Table of contents
Introduction
3m 51s
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Course Introduction | 2m 3s
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Environment Setup | 1m 48s
Big Data Fundamentals
56m 42s
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Introduction: Big Data Fundamentals | 44s
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What Is Big Data and Why Does It Matter? | 8m 22s
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The Data Landscape on GCP | 10m 20s
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Working with Public Datasets | 5m 11s
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Recap: Big Data Fundamentals | 2m 5s
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Big Data FoundationsBig Data Foundations | 30m
Building a Data Lake
1h 48m 14s
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Introduction: Building a Data Lake | 1m
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What Is a Data Lake? | 6m 11s
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Data Storage Options on GCP | 10m 12s
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ETL Options on GCP | 6m 29s
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Building a Data Lake with Cloud Storage | 12m 20s
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Extending the Data Lake with Cloud SQL | 8m 1s
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Recap: Building a Data Lake | 4m 1s
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Building a Data LakeBuilding a Data Lake | 30m
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Setting Cloud Storage Lifecycle RulesSetting Cloud Storage Lifecycle Rules | 30m
Streaming Data in Google Cloud
1h 47m 46s
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Introduction: Streaming Data in Google Cloud | 47s
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Ingesting Data with Pub/Sub | 7m 32s
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Demo: Ingesting Data with Pub/Sub | 6m 21s
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Streaming Data with Apache Beam | 8m 26s
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Streaming Data with Dataflow | 7m 37s
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Recap: Streaming Data on GCP | 2m 3s
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Streaming Data in Google CloudStreaming Data in Google Cloud | 30m
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Setting up a Data Streaming Pipeline with DataflowSetting Up a Data Streaming Pipeline with Dataflow | 45m
Processing Data for Analytics
1h 51m 19s
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Introduction: Processing Data for Analytics | 36s
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Prepare and Process Data with Dataprep | 13m 20s
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Run Apache Spark Jobs with Dataproc | 10m 20s
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Data Processing with Dataflow | 9m 47s
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Recap: Processing Data for Analytics | 2m 16s
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Processing Data for AnalyticsProcessing Data for Analytics | 30m
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Running a Spark Job on a Dataproc ClusterRunning a Spark Job on a Dataproc Cluster | 45m
BigQuery, the Modern Data Warehouse
2h 32m 29s
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Introduction: BigQuery, the Modern Data Warehouse | 57s
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Introduction to BigQuery | 8m 14s
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Loading Data into BigQuery | 7m 15s
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Working with Data Schemas and Sessions | 7m 11s
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Working with Views and Materialized Views | 5m 42s
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Working with Geospatial Analytics | 6m 2s
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Partitioning and Clustering in BigQuery | 8m 2s
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Introduction to BigQuery ML | 7m 36s
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Unifying Data Lakes and Warehouses with BigLake | 6m 35s
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Recap: BigQuery, the Modern Data Warehouse | 4m 55s
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BigQuery, the Modern Data WarehouseBigQuery, the Modern Data Warehouse | 45m
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Predicting with BigQuery MLPredicting with BigQuery ML | 45m
Visualizing Data with Looker and Data Studio
37m 51s
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Introduction: Visualizing Data with Looker and Data Studio | 35s
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Introduction to Looker and Data Studio | 6m 10s
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Visualize BigQuery Data | 4m 58s
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Create Interactive Reports in Data Studio | 9m 4s
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Recap: Visualizing Data with Looker and Data Studio | 2m 4s
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Visualizing Data with Looker and Data StudioVisualizing Data with Looker and Data Studio | 15m
Gaining Insights with AI
1h 22m 27s
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Introduction: Gaining Insights with AI | 31s
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A (Short) Introduction to Vertex AI | 8m 25s
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API Services | 6m 54s
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Demo: API Services | 5m 5s
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Recap: Gaining Insights with AI | 1m 32s
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Gaining Insights with AIGaining Insights with AI | 15m
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Classifying Content with Natural Language APIClassifying Content with the Natural Language API | 45m
Conclusion
2m 46s
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Course Summary | 1m 24s
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Conclusion and What’s Next | 1m 22s
About the author
Victor Dantas
IT professional with a PhD in Software-Defined Networking and certified Cloud Architect on Microsoft Azure and Google Cloud Platform (GCP). With a background ranging from software development and systems engineering to professional services for Cloud migration, I have accumulated experience in Hybrid cloud infrastructure, cloud architecture and design, Infrastructure-as-Code (IaC) as well as Agile/DevOps practices. My current interests involve AI / Machine Learning and Internet-of-Things. (IoT).
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