GCP Data Engineer Training

About Course

Google Cloud Platform (GCP) Data Engineer Training is a comprehensive program designed to equip individuals with the skills and knowledge needed to excel in data engineering roles using GCP’s powerful tools and services. This training covers key topics such as data ingestion, storage, transformation, and analysis, utilizing services like BigQuery, Dataflow, and Cloud Storage. Participants gain hands-on experience in building scalable data pipelines and mastering data warehousing solutions. With a focus on real-world scenarios and best practices, this training empowers data professionals to harness the full potential of GCP for managing and analyzing data, making them valuable assets in the evolving world of data engineering.

Course Content

Module 1: Designing data processing systems

1.1 Designing for security and compliance. Considerations include:
1.2 Designing for reliability and fidelity. Considerations include:
1.3 Designing for flexibility and portability. Considerations include:
1.4 Designing data migrations. Considerations include:

GCP Data Engineer Training

About Course

What Will You Learn?

Course Content

Module 1: Designing data processing systems

1.1 Designing for security and compliance. Considerations include:

1.2 Designing for reliability and fidelity. Considerations include:

1.3 Designing for flexibility and portability. Considerations include:

1.4 Designing data migrations. Considerations include:

Module 2: Ingesting and processing the data

2.1 Planning the data pipelines. Considerations include:

2.2 Building the pipelines. Considerations include:

2.3 Deploying and operationalizing the pipelines. Considerations include:

Module 3: Storing the data

3.1 Selecting storage systems. Considerations include:

3.2 Planning for using a data warehouse. Considerations include:

3.3 Using a data lake. Considerations include:

3.4 Designing for a data mesh. Considerations include:

Module 4: Preparing and using data for analysis

4.1 Preparing data for visualization. Considerations include:

4.2 Sharing data. Considerations include:

4.3 Exploring and analyzing data. Considerations include:

Module 5: Maintaining and automating data workloads

5.1 Optimizing resources. Considerations include:

5.2 Designing automation and repeatability. Considerations include:

5.3 Organizing workloads based on business requirements. Considerations include:

5.4 Monitoring and troubleshooting processes. Considerations include:

5.5 Maintaining awareness of failures and mitigating impact. Considerations include: