Se ha denunciado esta presentación.
Se está descargando tu SlideShare.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.
  • Sé el primero en comentar

BigQuery의 모든 것(기획자, 마케터, 신입 데이터 분석가를 위한) 입문편

  1. 1. 
 

  2. 2.
  3. 3.
  4. 4.
  5. 5. Column 1 Column 2 Column 3 Row 1 Row 2 Row 3 Row 4
  6. 6.
  7. 7.
  8. 8.
  9. 9. user_id(int) event(string) event_date(string) 1 login_facebook 2019-05-14 1 write_posting 2019-05-14 1 write_comment 2019-05-14 1 view_posting 2019-05-14 1 view_posintg 2019-05-14 2 login_facebook 2019-05-14 2 view_posting 2019-05-14 2 view_posting 2019-05-14 2 write_comment 2019-05-14 2 logout 2019-05-14 2 login_facebook 2019-05-15 3 login_google 2019-05-15 3 write_posting 2019-05-15 3 view_posting 2019-05-15 3 purchase_item 2019-05-18 3 write_comment 2019-05-17 1 view_posting 2019-05-17 4 view_posintg 2019-05-17 5 purchase_item 2019-05-16
  10. 10. user_id event event_date 1 login_facebook 2019-05-14 1 write_posting 2019-05-14 1 write_comment 2019-05-14 1 view_posting 2019-05-14 1 view_posintg 2019-05-14
  11. 11. user_id event event_date 1 login_facebook 2019-05-14 1 write_posting 2019-05-14 1 write_comment 2019-05-14 1 view_posting 2019-05-14 1 view_posintg 2019-05-14
  12. 12. user_id event event_date unique total 1 login_facebook 2019-05-14 1 1 1 write_posting 2019-05-14 1 1 1 write_comment 2019-05-14 1 1 1 view_posting 2019-05-14 1 2
  13. 13. user_id event event_date unique total 1 login_facebook 2019-05-14 1 1 1 write_posting 2019-05-14 1 1 1 write_comment 2019-05-14 1 1 1 view_posting 2019-05-14 1 2
  14. 14.
  15. 15.
  16. 16. SELECT EXTRACT(DAY FROM DATE '2019-12-25') as the_day; +---------+ | the_day | +---------+ | 25      | +---------+
  17. 17. SELECT DATE_ADD(DATE “2019-03-25", INTERVAL 5 DAY) as five_days_later; +--------------------+ | five_days_later    | +--------------------+ | 2019-03-30         | +--------------------+ SELECT DATE_DIFF(DATE '2010-07-07', DATE '2008-12-25', DAY) as days_diff; +-----------+ | days_diff | +-----------+ | 559       | +-----------+
  18. 18. SELECT DATE_TRUNC(DATE '2008-12-25', MONTH) as month; +------------+ | month      | +------------+ | 2008-12-01 | +------------+
  19. 19. SELECT FORMAT_DATE("%x", DATE "2019-12-25") as US_format; +------------+ | US_format  | +------------+ | 12/25/19   | +------------+ SELECT PARSE_DATE("%x", "12/25/19") as parsed; +------------+ | parsed     | +------------+ | 2019-12-25 | +------------+
  20. 20. SELECT EXTRACT(HOUR FROM CAST(‘2019-12-25 14:00:00’ AS DATETIME) as hour; +---------+ | hour | +---------+ | 14      | +---------+
  21. 21.
  22. 22.
  23. 23.
  24. 24. Python
  25. 25.
  26. 26. https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
  27. 27. https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
  28. 28. https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays
  29. 29. https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays
  30. 30. https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays#arrays-and-aggregation
  31. 31. https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays#arrays-and-aggregation
  32. 32. https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays#arrays-and-aggregation
  33. 33. https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays
  34. 34. https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays
  35. 35. https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#struct-type
  36. 36. https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#struct-type
  37. 37. 
 https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#struct-type
  38. 38. https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#with_clause
  39. 39. https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#with_clause
  40. 40. https://cloud.google.com/bigquery/docs/views-intro
  41. 41. https://cloud.google.com/bigquery/docs/views-intro
  42. 42. https://cloud.google.com/bigquery/docs/views-intro
  43. 43. https://cloud.google.com/bigquery/docs/views-intro
  44. 44. https://cloud.google.com/bigquery/docs/views-intro
  45. 45. https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts
  46. 46. https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts
  47. 47. user_id visit_month 1004 1 1004 3 1004 7 1004 8 2112 3 2112 6 2112 7 3912 4
  48. 48. user_id visit_month next_visit_month 1004 1 1004 3 1004 7 1004 8 2112 3 2112 6 2112 7 3912 4
  49. 49. user_id visit_month next_visit_month 1004 1 3 1004 3 7 1004 7 8 1004 8 null 2112 3 6 2112 6 7 2112 7 null 3912 4 null
  50. 50. user_id visit_month 1004 1 1004 3 1004 7 1004 8 2112 3 2112 6 2112 7 3912 4 user_id visit_month 1004 1 1004 3 1004 7 1004 8 user_id visit_month 2112 3 2112 6 2112 7 user_id visit_month 3912 4
  51. 51. user_id visit_month 1004 1 1004 3 1004 7 1004 8 2112 3 2112 6 2112 7 3912 4 user_id visit_month 1004 1 1004 3 1004 7 1004 8 user_id visit_month 2112 3 2112 6 2112 7 user_id visit_month 3912 4
  52. 52. user_id visit_month 1004 1 1004 3 1004 7 1004 8 2112 3 2112 6 2112 7 3912 4 user_id visit_month 1004 1 1004 3 1004 7 1004 8 user_id visit_month 2112 3 2112 6 2112 7 user_id visit_month next_visit_month 1004 1 3 1004 3 7 1004 7 8 1004 8 null 2112 3 6 2112 6 7 2112 7 null 3912 4 null user_id visit_month 3912 4
  53. 53. user_id visit_month next_visit_month next_two_
 visit_month 1004 1 3 7 1004 3 7 8 1004 7 8 null 1004 8 null null 2112 3 6 7 2112 6 7 null 2112 7 null null 3912 4 null null
  54. 54. user_id visit_month 1004 1 1004 3 1004 7 1004 8 2112 3 2112 6 2112 7 3912 4
  55. 55. user_id visit_month 1004 1 1004 3 1004 7 1004 8 2112 3 2112 6 2112 7 3912 4
  56. 56. user_id visit_month 1004 1 null 1004 3 1 1004 7 3 1004 8 7 2112 3 null 2112 6 3 2112 7 6 3912 4 null
  57. 57. https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts
  58. 58. datetime demand 2019-05-15 14:00:00 13 15 2019-05-15 15:00:00 16 16 2019-05-15 16:00:00 20 20 2019-05-15 17:00:00 25 29 2019-05-15 18:00:00 41 32 2019-05-15 19:00:00 31 34 2019-05-15 20:00:00 29 30
  59. 59. https://blog.statsbot.co/sql-window-functions-tutorial-b5075b87d129
  60. 60. https://blog.statsbot.co/sql-window-functions-tutorial-b5075b87d129
  61. 61. https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions
  62. 62. https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions
  63. 63. 
 
