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[FreeCourseSite.com] Udemy - Master Data Engineering using GCP Data Analytics

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FreeCourseSite com Udemy - Master Data Engineering using GCP Data Analytics

文件列表
  1. 1. Introduction to Data Engineering using GCP Data Analytics/1. Introduction to Data Engineering using GCP Data Analytics.mp421.83MB
  2. 1. Introduction to Data Engineering using GCP Data Analytics/2. Pre-requisites for Data Engineering using GCP Data Analytics.mp46.07MB
  3. 1. Introduction to Data Engineering using GCP Data Analytics/3. Highlights of the Data Engineering using GCP Data Analytics Course.mp410.37MB
  4. 1. Introduction to Data Engineering using GCP Data Analytics/4. Overview of Udemy Platform to take course effectively.mp447.27MB
  5. 1. Introduction to Data Engineering using GCP Data Analytics/5. Refund Policy and Request for Rating and Feedback.mp49.12MB
  6. 2. Setup Environment for Data Engineering using GCP Data Analytics/1. Review Data Engineering on GCP Folder.mp417.55MB
  7. 2. Setup Environment for Data Engineering using GCP Data Analytics/2. Setup VS Code Workspace for Data Engineering on GCP.mp416.49MB
  8. 2. Setup Environment for Data Engineering using GCP Data Analytics/3. Setup and Integrate Python 3.9 venv with VS Code Workspace.mp414.99MB
  9. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/1. Introduction to Getting Started with GCP.mp48.19MB
  10. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/10. Overview of Google Cloud Shell.mp417.51MB
  11. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/11. Install Google Cloud SDK on Windows.mp435.4MB
  12. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/12. Initialize gcloud CLI using GCP Project.mp425.59MB
  13. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/13. Reinitialize Google Cloud Shell with Project id.mp420.58MB
  14. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/14. Overview of Analytics Services on GCP.mp414.91MB
  15. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/15. Conclusion to Get Started with GCP for Data Engineering.mp47.85MB
  16. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/2. Pre-requisite Skills to Sign up for course on GCP Data Analytics.mp412.1MB
  17. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/3. Overview of Cloud Platforms.mp416.67MB
  18. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/4. Overview of Google Cloud Platform or GCP.mp421.93MB
  19. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/5. Overview of Signing for GCP Account.mp412.1MB
  20. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/6. Create New Google Account using Non Gmail Id.mp410.9MB
  21. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/7. Sign up for GCP using Google Account.mp415.97MB
  22. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/8. Overview of GCP Credits.mp424.3MB
  23. 3. Getting Started with GCP for Data Engineering using GCP Data Analytics/9. Overview of GCP Project and Billing.mp412.8MB
  24. 4. Setting up Data Lake using Google Cloud Storage/1. Getting Started with Google Cloud Storage or GCS.mp417.97MB
  25. 4. Setting up Data Lake using Google Cloud Storage/10. Cleanup Buckets in GCS using gsutil.mp439.02MB
  26. 4. Setting up Data Lake using Google Cloud Storage/11. Exercise to Manage Buckets and Files in GCS using gsutil.mp45.06MB
  27. 4. Setting up Data Lake using Google Cloud Storage/12. Overview of Setting up Data Lake using GCS.mp47.65MB
  28. 4. Setting up Data Lake using Google Cloud Storage/13. Setup Google Cloud Libraries in Python Virtual Environment.mp427.57MB
