Data Science on Google Cloud Platform: Designing Data Warehouses
1hIntermediate2018-09-07
Authors
Kumaran Ponnambalam
Working with data for 20+ years
Course details
Cloud computing brings unlimited scalability and elasticity to data science applications. Expertise in the major platforms, such as Google Cloud Platform (GCP), is essential to the IT professional. This course—one of a series by veteran cloud engineering specialist and data scientist Kumaran Ponnambalam—shows how to design and build data warehouses using GCP. Explore the different types of storage options available in GCP for files, relational data, documents, and big data, including Cloud SQL, Cloud Bigtable, and Cloud BigQuery. Then learn how to use one solution, BigQuery, to perform data storage and query operations, and review advanced use cases, such as working with partition tables and external data sources. Finally, learn best practices for table design, storage and query optimization, and monitoring of data warehouses in BigQuery.
Learning objectives
Options for storing data in Google Cloud Platform
Creating data assets in BigQuery
Querying data in BigQuery
Advanced data warehouse techniques
Best practices for data warehouses in GCP
Learning objectives
Options for storing data in Google Cloud Platform
Creating data assets in BigQuery
Querying data in BigQuery
Advanced data warehouse techniques
Best practices for data warehouses in GCP
Skills covered
Google CloudSQLData EngineeringSoftware Development ToolsGoogleCloud PlatformsCloud ComputingData ScienceOpen SourceSoftware DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Why data warehouses are important
- 02 - Data science modules covered
1. Storing Data in GCP
- 03 - GCP storage options
- 04 - Google Cloud Storage
- 05 - Cloud SQL
- 06 - Cloud Spanner
- 07 - Cloud Bigtable
- 08 - Cloud Datastore
- 09 - Cloud BigQuery
2. BigQuery Data Creation
- 10 - Intro to BigQuery
- 11 - Projects and datasets
- 12 - Tables
- 13 - Create a dataset
- 14 - Create a table with schema
- 15 - Create a table from CSV
- 16 - Load data from Cloud Storage
3. Querying Data in BigQuery
- 17 - Simple queries
- 18 - Filter data
- 19 - SQL functions
- 20 - Regular expressions
- 21 - Grouping and aggregations
- 22 - Joins and sub-queries
- 23 - Update data
4. Advanced BigQuery
- 24 - Partition tables
- 25 - External data sources
- 26 - Create views
- 27 - Create labels
- 28 - Google Cloud shell
- 29 - Other interfaces
5. Best Practices in BigQuery
- 30 - Table design considerations
- 31 - Optimize storage
- 32 - Load data
- 33 - Speed up queries
- 34 - Monitoring and logging
Conclusion
- 35 - Next steps