Это пробный урок. Оформите подписку, чтобы получить доступ ко всем материалам курса. Премиум

  1. Урок 1. 00:02:17
    Welcome
  2. Урок 2. 00:04:53
    Course Objective & Code
  3. Урок 3. 00:02:33
    Data Overview
  4. Урок 4. 01:48:48
    Office Hours 2026-05-05
  5. Урок 5. 01:35:25
    Office Hours 2026-05-07
  6. Урок 6. 01:26:33
    Office Hours 2026-05-12
  7. Урок 7. 01:30:30
    Office Hours 2026-05-14
  8. Урок 8. 01:09:18
    Office Hours 2026-05-19
  9. Урок 9. 00:09:23
    What is a Data Warehouse
  10. Урок 10. 00:06:27
    Kimball Data Model
  11. Урок 11. 00:03:49
    Analytical queries involve joining fact and dimension tables and grouping by dimension attribute(s)
  12. Урок 12. 00:05:16
    Facts are generated by your system, the user's browser, or purchased from a third-party
  13. Урок 13. 00:12:29
    Dimensions represent a business concept
  14. Урок 14. 00:10:36
    Python for extracting, transforming, & loading data into a modelled destination
  15. Урок 15. 00:09:08
    Data Storage Patterns Partitioning for efficient reads & Bucketing for efficient joinsgroup by
  16. Урок 16. 00:03:50
    Bus Matrix Get everyone on the same page
  17. Урок 17. 00:06:12
    Pipeline Types Full refresh processes the entire source, and incremental processes a time range-specific source
  18. Урок 18. 00:01:10
    Recap
  19. Урок 19. 00:03:00
    Python enables you to control multiple systems
  20. Урок 20. 00:10:25
    Create SCD2 tables with MERGE INTO
  21. Урок 21. 00:07:06
    Backfills are inevitable, design your pipelines for them
  22. Урок 22. 00:06:48
    Wait to process the fact data until you are certain most of it has arrived
  23. Урок 23. 00:05:28
    Data pipeline scripts should be re-runnable without creating duplicate or partial data (aka idempotent)
  24. Урок 24. 00:04:10
    Self-healing pipelines make maintenance easy.
  25. Урок 25. 00:01:39
    Recap
  26. Урок 26. 00:06:41
    3-hop architecture Bronze is source, Silver is factdims, & Gold is summary tables
  27. Урок 27. 00:09:15
    Gold tables are for select from gold_tbl by end users
  28. Урок 28. 00:09:11
    Use nested data structures to create wide OBTs
  29. Урок 29. 00:05:45
    Run fact pipelines hourly for data availability and daily to catch late events (aka Lambda Architecture)
  30. Урок 30. 00:00:53
    Recap
  31. Урок 31. 00:04:43
    Check your data before end-users use it, with the WAP pattern
  32. Урок 32. 00:06:17
    Choose the type of data quality check based on the data
  33. Урок 33. 00:09:13
    Implementing DQ checks
  34. Урок 34. 00:01:11
    Recap
  35. Урок 35. 00:13:42
    Scheduling data pipelines with Apache Airflow
  36. Урок 36. 00:11:20
    Time range of data to be processed is supplied by Airflow
  37. Урок 37. 00:10:46
    Running pipeline when a dataset is updated
  38. Урок 38. 00:04:43
    Airflow Architecture
  39. Урок 39. 00:02:10
    Recap
  40. Урок 40. 00:11:02
    Check that your code does what you think it does with tests
  41. Урок 41. 00:11:48
    Use Pytest to manage tests
  42. Урок 42. 00:04:49
    Ensure systems work together as expected with Integration tests
  43. Урок 43. 00:01:07
    Recap
  44. Урок 44. 00:08:06
    Data contract defines your requirements
  45. Урок 45. 00:03:48
    Objective
  46. Урок 46. 00:05:53
    Define Outcomes
  47. Урок 47. 00:04:27
    Architecture & Data Flow
  48. Урок 48. 00:07:58
    Write code - Bronze & Silver
  49. Урок 49. 00:07:25
    Write code - Gold
  50. Урок 50. 00:03:42
    Data quality
  51. Урок 51. 00:03:45
    Visualizing outputs
  52. Урок 52. 00:05:13
    Orchestrate your pipelines & Present them
  53. Урок 53. 00:02:11
    Recap
  54. Урок 54. 00:00:52
    Interview Prep Is a Process, Not a Checklist
  55. Урок 55. 00:17:42
    Data Structures & Algorithms
  56. Урок 56. 00:09:54
    SQL & Data Manipulation
  57. Урок 57. 00:13:54
    System Design, Defining Metrics & Data Modeling
  58. Урок 58. 00:04:56
    Behavioral Interview
  59. Урок 59. 00:02:18
    Company Specific Preparation
  60. Урок 60. 00:01:55
    Recap