 https://cloud.google.com/bigquery/docs/reference/standard-sql/user-defined-functions
  64. 64. https://cloud.google.com/bigquery/docs/reference/standard-sql/user-defined-functions#sql-udf-structure
  65. 65. https://cloud.google.com/bigquery/docs/reference/standard-sql/user-defined-functions#supported-external-udf-languages
  66. 66. https://cloud.google.com/bigquery/docs/reference/standard-sql/user-defined-functions#including-external-libraries
  67. 67. 
 https://cloud.google.com/bigquery/docs/partitioned-tables
  68. 68. 
 https://cloud.google.com/bigquery/docs/querying-wildcard-tables
  69. 69. https://cloud.google.com/bigquery/docs/creating-column-partitions
  70. 70. https://cloud.google.com/bigquery/docs/querying-partitioned-tables
  71. 71. 
 https://cloud.google.com/bigquery/docs/querying-partitioned-tables
  72. 72.
  73. 73. https://cloud.google.com/bigquery/docs/scheduling-queries
  74. 74. https://cloud.google.com/bigquery/docs/scheduling-queries
  75. 75. https://cloud.google.com/bigquery/docs/scheduling-queries
  76. 76. https://cloud.google.com/bigquery/docs/scheduling-queries
  77. 77. https://cloud.google.com/bigquery/docs/scheduling-queries
  78. 78. https://cloud.google.com/bigquery/docs/scheduling-queries
  79. 79. https://cloud.google.com/bigquery/docs/scheduling-queries
  80. 80. https://cloud.google.com/bigquery/docs/scheduling-queries
  81. 81. https://cloud.google.com/bigquery/docs/scheduling-queries
  82. 82. [ GS URL ] [ Table ] [ ( ] [ , ] [ ]
  83. 83.
  84. 84.
  85. 85. https://support.google.com/firebase/answer/7029846
  86. 86. https://support.google.com/firebase/answer/7029846
  87. 87. https://support.google.com/firebase/answer/6317485
  88. 88. https://support.google.com/firebase/answer/6317485
  89. 89. https://medium.com/firebase-developers/using-the-unnest-function-in-bigquery-to-analyze-event-parameters-in- analytics-fb828f890b42
  90. 90. https://medium.com/firebase-developers/using-the-unnest-function-in-bigquery-to-analyze-event-parameters-in- analytics-fb828f890b42
  91. 91.
  92. 92. https://www.slideshare.net/lynnlangit/google-cloud-and-data-pipeline-patterns
  93. 93. https://medium.com/teads-engineering/give-meaning-to-100-billion-analytics-events-a-day-d6ba09aa8f44
  94. 94. https://wecode.wepay.com/posts/bigquery-wepay
  95. 95.
  96. 96.
  97. 97. https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#sql-syntax