  29. 4. Setting up Data Lake using Google Cloud Storage/14. Setup Bucket and Files in GCS using gsutil.mp431.63MB
  30. 4. Setting up Data Lake using Google Cloud Storage/15. Getting Started to manage files in GCS using Python.mp427.25MB
  31. 4. Setting up Data Lake using Google Cloud Storage/16. Setup Credentials for Python and GCS Integration.mp416.51MB
  32. 4. Setting up Data Lake using Google Cloud Storage/17. Review Methods in Google Cloud Storage Python library.mp416.11MB
  33. 4. Setting up Data Lake using Google Cloud Storage/18. Get GCS Bucket Details using Python.mp435MB
  34. 4. Setting up Data Lake using Google Cloud Storage/19. Manage Blobs or Files in GCS using Python.mp473.98MB
  35. 4. Setting up Data Lake using Google Cloud Storage/2. Overview of Google Cloud Storage or GCS Web UI.mp427.66MB
  36. 4. Setting up Data Lake using Google Cloud Storage/20. Project Problem Statement to Manage Files in GCS using Python.mp412.38MB
  37. 4. Setting up Data Lake using Google Cloud Storage/21. Design to Upload multiple files into GCS using Python.mp420.82MB
  38. 4. Setting up Data Lake using Google Cloud Storage/22. Get File Names to upload into GCS using Python glob and os.mp415.26MB
  39. 4. Setting up Data Lake using Google Cloud Storage/23. Upload all Files to GCS as blobs using Python.mp432.72MB
  40. 4. Setting up Data Lake using Google Cloud Storage/24. Validate Files or Blobs in GCS using Python.mp434.72MB
  41. 4. Setting up Data Lake using Google Cloud Storage/25. Overview of Processing Data in GCS using Pandas.mp454.77MB
  42. 4. Setting up Data Lake using Google Cloud Storage/26. Convert Data to Parquet and Write to GCS using Pandas.mp437.31MB
  43. 4. Setting up Data Lake using Google Cloud Storage/27. Design to Upload multiple files into GCS using Pandas.mp417.63MB
  44. 4. Setting up Data Lake using Google Cloud Storage/28. Get File Names to upload into GCS using Python glob and os.mp420.54MB
  45. 4. Setting up Data Lake using Google Cloud Storage/29. Overview of Parquet File Format and Schemas JSON File.mp429.3MB
  46. 4. Setting up Data Lake using Google Cloud Storage/3. Upload Folders and Files using into GCS Bucket using GCP Web UI.mp412.43MB
  47. 4. Setting up Data Lake using Google Cloud Storage/30. Get Column Names for Dataset using Schemas JSON File.mp454.25MB
  48. 4. Setting up Data Lake using Google Cloud Storage/31. Upload all Files to GCS as Parquet using Pandas.mp447.38MB
  49. 4. Setting up Data Lake using Google Cloud Storage/32. Perform Validation of Files Copied using Pandas.mp440.81MB
  50. 4. Setting up Data Lake using Google Cloud Storage/4. Review GCS Buckets and Objects using gsutil commands.mp426.26MB
  51. 4. Setting up Data Lake using Google Cloud Storage/5. Delete GCS Bucket using Web UI.mp44.82MB
  52. 4. Setting up Data Lake using Google Cloud Storage/6. Setup Data Repository in Google Cloud Shell.mp415.74MB
  53. 4. Setting up Data Lake using Google Cloud Storage/7. Overview of Data Sets.mp423.44MB
  54. 4. Setting up Data Lake using Google Cloud Storage/8. Managing Buckets in GCS using gsutil.mp428.26MB
  55. 4. Setting up Data Lake using Google Cloud Storage/9. Copy Data Sets into GCS using gsutil.mp428.89MB
  56. 5. Setup Postgres Database using Cloud SQL/1. Overview of GCP Cloud SQL.mp416.25MB
  57. 5. Setup Postgres Database using Cloud SQL/10. Read Data From Files to Pandas Data Frame.mp459.68MB
  58. 5. Setup Postgres Database using Cloud SQL/11. Process Data using Pandas Dataframe APIs.mp430.78MB
