BigQuery Interview Questions and Answers for 5 years experience

1. What are the key differences between BigQuery and traditional relational databases like MySQL or PostgreSQL?

   • Answer: BigQuery is a fully managed, serverless data warehouse designed for large-scale analytics, while MySQL and PostgreSQL are relational database management systems (RDBMS) typically used for transactional workloads. Key differences include: BigQuery's scalability (handling petabytes of data), its columnar storage (optimized for analytical queries), its use of SQL (with some extensions), its pay-as-you-go pricing model, and its lack of support for transactions in the traditional sense. RDBMS systems generally excel at transactional processing, ACID properties, and smaller datasets, often offering more fine-grained control over schema and data management.

2. Explain the concept of partitioning and clustering in BigQuery. How do they improve query performance?

   • Answer: Partitioning divides a BigQuery table into smaller, manageable subsets based on a column's values (e.g., date). Clustering groups rows within partitions based on one or more columns frequently used in WHERE clauses. This improves query performance because BigQuery only needs to scan the relevant partitions and clustered data, drastically reducing the amount of data processed for a given query. It's analogous to indexing in traditional databases but operates at a larger scale and with different mechanisms.

3. Describe different data types supported by BigQuery. Give examples.

   • Answer: BigQuery supports various data types including: `INT64`, `FLOAT64`, `NUMERIC`, `STRING`, `BOOLEAN`, `BYTES`, `TIMESTAMP`, `DATE`, `TIME`, `GEOGRAPHY`, `ARRAY`, `STRUCT`, and `RECORD`. Examples: `INT64` for integer values (e.g., 123), `STRING` for text ("Hello World"), `TIMESTAMP` for timestamps (e.g., 2024-10-27 10:30:00), `ARRAY` for lists of values ([1,2,3]), `STRUCT` for nested data structures (e.g., containing name and address fields).

4. How do you handle large datasets in BigQuery efficiently?

   • Answer: Efficiently handling large datasets involves several strategies: Partitioning and clustering (as discussed previously), utilizing appropriate data types to minimize storage, employing wildcard tables to manage data over time, using optimized query patterns (avoiding full table scans), leveraging BigQuery's built-in functions for data manipulation and aggregation, and properly designing your schema for efficient querying. Understanding the data and query patterns is crucial for optimization.

5. Explain the difference between a BigQuery view and a table.

   • Answer: A BigQuery table stores data directly, while a view is a stored query that acts as a virtual table. Views don't store data themselves; they retrieve data dynamically when queried. Views can simplify complex queries, improve data security by restricting access to underlying tables, and provide different perspectives on the same data. However, views can be slower than querying tables directly, especially complex views.

6. What are user-defined functions (UDFs) in BigQuery? When would you use them?

   • Answer: UDFs are custom functions written in SQL, JavaScript, or Python that can be used within BigQuery queries. They allow you to encapsulate complex logic or perform operations not directly available in standard SQL. You'd use UDFs when you need to perform custom calculations, data transformations, or string manipulations that aren't easily expressed in standard SQL. They can enhance code reusability and readability.

7. How do you debug BigQuery queries?

   • Answer: BigQuery debugging involves several approaches: Analyzing query execution plans (to identify bottlenecks), using `INFORMATION_SCHEMA` metadata to inspect table schemas and data, breaking down complex queries into smaller, more manageable parts, using `LIMIT` clauses to examine a subset of results, leveraging BigQuery's logging capabilities to track query performance and errors, and using tools like the BigQuery web UI or command-line tools to inspect intermediate results and error messages.

8. Describe different ways to load data into BigQuery.

   • Answer: Data can be loaded into BigQuery via several methods: using the BigQuery web UI, the command-line tool (bq), client libraries (Python, Java, etc.), streaming inserts, batch loading from various sources (like Cloud Storage, Google Cloud Data Fusion), and using other Google Cloud services like Dataflow for data transformation and loading.

9. Explain the concept of legacy SQL and standard SQL in BigQuery. What are the differences?

   • Answer: BigQuery supports two SQL dialects: legacy SQL and standard SQL. Standard SQL is based on the ANSI SQL standard and offers improved syntax, better compatibility with other SQL databases, and support for window functions and common table expressions (CTEs). Legacy SQL, while still supported, is gradually being phased out; standard SQL is recommended for new projects due to its improved features and broader compliance. Key differences include syntax, function availability, and overall query capabilities.

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