Course Overview of Migrating Teradata Users to BigQuery

This course enables learners to translate various concepts from Teradata to analogous concepts in BigQuery. It provides a solid foundation for the journey from Teradata to BigQuery by comparing architectures, resource configurations, data types, and SQL dialects

After completing Migrating Teradata Users to BigQuery, students will be able to:

  • Compare architecture and provisioning of resources in Teradata and BigQuery.
  • Configure datasets and tables in BigQuery.
  • Map and compare data types and optimize schemas from Teradata to BigQuery.
  • Translate SQL dialects, including SELECT, DML, and DDL statements.

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Key Features of Migrating Teradata Users to BigQuery

  • Architecture Analysis: Understand the separation of compute and storage and the concept of BigQuery Slots. 

  • Hands-On Learning: Includes 3 dedicated labs for provisioning resources, schema migration, and writing SQL. 

  • Comprehensive Mapping: Detailed modules for data types, resource hierarchies, and schema optimization (partitioning and clustering). 

  • SQL Modernization: Learn to write user-defined functions (UDFs) and procedures in BigQuery SQL.

  • Structured Modules: 5 distinct modules covering the end-to-end conceptual migration path. 

Who Should Attend Migrating Teradata Users to BigQuery?

  • Teradata Users: Individuals with experience using Teradata as a data warehouse for managing data and performing SQL analysis.
  • Aspiring BigQuery Users: While basic experience with BigQuery is recommended, it is not a strict requirement for enrollment.
  • Customers: The primary group intended for this training.

Prerequisites of Migrating Teradata Users to BigQuery

  • Experience using Teradata as a data warehouse for managing data and performing SQL analysis.
  • Basic experience with BigQuery is recommended but not required.

    • Why choose CloudThat as your Migrating Teradata Users to BigQuery?

    • Specialized GCP Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, use cases, best practices, and hands-on experience tailored specifically for GCP. 
    • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios. 
    • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set. 
    • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills. 
    • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course. 
    • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals. 
    • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge. 
    • Positive Reviews and Testimonials: Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training. 

    Learning Objectives of Migrating Teradata Users to BigQuery

    • Architectural Comparison: Compare the high-level architecture and provisioning of resources between Teradata and BigQuery.  
    • BigQuery Slot Mechanics: Describe the concept of a "slot" and its role in BigQuery.
    • Resource Management: Understand the resource hierarchy within BigQuery and learn how to configure datasets and tables.
    • Data Translation: Map and compare Teradata data types to analogous types in BigQuery, including identifying data types unique to BigQuery.  
    • Schema Optimization: Define schemas in BigQuery and implement performance-enhancing features such as partitioning and clustering.  
    • SQL Dialect Mastery: Understand BigQuery SQL query capabilities and learn to translate SELECT, DML, and DDL statements from Teradata.  
    • Programmable Logic: Write user-defined functions (UDFs) and procedures using BigQuery SQL. 

    Course Outline for Migrating Teradata Users to BigQuery Download Course Outline

    Lecture Content

    • Quick architectural reminder of Teradata's shared-nothing MPP architecture (BYNET, Access Module Processors (AMPs), and vprocs)
    • High-level overview of BigQuery’s cloud-native, serverless design
    • Deep dive into the physical and logical separation of compute (Dremel execution engine) and storage (Colossus distributed filesystem)
    • Understanding BigQuery Slots as units of computational capacity (CPU and RAM)
    • Workload management strategies: On-demand vs. Capacity management slot allocation (Standard, Enterprise, Enterprise Plus tiers)

    Learning Objectives

    • Compare resource provisioning, concurrency management, and hardware scaling boundaries between Teradata's fixed-node capacity and BigQuery's elastic serverless pool
    • Define the exact structural concept of a "slot" and evaluate strategies for allocating slot capacity to project workloads

    Lecture Content

    • Mapping corporate resource hierarchies: Teradata (System > Databases > Users/Tables) to Google Cloud (Organization > Folders > Projects > Datasets > Tables)
    • Provisioning dataset containers, configuring geographical locations (multi-region vs. single-region), and setting table expiration timelines
    • Implementing access governance: Sharing datasets and resources using Cloud IAM roles, authorized views, and dataset-level permissions

    Learning Objectives

    • Design a secure, multi-tenant enterprise data resource hierarchy optimized for Google Cloud architectures
    • Create and configure target BigQuery datasets while applying strict identity governance rules

    Lab Content

    • Provisioning and Managing Resources in BigQuery

    Lecture Content

    • Systematic mapping of specific Teradata data types (e.g., BYTEINT, PERIOD, BYTE/VARBYTE) to BigQuery equivalents
    • Handling floating-point, variable-precision numbers, and strict boolean fields across systems
    • Exploring data types unique to the BigQuery environment (e.g., GEOGRAPHY, BIGNUMERIC, and native structural JSON attributes)
    • Managing structural casting anomalies and parsing semi-structured records cleanly

    Learning Objectives

    • Correlate structural Teradata schemas into fully compliant BigQuery table data types
    • Identify and utilize unique BigQuery-native data types to simplify layout complexities

    Lecture Content

    • Translating Teradata Primary Indexes (UPI and NUPI) into native BigQuery performance tuning mechanisms
    • Implementing explicit data partitioning strategies (Time-unit, ingestion time, or integer range partitioning)
    • Using multi-field table clustering to systematically sort, filter, and optimize high-volume data blocks
    • Best practices for nested and repeated fields (STRUCTs and ARRAYs) to eliminate unnecessary JOIN performance overhead

    Learning Objectives

    • Define, construct, and implement optimal table layout configurations via partitioning and clustering
    • Build highly performant target data structures that dramatically minimize bytes scanned and execution bills

    Lab Content

    • Schema Migration to BigQuery

    Lecture Content

    • Translating structural Teradata SQL text dialects (BTEQ scripts, Teradata-specific extensions) into standard GoogleSQL compliance layers
    • Mapping DDL (Data Definition Language) and DML (Data Manipulation Language) syntax variances (e.g., handling QUALIFY, SAMPLE, and explicit transaction modes)
    • Rewriting complex analytic clauses, macro definitions, and system metrics queries
    • Migrating programming modules: Porting Teradata User-Defined Functions (UDFs) and stored procedures into GoogleSQL equivalents

    Learning Objectives

    • Translate legacy SELECT, DDL, and DML syntax from Teradata code into valid GoogleSQL structures
    • Rewrite custom business database logic blocks, macros, stored procedures, and programmatic functions safely inside BigQuery

    Lab Content

    • Writing SQL for BigQuery

    Certification Details of Migrating Teradata Users to BigQuery

      CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

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    FAQs for Migrating Teradata Users to BigQuery

    It is intended for customers migrating from Teradata to BigQuery.

    No, it provides a foundation but is not a replacement for detailed BigQuery technical documentation.

    Yes, there are 3 hands-on labs included. 

    The primary focus is on BigQuery.

    Enquire Now