Data Engineering
Migrating Teradata Users to BigQuery
Master the transition from Teradata to BigQuery with this specialized course. Gain the skills to navigate both architectures, configure datasets, optimize schemas, and map users seamlessly to BigQuery for efficient data operations
1 Days
Target Audience
Perfect for Teradata users ready to make the leap to BigQuery! This course is tailored for data analysts, engineers, scientists, and developers eager to expand their expertise in modern data platforms
What you'll learn
Architecture & Resources:
Compare Teradata and BigQuery architectures and provisioning.
Schema & Data Mapping:
Optimize schemas and map Teradata data types to BigQuery.
SQL Translation:
Convert Teradata SQL into BigQuery-compatible queries.
COURSE AGENDA
Understanding BigQuery Architecture
- Gain a quick refresher on Teradata architecture.
- Explore the core architecture of BigQuery.
- Learn about the separation of compute and storage in BigQuery.
- Understand BigQuery Slots and how they influence performance.
- Dive into workload management strategies for BigQuery.
Creating Datasets & Tables in BigQuery
- Compare the resource hierarchies of Teradata and BigQuery.
- Learn how to create and manage resources in BigQuery.
- Understand sharing resources effectively within BigQuery.
- Lab: Practice provisioning and managing resources in BigQuery.
Mapping Data Types
- Map Teradata data types to BigQuery equivalents.
- Understand and utilize data types unique to BigQuery.
Schema Mapping & Optimization
- Define schemas in BigQuery to align with data requirements.
- Learn partitioning and clustering techniques in BigQuery for optimized performance.Lab: Practice schema migration from
- Teradata to BigQuery.
SQL Translation from Teradata to BigQuery
Translate SQL statements from Teradata to BigQuery:
- SELECT statements.
- DML (Data Manipulation Language) statements.
- DDL (Data Definition Language) statements.
- UDFs (User-Defined Functions) and Procedures.
- Lab: Write and optimize SQL for BigQuery.
Modern Application Development
Deploy with Cloud Run:
Launch serverless apps that scale automatically.
Build with Cloud Functions:
Create event-driven serverless applications.
Containerize & Scale:
Deploy containerized apps using GKE and Cloud Run.
Comprehensive Review
Consolidate Learning:
Review key Google Cloud concepts for a solid foundation.
Connect Concepts:
Understand how modules apply to real-world scenarios.
Prepare for Implementation:
Apply course knowledge to practical Google Cloud solutions.
Cloud Functions Core
- Deploy serverless solutions
- Implement function triggers
- Secure function access
Advanced Functions
- Integrate with databases
- Manage secrets and variables
- Implement best practices
Workflow Automation
- Design serverless workflows
- Implement logic controls
- Master service authentication
- Build automated processes