  59. 5. Setup Postgres Database using Cloud SQL/12. Write Pandas Dataframe into Postgres Database Table.mp444.28MB
  60. 5. Setup Postgres Database using Cloud SQL/13. Validate Data in Postgres Database Tables using Pandas.mp431.74MB
  61. 5. Setup Postgres Database using Cloud SQL/14. Getting Started with Secrets using GCP Secret Manager.mp416.84MB
  62. 5. Setup Postgres Database using Cloud SQL/15. Configure Access to GCP Secret Manager via IAM Roles.mp426.03MB
  63. 5. Setup Postgres Database using Cloud SQL/16. Install Google Cloud Secret Manager Python Library.mp49.99MB
  64. 5. Setup Postgres Database using Cloud SQL/17. Get Secret Details from GCP Secret Manager using Python.mp441.56MB
  65. 5. Setup Postgres Database using Cloud SQL/18. Connect to Database using Credentials from Secret Manager.mp432.51MB
  66. 5. Setup Postgres Database using Cloud SQL/19. Stop GCP Cloud SQL Postgres Database Server.mp416.79MB
  67. 5. Setup Postgres Database using Cloud SQL/2. Setup Postgres Database Server using GCP Cloud SQL.mp418.86MB
  68. 5. Setup Postgres Database using Cloud SQL/3. Configure Network for Cloud SQL Postgres Database.mp426.12MB
  69. 5. Setup Postgres Database using Cloud SQL/4. Validate Client Tools for Postgres on Mac or PC.mp411.47MB
  70. 5. Setup Postgres Database using Cloud SQL/5. Setup Database in GCP Cloud SQL Postgres Database Server.mp424.03MB
  71. 5. Setup Postgres Database using Cloud SQL/6. Setup Tables in GCP Cloud SQL Postgres Database.mp426.56MB
  72. 5. Setup Postgres Database using Cloud SQL/7. Validate Data in GCP Cloud SQL Postgres Database Tables.mp414.75MB
  73. 5. Setup Postgres Database using Cloud SQL/8. Integration of GCP Cloud SQL Postgres with Python.mp453.87MB
  74. 5. Setup Postgres Database using Cloud SQL/9. Overview of Integration of GCP Cloud SQL Postgres with Pandas.mp433.09MB
  75. 6. Big Data Processing using Google Dataproc/1. Getting Started with GCP Dataproc.mp421.47MB
  76. 6. Big Data Processing using Google Dataproc/10. Copy Local Files into HDFS on Dataproc.mp440.24MB
  77. 6. Big Data Processing using Google Dataproc/11. Copy GCS Files into HDFS on Dataproc.cmproj.mp439.43MB
  78. 6. Big Data Processing using Google Dataproc/12. Validate Pyspark CLI in Dataproc Cluster.mp430.8MB
  79. 6. Big Data Processing using Google Dataproc/13. Validate Spark Scala CLI in Dataproc Cluster.mp426.4MB
  80. 6. Big Data Processing using Google Dataproc/14. Validate Spark SQL CLI in Dataproc Cluster.mp425.66MB
  81. 6. Big Data Processing using Google Dataproc/2. Setup Single Node Dataproc Cluster for Development.mp432.12MB
  82. 6. Big Data Processing using Google Dataproc/3. Validate SSH Connectivity to Master Node of Dataproc Cluster.mp432.93MB
  83. 6. Big Data Processing using Google Dataproc/4. Allocate Static IP to the Master Node VM of Dataproc Cluster.mp430.62MB
  84. 6. Big Data Processing using Google Dataproc/5. Setup VS Code Remote Window for Dataproc VM.mp424.83MB
  85. 6. Big Data Processing using Google Dataproc/6. Setup Workspace using VS Code on Dataproc.mp414.94MB
  86. 6. Big Data Processing using Google Dataproc/7. Getting Started with HDFS Commands on Dataproc.mp425.77MB
  87. 6. Big Data Processing using Google Dataproc/8. Recap of gsutil to manage files and folders in GCS.mp426.21MB
  88. 6. Big Data Processing using Google Dataproc/9. Review Data Sets setup on Dataproc Master Node VM.mp416.97MB
  89. 7. ELT Data Pipelines using Dataproc on GCP/1. Overview of GCP Dataproc Jobs and Workflow.mp418.56MB
  90. 7. ELT Data Pipelines using Dataproc on GCP/10. Develop Spark SQL Script to Cleanup Databases.mp428.28MB
