BigQuery Interview Questions and Answers for internship

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BigQuery Internship Interview Questions and Answers

  1. What is BigQuery?

    • Answer: BigQuery is Google's fully managed, serverless data warehouse designed for analytics. It allows you to store and analyze massive datasets using SQL. Its scalability and performance make it ideal for handling large-scale analytical queries.

  2. Explain the difference between BigQuery and traditional databases.

    • Answer: Traditional databases (like MySQL or PostgreSQL) are optimized for transactional workloads (OLTP), focusing on fast individual read/write operations. BigQuery, on the other hand, is optimized for analytical workloads (OLAP), focusing on fast query execution over massive datasets. It prioritizes read performance over write performance.

  3. What are some key features of BigQuery?

    • Answer: Key features include: scalability, serverless architecture, columnar storage (for faster query performance), support for standard SQL, integration with other Google Cloud services (like Data Studio and Dataflow), cost-effectiveness based on usage, and built-in security and access control.

  4. Describe the different data types in BigQuery.

    • Answer: BigQuery supports various data types including: STRING, INTEGER, FLOAT, BOOLEAN, DATE, DATETIME, TIMESTAMP, GEOGRAPHY, RECORD (nested and repeated fields), and ARRAY.

  5. Explain the concept of partitioning and clustering in BigQuery.

    • Answer: Partitioning divides a table into smaller, manageable subsets based on a column (e.g., date). This improves query performance by allowing BigQuery to scan only relevant partitions. Clustering orders rows within each partition based on one or more columns, further optimizing query performance for queries filtering or aggregating on those columns.

  6. What is a BigQuery project?

    • Answer: A BigQuery project is a container for your datasets, tables, and other resources within the BigQuery service. It provides a way to organize and manage your data and access control.

  7. How do you create a table in BigQuery?

    • Answer: You create a table using SQL statements like `CREATE TABLE` specifying the table name, schema (column names and data types), and optionally partitioning and clustering options.

  8. Explain the different storage classes in BigQuery.

    • Answer: BigQuery offers different storage classes: `ON-DEMAND` (for frequently accessed data), `COLDLINE` (for less frequently accessed data, cheaper storage), and `ARCHIVE` (for rarely accessed data, the cheapest storage). The storage class affects the retrieval speed and cost.

  9. How do you query data in BigQuery?

    • Answer: Data is queried using standard SQL. You use the `SELECT` statement to specify the columns to retrieve, `FROM` to specify the table, `WHERE` to filter rows, and other clauses like `GROUP BY`, `ORDER BY`, `HAVING` to manipulate and aggregate data.

  10. What are User-Defined Functions (UDFs) in BigQuery?

    • Answer: UDFs are custom functions written in SQL or JavaScript that can be used within your BigQuery queries to perform complex data transformations or calculations not available in standard SQL functions.

  11. Explain the concept of views in BigQuery.

    • Answer: A view is a stored query that acts like a virtual table. It doesn't store data itself, but provides a customized way to access data from one or more underlying tables. Changes to the underlying tables are reflected in the view.

  12. How do you handle NULL values in BigQuery?

    • Answer: NULL values represent missing or unknown data. You can use functions like `IFNULL` or `COALESCE` to replace NULLs with a default value, or use the `IS NULL` or `IS NOT NULL` predicates in WHERE clauses to filter them.

  13. What are some best practices for writing efficient BigQuery queries?

    • Answer: Best practices include using appropriate partitioning and clustering, filtering data early in the query using `WHERE` clauses, using specific column names instead of `SELECT *`, avoiding unnecessary joins, and using appropriate data types.

  14. How do you perform joins in BigQuery?

    • Answer: BigQuery supports various join types: `INNER JOIN`, `LEFT JOIN`, `RIGHT JOIN`, `FULL OUTER JOIN`. You specify the join condition using the `ON` clause.

  15. Explain the role of BigQuery's access control.

    • Answer: BigQuery uses role-based access control (RBAC) to manage who can access and modify data. You grant specific roles (e.g., `OWNER`, `WRITER`, `READER`) to users and service accounts to control their permissions.

  16. How can you monitor the performance of your BigQuery queries?

    • Answer: The BigQuery web UI and command-line tools provide information on query execution time, bytes processed, and other performance metrics. You can use these metrics to identify slow queries and optimize them.

  17. What is BigQuery's pricing model?

    • Answer: BigQuery uses a pay-as-you-go pricing model. You are charged based on the amount of data processed by your queries and the storage used for your datasets. Pricing also varies depending on the storage class.

  18. How does BigQuery handle data ingestion?

    • Answer: Data can be ingested into BigQuery through various methods: loading from CSV, JSON, Avro, Parquet files; using streaming inserts for real-time data; or using tools like Dataflow for ETL processes.

  19. What are some common BigQuery error messages and how do you troubleshoot them?

    • Answer: Common errors include exceeding query resource limits, syntax errors, permission issues, and data loading errors. Troubleshooting involves examining error messages, checking query syntax, verifying permissions, and inspecting the data being loaded.

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