solutions implemented. They can highlight any innovative approaches, optimization techniques, or successful outcomes achieved.