  91. 7. ELT Data Pipelines using Dataproc on GCP/11. Copy Spark SQL Scripts to GCS.mp411MB
  92. 7. ELT Data Pipelines using Dataproc on GCP/12. Run and Validate Spark SQL Scripts in GCS.mp489.59MB
  93. 7. ELT Data Pipelines using Dataproc on GCP/13. Limitations of Running Spark SQL Scripts using Dataproc Jobs.mp425.07MB
  94. 7. ELT Data Pipelines using Dataproc on GCP/14. Manage Dataproc Clusters using gcloud Commands.mp427.31MB
  95. 7. ELT Data Pipelines using Dataproc on GCP/15. Run Dataproc Jobs using Spark SQL Command or Query.mp445.55MB
  96. 7. ELT Data Pipelines using Dataproc on GCP/16. Run Dataproc Jobs using Spark SQL Scripts.mp473.39MB
  97. 7. ELT Data Pipelines using Dataproc on GCP/17. Exercises to Run Spark SQL Scripts as Dataproc Jobs using gcloud.mp417.28MB
  98. 7. ELT Data Pipelines using Dataproc on GCP/18. Delete Dataproc Jobs using gcloud commands.mp426.57MB
  99. 7. ELT Data Pipelines using Dataproc on GCP/19. Importance of using gcloud commands to manage dataproc jobs.mp413.62MB
  100. 7. ELT Data Pipelines using Dataproc on GCP/2. Setup JSON Dataset in GCS for Dataproc Jobs.mp418.32MB
  101. 7. ELT Data Pipelines using Dataproc on GCP/20. Getting Started with Dataproc Workflow Templates using Web UI.mp446.34MB
  102. 7. ELT Data Pipelines using Dataproc on GCP/21. Review Steps and Design to create Dataproc Workflow Template.mp474.29MB
  103. 7. ELT Data Pipelines using Dataproc on GCP/22. Create Dataproc Workflow Template and Add Cluster using gcloud Commands.mp459.95MB
  104. 7. ELT Data Pipelines using Dataproc on GCP/23. Review gcloud Commands to Add Jobs to Dataproc Workflow Templates.mp474.28MB
  105. 7. ELT Data Pipelines using Dataproc on GCP/24. Add Jobs to Dataproc Workflow Template using Commands.mp443.22MB
  106. 7. ELT Data Pipelines using Dataproc on GCP/25. Instantiate Dataproc Workflow Template to run the Data Pipeline.mp436.7MB
  107. 7. ELT Data Pipelines using Dataproc on GCP/26. Overview of Dataproc Operations and Deleting Workflow Runs.mp438.71MB
  108. 7. ELT Data Pipelines using Dataproc on GCP/27. Run and Validate ELT Data Pipeline using Dataproc.mp463.78MB
  109. 7. ELT Data Pipelines using Dataproc on GCP/28. Stop Dataproc Cluster.mp49.15MB
  110. 7. ELT Data Pipelines using Dataproc on GCP/3. Review Spark SQL Commands used for Dataproc Jobs.mp461.51MB
  111. 7. ELT Data Pipelines using Dataproc on GCP/4. Run Dataproc Job using Spark SQL.mp429.89MB
  112. 7. ELT Data Pipelines using Dataproc on GCP/5. Overview of Modularizing Spark SQL Applications for Dataproc.mp428.37MB
  113. 7. ELT Data Pipelines using Dataproc on GCP/6. Review Spark SQL Scripts for Dataproc Jobs and Workflows.mp429.29MB
  114. 7. ELT Data Pipelines using Dataproc on GCP/7. Validate Spark SQL Script for File Format Conversion.mp461.93MB
  115. 7. ELT Data Pipelines using Dataproc on GCP/8. Exercise to convert file format using Spark SQL Script.mp420.74MB
  116. 7. ELT Data Pipelines using Dataproc on GCP/9. Validate Spark SQL Script for Daily Product Revenue.mp444.69MB
  117. 8. Big Data Processing using Databricks on GCP/1. Signing up for Databricks on GCP.mp419.24MB
  118. 8. Big Data Processing using Databricks on GCP/10. Upload Data Set into DBFS using GCS Web UI.mp421.21MB
  119. 8. Big Data Processing using Databricks on GCP/11. Copy Data Set into DBFS using gsutil.mp415.41MB
  120. 8. Big Data Processing using Databricks on GCP/12. Process Data in DBFS using Databricks Spark SQL.mp425.79MB
  121. 8. Big Data Processing using Databricks on GCP/13. Getting Started with Spark SQL Example using Databricks.mp432.71MB
