BigQuery Interview Questions and Answers for 7 years experience

1. What are the different data types supported by BigQuery?

   • Answer: BigQuery supports various data types including STRING, BYTES, INTEGER, INT64, FLOAT64, NUMERIC, BOOLEAN, DATE, DATETIME, TIMESTAMP, GEOGRAPHY, ARRAY, STRUCT, and RECORD. Understanding the nuances of each, particularly the differences between INT64 and NUMERIC for large numbers, and the efficient use of arrays and structs, is crucial.

2. Explain the difference between clustered and non-clustered tables in BigQuery.

   • Answer: Clustered tables are optimized for queries that filter on the clustering key. Data is physically sorted by the clustering key columns, leading to faster query performance for those specific filters. Non-clustered tables don't have this physical sorting, making them suitable for more general-purpose querying where filtering on the clustering key isn't predominant. The choice depends on the typical query patterns against the table.

3. Describe different BigQuery storage options and when you would choose each one.

   • Answer: BigQuery offers various storage options: Standard storage is for frequently queried data. Nearline storage is for less frequently accessed data with lower cost. Coldline storage is for archival purposes with the lowest cost but longer retrieval times. The choice depends on the query frequency and cost-performance trade-offs. Understanding the retrieval latency of each is essential.

4. How do you handle data loading into BigQuery efficiently?

   • Answer: Efficient data loading involves using the optimal loading methods (e.g., streaming inserts for real-time data, batch loading for large datasets), partitioning and clustering for improved query performance, schema design optimization, and appropriate data formatting (e.g., Avro, Parquet) to minimize ingestion time and storage costs. Utilizing parallel loading techniques and understanding the limitations of each method are key aspects.

5. Explain the concept of partitioning in BigQuery and its benefits.

   • Answer: Partitioning divides a table into smaller, manageable subsets based on a column (e.g., date). This significantly improves query performance by allowing BigQuery to scan only the relevant partitions instead of the entire table. It also helps with data management, cost optimization (by deleting older partitions), and simplifies data lifecycle management.

6. How do you optimize BigQuery queries for performance?

   • Answer: Query optimization involves several strategies: using appropriate filters and WHERE clauses, leveraging partitioning and clustering, employing appropriate data types, using pre-aggregated tables for frequently accessed summaries, avoiding unnecessary joins and subqueries, employing wildcard functions sparingly, and analyzing query plans for bottlenecks. Tools like the BigQuery query profiler and execution plan visualization are vital.

7. What are BigQuery's different pricing models?

   • Answer: BigQuery uses a pay-as-you-go model based on storage used and query processing costs. Storage costs vary by storage class (Standard, Nearline, Coldline). Query costs depend on the amount of data processed. Understanding the different pricing tiers and how to optimize for cost-efficiency is critical for managing budgets.

8. Explain the role of UDFs (User-Defined Functions) in BigQuery.

   • Answer: UDFs allow you to extend BigQuery's built-in functions with custom logic written in SQL, JavaScript, or Python. They enable you to encapsulate complex calculations or data transformations, making your queries more concise and readable. However, they can impact query performance if not implemented efficiently, so careful consideration of their use is crucial.

9. Describe your experience with BigQuery's data security and access control features.

   • Answer: [Describe specific experience with IAM roles, data masking, encryption at rest and in transit, network restrictions, and other security features used. Quantify the impact of these security measures on data protection and compliance.]

10. How do you handle errors and exceptions while working with BigQuery?

    • Answer: Error handling involves using `TRY...CATCH` blocks in SQL or exception handling in client-side code to gracefully manage potential failures. Implementing robust logging mechanisms to track errors and their root causes and using retry mechanisms for transient errors are vital for building reliable BigQuery applications.

11. How do you perform data validation in BigQuery?

    • Answer: Data validation involves checking data quality through various methods, including using constraints (e.g., `NOT NULL`, `UNIQUE`), data type validation, range checks, pattern matching using regular expressions, and custom validation logic using UDFs. Implementing automated data validation pipelines and using monitoring tools to detect anomalies is also crucial.

12. Explain your experience with BigQuery's integration with other Google Cloud Platform (GCP) services.

    • Answer: [Describe specific experience with integrating BigQuery with services like Dataflow, Dataproc, Cloud Storage, Data Fusion, and Cloud Functions. Provide examples of how you leveraged these integrations to build end-to-end data pipelines or solutions.]

13. How do you handle large-scale data transformations in BigQuery?

    • Answer: For large-scale transformations, I leverage techniques like batch processing with Dataflow or Dataproc, breaking down transformations into smaller, manageable jobs. I also optimize for performance using techniques like partitioning, clustering, and avoiding unnecessary data shuffling. Understanding the tradeoffs between different processing frameworks is critical for scalability.

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