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

  1. Урок 1. 00:01:35
    The Data Engineering Bootcamp: Zero to Mastery
  2. Урок 2. 00:04:17
    Introduction to Data Engineering
  3. Урок 3. 00:04:43
    Who Are Data Engineers?
  4. Урок 4. 00:03:19
    Prerequisites
  5. Урок 5. 00:01:19
    Source Code for This Bootcamp
  6. Урок 6. 00:04:38
    Plan for This Bootcamp
  7. Урок 7. 00:06:37
    [Optional] What Is a Virtualenv?
  8. Урок 8. 00:11:03
    [Optional] What Is Docker?
  9. Урок 9. 00:04:08
    Introduction
  10. Урок 10. 00:03:44
    Apache Spark
  11. Урок 11. 00:04:24
    How Spark Works
  12. Урок 12. 00:07:41
    Spark Application
  13. Урок 13. 00:06:43
    DataFrames
  14. Урок 14. 00:05:51
    Installing Spark
  15. Урок 15. 00:07:02
    Inside Airbnb Data
  16. Урок 16. 00:07:05
    Writing Your First Spark Job
  17. Урок 17. 00:02:16
    Lazy Processing
  18. Урок 18. 00:01:29
    [Exercise] Basic Functions
  19. Урок 19. 00:06:41
    [Exercise] Basic Functions - Solution
  20. Урок 20. 00:04:00
    Aggregating Data
  21. Урок 21. 00:04:40
    Joining Data
  22. Урок 22. 00:06:10
    Aggregations and Joins with Spark
  23. Урок 23. 00:05:09
    Complex Data Types
  24. Урок 24. 00:00:50
    [Exercise] Aggregate Functions
  25. Урок 25. 00:05:54
    [Exercise] Aggregate Functions - Solution
  26. Урок 26. 00:03:25
    User Defined Functions
  27. Урок 27. 00:06:14
    Data Shuffle
  28. Урок 28. 00:03:42
    Data Accumulators
  29. Урок 29. 00:07:39
    Optimizing Spark Jobs
  30. Урок 30. 00:04:29
    Submitting Spark Jobs
  31. Урок 31. 00:05:16
    Other Spark APIs
  32. Урок 32. 00:04:33
    Spark SQL
  33. Урок 33. 00:02:10
    [Exercise] Advanced Spark
  34. Урок 34. 00:05:26
    [Exercise] Advanced Spark - Solution
  35. Урок 35. 00:03:08
    Summary
  36. Урок 36. 00:04:26
    Introduction
  37. Урок 37. 00:09:08
    What Is a Data Lake?
  38. Урок 38. 00:07:47
    Amazon Web Services (AWS)
  39. Урок 39. 00:05:45
    Simple Storage Service (S3)
  40. Урок 40. 00:09:29
    Setting Up an AWS Account
  41. Урок 41. 00:03:24
    Data Partitioning
  42. Урок 42. 00:07:49
    Using S3
  43. Урок 43. 00:02:59
    EMR Serverless
  44. Урок 44. 00:02:52
    IAM Roles
  45. Урок 45. 00:08:49
    Running a Spark Job
  46. Урок 46. 00:07:41
    Parquet Data Format
  47. Урок 47. 00:05:32
    Implementing a Data Catalog
  48. Урок 48. 00:06:42
    Data Catalog Demo
  49. Урок 49. 00:04:00
    Querying a Data Lake
  50. Урок 50. 00:03:39
    Summary
  51. Урок 51. 00:05:53
    Introduction
  52. Урок 52. 00:05:19
    What Is Apache Airflow?
  53. Урок 53. 00:03:15
    Airflow’s Architecture
  54. Урок 54. 00:06:33
    Installing Airflow
  55. Урок 55. 00:08:03
    Defining an Airflow DAG
  56. Урок 56. 00:03:38
    Errors Handling
  57. Урок 57. 00:04:54
    Idempotent Tasks
  58. Урок 58. 00:04:58
    Creating a DAG - Part 1
  59. Урок 59. 00:04:42
    Creating a DAG - Part 2
  60. Урок 60. 00:04:09
    Handling Failed Tasks
  61. Урок 61. 00:04:31
    [Exercise] Data Validation
  62. Урок 62. 00:03:27
    [Exercise] Data Validation - Solution
  63. Урок 63. 00:03:02
    Spark with Airflow
  64. Урок 64. 00:07:39
    Using Spark with Airflow - Part 1
  65. Урок 65. 00:05:52
    Using Spark with Airflow - Part 2
  66. Урок 66. 00:04:46
    Sensors In Airflow
  67. Урок 67. 00:04:08
    Using File Sensors
  68. Урок 68. 00:05:50
    Data Ingestion
  69. Урок 69. 00:06:03
    Reading Data From Postgres - Part 1
  70. Урок 70. 00:05:40
    Reading Data from Postgres - Part 2
  71. Урок 71. 00:03:53
    [Exercise] Average Customer Review
  72. Урок 72. 00:04:33
    [Exercise] Average Customer Review - Solution
  73. Урок 73. 00:04:26
    Advanced DAGs
  74. Урок 74. 00:02:27
    Summary
  75. Урок 75. 00:05:28
    Introduction
  76. Урок 76. 00:06:06
    What Is Machine Learning
