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Google Cloud Data Engineer

This learning path prepares you for a career as a Google Cloud Data Engineer. You'll start with data engineering fundamentals, then build data lakes and warehouses using BigQuery, BigLake, and Apache Iceberg. Next, you'll design both batch and streaming data pipelines with Dataflow, Serverless Spark, Pub/Sub, and Cloud Composer. Three dedicated courses cover serverless data processing with Dataflow from foundations through development and operations. You'll also learn to use Gemini AI models in BigQuery for advanced analytics and sentiment analysis. The path concludes with certification preparation for the Google Cloud Professional Data Engineer exam, featuring diagnostic questions across all key domains.

Предварительные требования

Для этого трека нет предварительных требований

Курс
1
Preparing for your Professional Data Engineer Journey
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Prepare for the Google Cloud Professional Data Engineer certification with diagnostic questions covering migration, storage, analytics, and automation.
Prepare for the Google Cloud Professional Data Engineer certification with diagnostic questions covering migration, storage, analytics, and automation.
Курс
2
Introduction to Data Engineering on Google Cloud
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Learn the data engineering role on Google Cloud. Explore data sources, storage solutions, ETL/ELT architectures, BigQuery, Dataform, and Dataproc.
Learn the data engineering role on Google Cloud. Explore data sources, storage solutions, ETL/ELT architectures, BigQuery, Dataform, and Dataproc.
Курс
3
Build Data Lakes and Data Warehouses on Google Cloud
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Build modern data lakehouses on Google Cloud using BigQuery, Cloud Storage, Apache Iceberg, BigLake, federated queries, and data governance tools.
Build modern data lakehouses on Google Cloud using BigQuery, Cloud Storage, Apache Iceberg, BigLake, federated queries, and data governance tools.
Курс
4
Build Batch Data Pipelines on Google Cloud
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Explore streaming data architectures on Google Cloud with Pub/Sub, Managed Kafka, Dataflow, and BigQuery for real-time data processing.
Explore streaming data architectures on Google Cloud with Pub/Sub, Managed Kafka, Dataflow, and BigQuery for real-time data processing.
Курс
5
Build Streaming Data Pipelines on Google Cloud
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Design and operate batch data pipelines on Google Cloud using Dataflow, Serverless Spark, Cloud Composer, and data validation techniques.
Design and operate batch data pipelines on Google Cloud using Dataflow, Serverless Spark, Cloud Composer, and data validation techniques.
Курс
6
Serverless Data Processing with Dataflow: Foundations
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Master Apache Beam and Dataflow foundations including portability, Runner v2, Shuffle Service, Streaming Engine, IAM, quotas, and security.
Master Apache Beam and Dataflow foundations including portability, Runner v2, Shuffle Service, Streaming Engine, IAM, quotas, and security.
Курс
7
Serverless Data Processing with Dataflow: Develop Pipelines
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Develop data pipelines with Apache Beam and Dataflow. Cover transforms, windowing, I/O connectors, schemas, state APIs, Beam SQL, and notebooks.
Develop data pipelines with Apache Beam and Dataflow. Cover transforms, windowing, I/O connectors, schemas, state APIs, Beam SQL, and notebooks.
Курс
8
Serverless Data Processing with Dataflow: Operations
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Operate Dataflow pipelines in production. Learn monitoring, logging, troubleshooting, performance tuning, CI/CD, reliability, and templates.
Operate Dataflow pipelines in production. Learn monitoring, logging, troubleshooting, performance tuning, CI/CD, reliability, and templates.
Курс
9
Boost Productivity with Gemini in BigQuery
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Use Gemini AI to boost your productivity in BigQuery. Explore data, accelerate code development, and discover visualization workflows.
Use Gemini AI to boost your productivity in BigQuery. Explore data, accelerate code development, and discover visualization workflows.
Курс
10
Work with Gemini Models in BigQuery
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Work with Gemini AI models in BigQuery for sentiment analysis. Analyze customer reviews using SQL and Python notebooks with Gemini.
Work with Gemini AI models in BigQuery for sentiment analysis. Analyze customer reviews using SQL and Python notebooks with Gemini.

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Google Cloud Data Engineer

Описание трека