Description
Introduction
Teradata Analytics, AI & Advanced Data Engineering training covers enterprise analytics, artificial intelligence, machine learning, advanced data processing, and modern data engineering using Teradata Vantage. Participants learn to develop analytical solutions, build scalable data pipelines, integrate AI workflows, and manage enterprise data environments.
Prerequisites
- Basic understanding of relational database concepts and SQL.
- Familiarity with Teradata Vantage or another enterprise database platform.
- Knowledge of data warehousing and analytical concepts.
- Basic understanding of Python or another programming language.
- Awareness of statistics, data analysis, and business intelligence concepts.
- Familiarity with cloud computing and distributed data processing is beneficial.
Estimated Duration
- Beginner Level: 30–35 Hours
- Intermediate Level: 40–45 Hours
- Advanced Level: 50–55 Hours
- Enterprise Level: 60–70 Hours
Training Lab Setup Specifications
- Teradata Vantage environment or authorized Teradata cloud instance.
- Teradata Studio, Teradata SQL Assistant, or compatible SQL development tools.
- Python environment with Jupyter Notebook.
- Required Teradata Python libraries and database connectivity drivers.
- Sample enterprise datasets for analytics and data engineering exercises.
- Data visualization and business intelligence tools.
- Machine learning libraries for model development and experimentation.
- Cloud storage and data integration environment for pipeline exercises.
- Appropriate database permissions for development and analytical operations.
- Stable internet connectivity and sufficient system resources for practical labs.
TABLE OF CONTENTS
Module 1: Introduction to Teradata Analytics & Advanced Data Engineering
1.1 Overview of Teradata Analytics
1.1.1 Introduction to Teradata Vantage Analytics
1.1.2 Enterprise Analytics Architecture and Capabilities
1.1.3 Analytical Workloads and Business Use Cases
1.1.4 Role of Teradata in Modern Data Platforms
1.2 Teradata Data Engineering Architecture
1.2.1 Massively Parallel Processing Architecture
1.2.2 Data Distribution and Parallel Execution
1.2.3 Storage, Compute, and Query Processing
1.2.4 Enterprise Data Engineering Workflows
1.3 Modern Analytics and AI Ecosystem
1.3.1 Descriptive, Diagnostic, Predictive, and Prescriptive Analytics
1.3.2 Artificial Intelligence and Machine Learning Overview
1.3.3 Advanced Analytics Use Cases
1.3.4 Data Engineering and AI Integration
Module 2: Advanced Teradata SQL for Analytical Processing
2.1 Advanced SQL Query Development
2.1.1 Complex Joins and Multi-Table Queries
2.1.2 Common Table Expressions and Recursive Queries
2.1.3 Subqueries and Correlated Queries
2.1.4 Set Operations and Conditional Expressions
2.2 Analytical SQL Functions
2.2.1 Aggregate and Statistical Functions
2.2.2 Window Functions and Ranking Operations
2.2.3 Partitioning and Ordering in Analytical Queries
2.2.4 Advanced Time-Series SQL Operations
2.3 Advanced SQL Performance Techniques
2.3.1 Query Execution Plans and Explain Statements
2.3.2 Join Strategies and Data Redistribution
2.3.3 Statistics Collection and Query Optimization
2.3.4 Analytical Query Performance Tuning
Module 3: Enterprise Data Warehousing & Analytical Modeling
3.1 Advanced Data Warehouse Architecture
3.1.1 Enterprise Data Warehouse Concepts
3.1.2 Centralized and Distributed Data Warehouse Models
3.1.3 Data Warehouse Layers and Architecture
3.1.4 Teradata Data Warehouse Implementation
3.2 Analytical Data Modeling
3.2.1 Dimensional Modeling Principles
3.2.2 Star and Snowflake Schema Design
3.2.3 Fact Tables, Dimension Tables, and Measures
3.2.4 Slowly Changing Dimensions and Historical Data
3.3 Enterprise Data Integration Models
3.3.1 Data Integration and Transformation Architecture
3.3.2 Data Marts and Subject-Oriented Data Models
3.3.3 Metadata Management and Data Lineage
3.3.4 Analytical Data Model Validation
Module 4: Teradata Data Integration, ETL & ELT Engineering
4.1 Enterprise Data Integration
4.1.1 Data Integration Architecture and Design
4.1.2 Batch and Real-Time Data Integration
4.1.3 Source-to-Target Data Mapping
4.2 ETL and ELT Development
4.2.1 ETL and ELT Architecture in Teradata
4.2.2 Data Transformation and Cleansing
4.2.3 Incremental and Full Data Loading
4.3 Advanced Data Loading Utilities
4.3.1 Teradata FastLoad and MultiLoad Concepts
4.3.2 Teradata FastExport and TPT Fundamentals
4.3.3 Bulk Data Loading and Export Operations
Module 5: Python Programming & Teradata Analytics Integration
5.1 Python for Data Engineering
5.1.1 Python Fundamentals for Data Processing
5.1.2 Data Structures and Functional Programming
5.1.3 Data Manipulation Using Pandas and NumPy
5.1.4 Working with Structured and Semi-Structured Data
5.2 Python and Teradata Connectivity
5.2.1 Teradata Python Connectivity and Drivers
5.2.2 Connecting Python Applications to Teradata
5.2.3 Executing SQL Queries from Python
5.2.4 Data Transfer Between Python and Teradata
5.3 Python-Based Analytical Workflows
5.3.1 Data Preparation and Feature Engineering
5.3.2 Exploratory Data Analysis