    Sé el primero en comentar

    Inicia sesión para ver los comentarios

  • tellaeve

    Oct. 11, 2020
  • JiHyunYoon1

    Oct. 15, 2020
  • softroom

    Oct. 28, 2020
  • ssuserb63bbd

    Nov. 3, 2020
  • seobmin

    Nov. 26, 2020
  • soohyunc

    Nov. 26, 2020
  • kodatt

    Nov. 28, 2020
  • HakyungKim6

    Nov. 28, 2020
  • kyusunshim

    Dec. 1, 2020
  • ssuser69c8dd

    Dec. 20, 2020
  • eunice_yoon

    Dec. 29, 2020
  • JeongroSeok1

    Jan. 26, 2021
  • ssusereafe89

    Feb. 3, 2021
  • SKKey

    Feb. 9, 2021
  • YoonsunCho2

    Jun. 10, 2021
  • hideyf

    Jun. 18, 2021
  • jh0x4s

    Jul. 2, 2021
  • jonghoWoo1

    Jul. 3, 2021
  • ssuser03f1c9

    Jul. 19, 2021
  • ssuser4fdd02

    Aug. 2, 2021

기획자, 마케터, 신입 데이터 분석가를 위한 BigQuery의 모든 것 - 입문편입니다 미리보기에선 저화질인데 다운로드하면 고화질로 다운된다고 합니다! 반응이 좋을 경우 심화편도 만들겠습니다 :)

Vistas

Total de vistas

25.750

En Slideshare

0

De embebidos

0

Número de embebidos

2.307

Acciones

Descargas

1.147

Compartidos

0

Comentarios

0

Me gusta

145

×