DDrona4U
Sign inCreate account
My learningBigquery Interview QuestionsLesson 02
BigQuery Architecture Explained Simply: Dremel, Colossus, Jupiter & Borg

Lesson 02

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

BigQuery’s powerful architecture is built on four core Google technologies: Dremel, Colossus, Jupiter, and Borg. Dremel is the distributed query engine that processes SQL queries at massive scale, Colossus is Google’s highly reliable distributed storage system, Jupiter is the ultra-fast network connecting thousands of machines, and Borg is Google’s cluster management system that automatically handles resource allocation and scaling. Together, these technologies allow BigQuery to analyze petabytes of data quickly, efficiently, and without infrastructure management.

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. 0502:41

    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