5.3.3 Automating Analytical Workflows
5.3.4 Python-Based Reporting and Visualization
Module 6: Advanced Analytics & Statistical Computing
6.1 Statistical Analysis with Teradata
6.1.1 Descriptive and Inferential Statistics
6.1.2 Probability Distributions and Sampling
6.1.3 Correlation and Regression Analysis
6.2 Advanced Analytical Techniques
6.2.1 Time-Series Analysis and Forecasting
6.2.2 Segmentation and Clustering Analysis
6.2.3 Classification and Predictive Analytics
6.3 Enterprise Analytical Applications
6.3.1 Customer and Business Performance Analytics
6.3.2 Financial and Operational Analytics
6.3.3 Risk Analysis and Fraud Detection
Module 7: Artificial Intelligence & Machine Learning with Teradata
7.1 AI and Machine Learning Fundamentals
7.1.1 Introduction to AI and Machine Learning
7.1.2 Supervised and Unsupervised Learning
7.1.3 Machine Learning Lifecycle and Workflows
7.2 Machine Learning Model Development
7.2.1 Data Preparation and Feature Selection
7.2.2 Regression and Classification Models
7.2.3 Clustering and Recommendation Techniques
7.2.4 Model Training and Validation
7.3 Teradata AI and ML Integration
7.3.1 In-Database Analytics and Model Execution
7.3.2 Teradata Machine Learning Capabilities
7.3.3 Python-Based Machine Learning Integration
7.3.4 Model Deployment and Performance Evaluation
Module 8: Advanced Data Engineering & Big Data Processing
8.1 Advanced Data Pipeline Architecture
8.1.1 Enterprise Data Pipeline Design
8.1.2 Batch and Streaming Data Processing
8.1.3 Distributed Data Processing Concepts
8.1.4 Pipeline Orchestration and Dependency Management
8.2 Big Data Integration and Processing
8.2.1 Big Data Architecture and Ecosystem Integration
8.2.2 Structured, Semi-Structured, and Unstructured Data
8.2.3 JSON and Other Complex Data Type Processing
8.2.4 Large-Scale Data Transformation Techniques
8.3 Data Pipeline Reliability and Optimization
8.3.1 Pipeline Monitoring and Error Management
8.3.2 Data Quality Checks and Validation
8.3.3 Pipeline Scheduling and Automation
Module 9: Cloud Analytics & Teradata Vantage Integration
9.1 Teradata Cloud Architecture
9.1.1 Introduction to Teradata VantageCloud
9.1.2 Cloud Data Warehouse Architecture
9.1.3 Cloud Storage and Compute Integration
9.1.4 Cloud Deployment and Resource Management
9.2 Multi-Cloud Data Engineering
9.2.1 AWS Integration with Teradata
9.2.2 Microsoft Azure Integration with Teradata
9.2.3 Google Cloud Integration with Teradata
9.3 Cloud-Based Analytics and AI
9.3.1 Cloud Analytical Workload Management
9.3.2 Cloud Data Pipeline Development
9.3.3 AI and Machine Learning in Cloud Environments
9.3.4 Cloud Analytics Security and Governance
Module 10: Business Intelligence, Visualization & Advanced Reporting
10.1 Business Intelligence Integration
10.1.1 Business Intelligence Architecture
10.1.2 Connecting Teradata to BI Platforms
10.1.3 Analytical Data Preparation for Reporting
10.1.4 Enterprise Reporting Requirements
10.2 Data Visualization and Dashboard Development
10.2.1 Data Visualization Principles
10.2.2 Dashboard Design and Development
10.2.3 KPI and Business Metrics Development
10.3 Advanced Reporting and Analytics
10.3.1 Self-Service Analytics and Reporting
10.3.2 Operational and Strategic Reporting
10.3.3 Data Storytelling and Business Insights
10.3.4 Reporting Performance and Optimization
Module 11: Data Governance, Security & Advanced Platform Operations
11.1 Enterprise Data Governance
11.1.1 Data Governance Frameworks and Policies
11.1.2 Data Quality and Data Management
11.1.3 Metadata, Data Catalogs, and Data Lineage
11.2 Data Security and Compliance
11.2.1 Teradata User and Role Management
11.2.2 Access Control and Data Authorization
11.2.3 Data Encryption and Sensitive Data Protection
11.2.4 Regulatory Compliance and Audit Management
11.3 Advanced Analytics Platform Management
11.3.1 Analytical Workload Monitoring
11.3.2 Resource Management and Workload Optimization
11.3.3 Platform Reliability and Availability
Module 12: Advanced Analytics Labs & Enterprise Capstone Project
12.1 Teradata SQL and Analytical Development Lab
12.1.1 Developing Advanced Analytical SQL Queries
12.1.2 Building Analytical Data Models
12.1.3 Optimizing Complex Analytical Workloads
12.1.4 Validating Analytical Results
12.2 AI and Machine Learning Practical Lab
12.2.1 Preparing Enterprise Data for Machine Learning
12.2.2 Developing Predictive Analytics Models
12.2.3 Integrating Python with Teradata
12.2.4 Evaluating Model Performance
12.3 Advanced Data Engineering Project
12.3.1 Designing an Enterprise Data Pipeline
12.3.2 Implementing ETL and ELT Workflows
12.3.3 Integrating Cloud Data Sources
12.3.4 Monitoring and Optimizing Data Pipelines
Conclusion
The Teradata Analytics, AI & Advanced Data Engineering training develops practical skills in enterprise analytics, artificial intelligence, machine learning, advanced SQL, cloud integration, and scalable data engineering. Participants gain experience in building, integrating, and optimizing modern data solutions through hands-on exercises and an enterprise capstone project.
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