Data Science on Google Cloud Platform: Designing Data Warehouses

Description

In this course, you will :

• Demonstrates how to use GCP to design and build data warehouses.
• Examine the various storage options available in GCP for files, relational data, documents, and big data, such as Cloud SQL, Cloud Bigtable, and Cloud BigQuery.
• Learn how to use BigQuery, a single solution, to perform data storage and query operations, as well as advanced use cases like working with partition tables and external data sources.
• Learn best practises for table design, storage and query optimization, and data warehouse monitoring in BigQuery.

Syllabus :

1. Storing Data in GCP

• GCP storage options
• Google Cloud Storage
• Cloud SQL
• Cloud Spanner
• Cloud Bigtable
• Cloud Datastore
• Cloud BigQuery

 

2. BigQuery Data Creation

• Intro to BigQuery
• Projects and datasets
• Tables
• Create a dataset
• Create a table with schema
• Create a table from CSV
• Load data from Cloud Storage

 

3. Querying Data in BigQuery

• Simple queries
• Filter data
• SQL functions
• Regular expressions
• Grouping and aggregations
• Joins and sub-queries
• Update data

 

4. Advanced BigQuery

• Partition tables
• External data sources
• Create views
• Create labels
• Google Cloud shell
• Other interfaces

 

5. Best Practices in BigQuery

• Table design considerations
• Optimize storage
• Load data
• Speed up queries
• Monitoring and logging

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