  77. Урок 77. 00:05:38
    Regression Algorithms
  78. Урок 78. 00:05:04
    Building a Regression Model
  79. Урок 79. 00:09:46
    Training a Model
  80. Урок 80. 00:07:26
    Model Evaluation
  81. Урок 81. 00:03:57
    Testing a Regression Model
  82. Урок 82. 00:02:12
    Model Lifecycle
  83. Урок 83. 00:08:44
    Feature Engineering
  84. Урок 84. 00:07:34
    Improving a Regression Model
  85. Урок 85. 00:03:56
    Machine Learning Pipelines
  86. Урок 86. 00:02:41
    Creating a Pipeline
  87. Урок 87. 00:01:59
    [Exercise] House Price Estimation
  88. Урок 88. 00:03:12
    [Exercise] House Price Estimation - Solution
  89. Урок 89. 00:02:57
    [Exercise] Imposter Syndrome
  90. Урок 90. 00:07:37
    Classification
  91. Урок 91. 00:04:27
    Classifiers Evaluation
  92. Урок 92. 00:08:31
    Training a Classifier
  93. Урок 93. 00:08:06
    Hyperparameters
  94. Урок 94. 00:03:02
    Optimizing a Model
  95. Урок 95. 00:02:34
    [Exercise] Loan Approval
  96. Урок 96. 00:02:33
    [Exercise] Load Approval - Solution
  97. Урок 97. 00:06:56
    Deep Learning
  98. Урок 98. 00:03:23
    Summary
  99. Урок 99. 00:05:07
    Introduction
  100. Урок 100. 00:06:11
    Natural Language Processing (NLP) before LLMs
  101. Урок 101. 00:06:21
    Transformers
  102. Урок 102. 00:07:40
    Types of LLMs
  103. Урок 103. 00:02:19
    Hugging Face
  104. Урок 104. 00:10:38
    Databricks Set Up
  105. Урок 105. 00:07:36
    Using an LLM
  106. Урок 106. 00:03:42
    Structured Output
  107. Урок 107. 00:05:10
    Producing JSON Output
  108. Урок 108. 00:05:20
    LLMs With Apache Spark
  109. Урок 109. 00:02:48
    Summary
  110. Урок 110. 00:06:06
    Introduction
  111. Урок 111. 00:07:00
    What Is Apache Kafka?
  112. Урок 112. 00:08:56
    Partitioning Data
  113. Урок 113. 00:07:42
    Kafka API
  114. Урок 114. 00:03:15
    Kafka Architecture
  115. Урок 115. 00:05:53
    Set Up Kafka
  116. Урок 116. 00:06:07
    Writing to Kafka
  117. Урок 117. 00:07:37
    Reading from Kafka
  118. Урок 118. 00:06:39
    Data Durability
  119. Урок 119. 00:02:11
    Kafka vs Queues
  120. Урок 120. 00:03:44
    [Exercise] Processing Records
  121. Урок 121. 00:02:59
    [Exercise] Processing Records - Solution
  122. Урок 122. 00:05:53
    Delivery Semantics
  123. Урок 123. 00:04:34
    Kafka Transactions
  124. Урок 124. 00:03:23
    Log Compaction
  125. Урок 125. 00:06:59
    Kafka Connect
  126. Урок 126. 00:09:44
    Using Kafka Connect
  127. Урок 127. 00:04:31
    Outbox Pattern
  128. Урок 128. 00:08:01
    Schema Registry
  129. Урок 129. 00:08:10
    Using Schema Registry
  130. Урок 130. 00:03:28
    Tiered Storage
  131. Урок 131. 00:04:27
    [Exercise] Track Order Status Changes
  132. Урок 132. 00:05:06
    [Exercise] Track Order Status Changes - Solution
  133. Урок 133. 00:04:41
    Summary
  134. Урок 134. 00:05:40
    Introduction
  135. Урок 135. 00:05:24
    What Is Apache Flink?
  136. Урок 136. 00:08:11
    Kafka Application
  137. Урок 137. 00:03:11
    Multiple Streams
  138. Урок 138. 00:05:46
    Installing Apache Flink
  139. Урок 139. 00:07:22
    Processing Individual Records
  140. Урок 140. 00:04:02
    [Exercise] Stream Processing
  141. Урок 141. 00:02:40
    [Exercise] Stream Processing - Solution
  142. Урок 142. 00:06:49
    Time Windows
  143. Урок 143. 00:02:40
    Keyed Windows
  144. Урок 144. 00:05:18
    Using Time Windows
  145. Урок 145. 00:10:06
    Watermarks
  146. Урок 146. 00:06:17
    Advanced Window Operations
  147. Урок 147. 00:07:50
    Stateful Stream Processing
  148. Урок 148. 00:04:42
    Using Local State
  149. Урок 149. 00:04:35
    [Exercise] Anomalies Detection
  150. Урок 150. 00:03:34
    [Exercise] Anomalies Detection - Solution
  151. Урок 151. 00:05:50
    Joining Streams
  152. Урок 152. 00:03:10
    Summary
  153. Урок 153. 00:01:18
    Thank You!