DDrona4U
Sign inCreate account
My learningBigquery Interview QuestionsLesson 05
BigQuery Data Types Explained: Standard SQL vs BigQuery-Specific Types

Lesson 05

BigQuery Data Types Explained: Standard SQL vs BigQuery-Specific Types

Google Cloud BigQuery supports common SQL data types such as STRING, INT64, FLOAT64, BOOL, DATE, DATETIME, TIME, and TIMESTAMP for storing text, numbers, and time-based data. It also includes advanced types like ARRAY and STRUCT (RECORD), which are more unique compared to traditional relational databases because they allow nested and repeated data inside a single table row. BigQuery additionally supports GEOGRAPHY for spatial/location data and JSON for semi-structured data, making it highly flexible for modern analytics workloads.

Get the full lesson

Sign in to unlock everything beyond the preview — it's free.

  • Take timestamped notes as you watch
  • Read the full transcript and download resources
  • Join the discussion and track your progress
Sign inCreate free account

Curriculum

19 lessons · 47m

0/19 lessons done47m left
  1. 02:40

    BigQuery vs Traditional Databases: What Makes It Different?

    02:40

  2. 02:43

    BigQuery Architecture Explained Simply: Dremel, Colossus, Jupiter & Borg

    02:43

  3. 02:26

    Why BigQuery Separates Storage and Compute — And Why It Matters for Business

    02:26

  4. 0402:23

    BigQuery Dataset vs Project vs Table Explained Simply

    02:23

  5. 02:41

    BigQuery Data Types Explained: Standard SQL vs BigQuery-Specific Types

    02:41

  6. 0602:24

    BigQuery Table vs View vs Materialized View Explained

    02:24

  7. 0702:31

    BigQuery Slots Explained: How Queries Use Compute Resources

    02:31

  8. 0802:25

    How BigQuery’s Columnar Storage Makes Queries Faster Than Row-Based Databases

    02:25

  9. 0902:40

    BigQuery Limitations Compared to PostgreSQL OLTP Databases

    02:40

  10. 1002:26

    BigQuery Table Partitioning Explained: Improving Cost and Query Performance

    02:26

  11. 1102:22

    BigQuery Partitioning Types Explained: Date, Ingestion-Time, Integer-Range & Time-Unit Columns

    02:22

  12. 1202:26

    BigQuery Clustering vs Partitioning Explained Clearly

    02:26

  13. 1302:24

    Using Partitioning and Clustering Together in BigQuery: When and Why

    02:24

  14. 1402:35

    Designing BigQuery Partitioning for Large Time-Series Tables (10 TB+)

    02:35

  15. 1502:17

    BigQuery Partition Performance Without a WHERE Filter on the Partition Column

    02:17

  16. 1602:23

    BigQuery require_partition_filter Explained: Why It’s a Best Practice for Large Tables

    02:23

  17. 1702:23

    BigQuery Schema Evolution Explained: Adding New Columns Safely

    02:23

  18. 1802:24

    BigQuery Time Travel Explained: Recovering Accidentally Deleted Tables

    02:24

  19. 1902:25

    BigQuery Table Snapshots vs Clones vs Copies Explained

    02:25