Oracle Technical Training
As part of an all-inclusive Deploying Analytics (Big Data, Business Intelligence, Knowledge Management) or Technical Training Certificate, candidates learn how to harness Oracle to meet specific business needs/objectives and find innovative ways to reach new customers, maximize efficiency/effectiveness, and drive profitable growth.
Before taking any of these courses, candidates should have taken Fundamentals of Information Technology Management and The Essentials of Data Management courses or their equivalent. These hands-on classes described below prepare candidates to apply Oracle’s distinctive functional modules to the most widely used database, data management, and business analytics initiatives:
While GIIM’s primary focus is empowering senior leaders to drive digital transformation, effective leadership requires a deep, grounded understanding of the underlying technologies shaping modern business. Our hands-on technical tracks are purposefully designed as high-impact components within our broader Technical Leadership and Digital Management certificate pathways. These intensive modules provide the technical proficiency required to lead engineering teams, architect modern solutions, and make informed strategic decisions—serving either as targeted skill enhancements or as foundational stepping stones toward GIIM’s advanced executive credentials.
Course Descriptions:
1. Oracle Analytics: Semantic Repository & Data Modeling Architecture
Duration: 4 Days (6 Hours / Day) | 24 Instructional Hours
Delivery Formats: Live Virtual, On-Site Corporate Cohort, or Hybrid Blended
Course Overview
Modern enterprise analytics depends on a robust, highly optimized semantic layer that translates complex corporate data into actionable, trustworthy business insights. This intensive, 4-day hands-on course provides step-by-step guidance on designing, building, validating, and administering an enterprise-grade Oracle Analytics semantic repository (RPD / Semantic Model).
Beginning with core business requirements, participants will leverage the Oracle Analytics Administration environment to construct a complete three-layer semantic architecture—Physical, Logical/Business Model, and Presentation. Candidates will import diverse schemas, establish physical and logical relationships, configure simple and calculated measures, and expose secure data structures to end users. Hands-on labs reinforce validation techniques through real-time query logging, performance tuning, and direct analysis verification.
As the program progresses, candidates tackle complex enterprise modeling challenges, including multi-source dimensional hierarchies, aggregate table navigation, data partitioning, and time-series analysis. The curriculum thoroughly addresses enterprise security, cache management, Multi-User Development (MUD) workflows, usage tracking, and multi-language support—equipping data architects and BI professionals to deploy scalable, high-performance analytics environments.
Core Learning Objectives & Syllabus
Day 1: Fundamentals of Semantic Architecture & The 3-Layer Model
Repository & Architecture Basics: Understanding the Oracle Analytics Server (OAS) and Oracle Analytics Cloud (OAC) semantic engine.
Physical Layer Construction: Importing physical schemas, configuring connection pools, and defining physical keys and joins.
Business Model & Mapping (BMM) Layer: Translating relational schemas into dimensional business models; defining logical tables, columns, and joins.
Presentation Layer Design: Exposing curated, user-friendly subject areas, presentation tables, and measures to business analysts.
Testing & Verification: Executing analyses, reviewing SQL/query logs, and validating business logic.
Day 2: Advanced Logical Modeling & Complex Calculations
Calculations & Measures: Building derived, logical, and expression-based measures using built-in analytics functions.
Dimensional Hierarchies: Designing level-based and parent-child hierarchies to enable seamless drill-down reporting.
Multiple Logical Table Sources (LTS): Mapping multiple physical tables to a single logical entity to streamline reporting models.
Time-Series Calculations: Implementing time-oriented analysis (e.g., Year-to-Date, Quarter-Over-Quarter, offset functions).
Day 3: Enterprise Performance & Data Optimization
Aggregate Tables & Performance Tuning: Configuring aggregate sources to accelerate high-volume queries.
Partitioning & Fragmented Sources: Modeling partitioned physical tables for optimal data retrieval.
Bridge Tables & Complex Relationships: Handling many-to-many relationships, bridge tables, and implicit fact columns.
Cache Management: Configuring, purging, and monitoring Oracle Analytics query cache to ensure optimal system speed.
Day 4: Security, Collaboration & Advanced Governance
Data & Object Security: Implementing role-based security, object-level permissions, and row-level data restriction filters.
Multi-User Development (MUD): Setting up collaborative development environments, project check-out/check-in processes, and version control.
Advanced Features: Configuring usage tracking, enabling write-back capability, supporting multi-language (localization) environments, and performing patch merges.
Maintenance & Utilities: Leveraging administration wizards, diagnostic tools, and lifecycle management utilities.