  122. 8. Big Data Processing using Databricks on GCP/14. Create Temporary Views using Spark SQL.mp436.4MB
  123. 8. Big Data Processing using Databricks on GCP/15. Exercise to create temporary views using Spark SQL.mp49.11MB
  124. 8. Big Data Processing using Databricks on GCP/16. Spark SQL Query to compute Daily Product Revenue.mp435.36MB
  125. 8. Big Data Processing using Databricks on GCP/17. Save Query Result to DBFS using Spark SQL.mp426.53MB
  126. 8. Big Data Processing using Databricks on GCP/18. Overview of Pyspark Examples on Databricks.cmproj.mp45.85MB
  127. 8. Big Data Processing using Databricks on GCP/19. Process Schema Details in JSON using Pyspark.mp446.27MB
  128. 8. Big Data Processing using Databricks on GCP/2. Create Databricks Workspace on GCP.mp419.54MB
  129. 8. Big Data Processing using Databricks on GCP/20. Create Dataframe with Schema from JSON File using Pyspark.mp445.19MB
  130. 8. Big Data Processing using Databricks on GCP/21. Transform Data using Spark APIs.mp422.67MB
  131. 8. Big Data Processing using Databricks on GCP/22. Get Schema Details for all Data Sets using Pyspark.mp421.2MB
  132. 8. Big Data Processing using Databricks on GCP/23. Convert CSV to Parquet with Schema using Pyspark.mp435.95MB
  133. 8. Big Data Processing using Databricks on GCP/3. Getting Started with Databricks Clusters on GCP.mp413.69MB
  134. 8. Big Data Processing using Databricks on GCP/4. Getting Started with Databricks Notebook.mp412.59MB
  135. 8. Big Data Processing using Databricks on GCP/5. Overview of Databricks on GCP.mp436.77MB
  136. 8. Big Data Processing using Databricks on GCP/6. Overview of Databricks CLI Commands.mp425.02MB
  137. 8. Big Data Processing using Databricks on GCP/7. Limitations of Managing DBFS using Databricks CLI.mp419.67MB
  138. 8. Big Data Processing using Databricks on GCP/8. Overview of Copying Data Sets into DBFS on GCS.mp416.59MB
  139. 8. Big Data Processing using Databricks on GCP/9. Create Folder in GCS using DBFS Commands.mp427.5MB
  140. 9. ELT Data Pipelines using Databricks on GCP/1. Overview of Databricks Workflows.mp418.22MB
  141. 9. ELT Data Pipelines using Databricks on GCP/10. Review Databricks SQL Notebooks for Tables and Final Results.mp419.09MB
  142. 9. ELT Data Pipelines using Databricks on GCP/11. Validate Applications for ELT Pipeline using Databricks.mp448.03MB
  143. 9. ELT Data Pipelines using Databricks on GCP/12. Build ELT Pipeline using Databricks Job in Workflows.mp449.96MB
  144. 9. ELT Data Pipelines using Databricks on GCP/13. Run and Review Execution details of ELT Data Pipeline using Databricks Job.mp428.67MB
  145. 9. ELT Data Pipelines using Databricks on GCP/2. Pass Arguments to Databricks Python Notebooks.mp413.87MB
  146. 9. ELT Data Pipelines using Databricks on GCP/3. Pass Arguments to Databricks SQL Notebooks.mp413.87MB
  147. 9. ELT Data Pipelines using Databricks on GCP/4. Create and Run First Databricks Job.mp432.35MB
  148. 9. ELT Data Pipelines using Databricks on GCP/5. Run Databricks Jobs and Tasks with Parameters.mp429.11MB
  149. 9. ELT Data Pipelines using Databricks on GCP/6. Create and Run Orchestrated Pipeline using Databricks Job.mp439.07MB
  150. 9. ELT Data Pipelines using Databricks on GCP/7. Import ELT Data Pipeline Applications into Databricks Environment.mp414.64MB
  151. 9. ELT Data Pipelines using Databricks on GCP/8. Spark SQL Application to Cleanup Database and Datasets.mp416.09MB
  152. 9. ELT Data Pipelines using Databricks on GCP/9. Review File Format Converter Pyspark Code.mp430.17MB
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