What you’ll learn
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Data Engineering leveraging Services under GCP Data Analytics
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Setup Development Environment using Visual Studio Code on Windows
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Building Data Lake using GCS
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Process Data in the Data Lake using Python and Pandas
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Build Data Warehouse using Google BigQuery
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Loading Data into Google BigQuery tables using Python and Pandas
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Setup Development Environment using Visual Studio Code on Google Dataproc with Remote Connection
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Big Data Processing or Data Engineering using Google Dataproc
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Run Spark SQL based applications as Dataproc Jobs using Commands
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Build Spark SQL based ELT Data Pipelines using Google Dataproc Workflow Templates
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Run or Instantiate ELT Data Pipelines or Dataproc Workflow Template using gcloud dataproc commands
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Big Data Processing or Data Engineering using Databricks on GCP
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Integration of GCS and Databricks on GCP
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Build and Run Spark based ELT Data Pipelines using Databricks Workflows on GCP
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Integration of Spark on Dataproc with Google BigQuery
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Build and Run Spark based ELT Pipeline using Google Dataproc Workflow Template with BigQuery Integration
Who this course is for:
- Beginner or Intermediate Data Engineers who want to learn GCP Analytics Services for Data Engineering
- Intermediate Application Engineers who want to explore Data Engineering using GCP Analytics Services
- Data and Analytics Engineers who want to learn Data Engineering using GCP Analytics Services
- Testers who want to learn key skills to test Data Engineering applications built using GCP Analytics Services
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