2. BI - Create Analyses & Dashboards
Duration: 3 Days (6 Hours / Day) | 18 Instructional Hours
Delivery Formats: Live Virtual, On-Site Corporate Cohort, or Hybrid Blended
Course Overview
Unlocking the full value of enterprise data requires intuitive, interactive dashboards and actionable visual analytics. This 3-day hands-on course equips business analysts, BI developers, and data practitioners with the skills needed to design, construct, and deploy high-impact analyses and interactive dashboards using the Oracle Analytics platform (OAC / OAS).
Participants will start by mastering core analysis creation within the presentation catalog before progressing to advanced visualization techniques. The curriculum covers a rich spectrum of presentation tools—including multi-dimensional pivot tables, interactive charts, geographical maps, dynamic prompts, embedded external content, and Key Performance Indicators (KPIs).
Candidates will also master automated distribution and mobile delivery by configuring intelligent agents to deliver personalized, real-time alerts. Finally, the course covers seamless integration with Microsoft Office tools (Excel and PowerPoint) and mobile-optimized reporting, enabling organizations to deliver timely, data-driven insights across all user touchpoints.
Core Learning Objectives & Syllabus
Day 1: Foundations of Analysis & Advanced Data Visualization
Presentation Catalog & Interface Navigation: Managing catalog folders, permissions, and objects across shared and personal spaces.
Building Dynamic Analyses: Selecting subject areas, defining column criteria, applying formulas, and formatting data.
Filtering & Data Manipulation: Applying static, dynamic, and prompt-driven filters; constructing groups and calculated items.
Hierarchical Analysis: Leveraging hierarchical columns, drill-down paths, and parent-child navigation.
Interactive Views & Charts: Designing pivot tables, trellises, gauge charts, heatmaps, and geographical map views to surface key patterns.
Day 2: Advanced Data Combination, KPIs & Interactive Dashboards
Set Operations & Direct Requests: Combining multiple analyses via union, intersect, and minus operations; executing direct database queries when needed.
Dashboard Design & Architecture: Creating section layouts, column structures, and multi-tab interactive dashboards.
Dashboard Prompts & Interactivity: Implementing global and inline prompts, master-detail linking, and page-to-page navigation.
KPIs & Scorecards: Defining Key Performance Indicators, setting target thresholds, tracking status/trends, and embedding KPIs into executive dashboards.
Day 3: Intelligent Alerting, Mobile Analytics & Enterprise Integration
Automated Delivery & Alerting (Agents): Configuring intelligent agents to monitor threshold conditions, schedule automated reports, and deliver personalized alerts to subscribers.
Microsoft Office Integration: Embedding live Oracle Analytics views directly into Microsoft PowerPoint presentations and Excel spreadsheets using Oracle Smart View / MS Office plugins.
Mobile & Executive Accessibility: Optimizing dashboard layouts for touch interfaces, tablet viewing, and mobile app consumption.
Catalog Administration & Governance: Managing catalog security, archiving/unarchiving content, and exporting briefing books or executive report packages.
3. Oracle Analytics: Introduction to Self-Service End-User Tools
Duration: 1 Day (6 Hours) | 6 Instructional Hours
Delivery Formats: Live Virtual, On-Site Corporate Cohort, or Hybrid Blended
Target Audience: Business Analysts, Managers, Department Heads, and Non-Technical Professionals
Course Overview
In today’s data-driven business environment, decision-makers must be able to explore data, extract actionable insights, and monitor performance without relying heavily on IT support. This 1-day, hands-on workshop introduces non-technical business users to the powerful self-service capabilities of Oracle Analytics (OAC / OAS).
Participants will learn how to navigate the web-based analytics interface to interrogate data, build custom visual analyses, assemble interactive executive dashboards, and track operational goals using Key Performance Indicators (KPIs) and Balanced Scorecards. The course also introduces candidates to enterprise document reporting (via Pixel-Perfect / BI Publisher reports), automated email/mobile alerts, and direct integration with Microsoft Office tools—equipping business users to make faster, more confident, data-informed decisions.
Key Modules & Course Outline
Module 1: Self-Service Data Exploration & Analysis
Navigating the user interface and searching the analytics catalog.
Interrogating business data using intuitive drop-down criteria, filters, and conditional formatting.
Transforming raw tables into compelling visual charts, pivot views, and interactive summary cards.
Module 2: Executive Dashboards & Scorecards
Assembling individual analyses into clean, multi-tab business dashboards.
Adding dynamic prompts, filters, and interactive objects to customize views on the fly.
Defining Key Performance Indicators (KPIs) and integrating them into Balanced Scorecards to track strategic objectives.
Module 3: Automated Delivery, Enterprise Reporting & Office Integration
Building enterprise-ready pixel-perfect reports (BI Publisher) for structured operational documents (e.g., invoices, financial statements).
Setting up automated agents and personalized alerts to push critical data threshold notifications via email or mobile.
Exporting and integrating live Oracle Analytics views directly into Microsoft Excel spreadsheets and PowerPoint presentations.
4. Oracle Data Integrator: Enterprise ELT Pipelines & Administration
Duration: 5 Days (6 Hours / Day) | 30 Instructional Hours
Delivery Formats: Live Virtual, On-Site Corporate Cohort, or Hybrid Blended
Course Overview
In modern data architecture, high-performance data pipelines are critical for powering cloud analytics, enterprise data warehouses, and real-time decision engines. This intensive 5-day hands-on course provides in-depth technical training on Oracle Data Integrator (ODI)—the industry-leading data integration platform built for high-volume, high-performance Extract, Load, Transform (ELT) architectures.
Unlike traditional ETL tools that process data on separate middleware servers, ODI leverages the processing power of target database engines to execute transformations at maximum speed with significantly lower infrastructure costs. Participants will learn how to design, execute, monitor, and administer complex data pipelines using ODI’s declarative design approach.
The curriculum covers end-to-end data integration lifecycle management: building advanced mappings, reusable procedures, and orchestrating complex packages; setting up automated Change Data Capture (CDC) for real-time streaming/loading; integrating with Web Services and modern REST/SOA frameworks; and configuring multi-user security, topologies, and agents across hybrid cloud and on-premises environments.
Core Learning Objectives & Syllabus
Day 1: ODI Architecture, Topology & Core ELT Principles
ODI Architecture Overview: Understanding the declarative ELT paradigm vs. traditional ETL middleware.
Master & Work Repositories: Architecting repository environments for development, testing, and production.
Topology Management: Setting up Physical and Logical Architectures, Data Servers, Physical/Logical Schemas, and Contexts across heterogeneous databases.
Designing Initial Models: Importing metadata, defining models, configuring datastores, and setting up primary/foreign key constraints.
Day 2: Mapping Design, Knowledge Modules & Declarative Rules
Designing ODI Mappings: Constructing data transformations, joins, filters, and lookups using ODI’s graphical interface.
Knowledge Modules (KMs): Selecting, configuring, and customizing Loading (LKM), Integration (IKM), Check (CKM), and Reverse-Engineering (RKM) Knowledge Modules.
Data Quality & Error Handling: Implementing static/flow control checks and managing error tables (E$) without interrupting data execution.
Day 3: Orchestration: Packages, Procedures & Variables
Advanced Package Design: Building multi-step workflows, configuring conditional branching, looping, and error handling.
Variables & Sequences: Defining dynamic variables, tracking execution state, and generating auto-incrementing sequences.
Custom Procedures: Writing custom reusable procedures using native SQL, Jython, Groovy, and OS commands to automate complex operational tasks.
Day 4: Real-Time Integration, CDC & Web Services
Change Data Capture (CDC): Implementing Journaling KMs to capture delta/real-time database changes for low-latency data integration.
Web Services & SOA/REST Integration: Exposing ODI workflows as Web Services (OdiInvoke) and integrating data pipelines with service-oriented and cloud API architectures.
Web-Based Management: Utilizing ODI Console and Enterprise Manager for web-based monitoring, topology inspection, and operator oversight.
Day 5: Enterprise Administration, Security & Environment Management
Agent Deployment & Management: Configuring Standalone, Collocated, and JEE Agents for job scheduling, load balancing, and high availability.
Security & Governance: Setting up role-based access control (RBAC), user authentication, privilege profiles, and object-level permissions in a multi-developer environment.
Version Control & Deployment Lifecycle: Managing object versioning, exporting/importing deployment units, and migrating solutions across Dev/Test/Prod environments.
5. Oracle Data Integrator 12c: Advanced ELT Architecture & Enterprise Administration
Duration: 5 Days (6 Hours / Day) | 30 Instructional Hours
Delivery Formats: Live Virtual, On-Site Corporate Cohort, or Hybrid Blended
Course Overview
In modern data engineering, organizations must move and transform vast volumes of data across heterogeneous, hybrid, and cloud environments with maximum speed and minimal cost. This intensive 5-day hands-on course provides comprehensive training on Oracle Data Integrator 12c (ODI 12c)—the industry standard for high-performance Extract, Load, Transform (ELT) architecture.
Unlike traditional middleware ETL engines that process data on standalone servers, ODI 12c leverages native database processing power to execute transformations directly within source and target engines. Participants will master ODI 12c’s advanced component-based mapping interface, declarative design methodology, and Knowledge Module (KM) architecture.
Candidates will learn how to build automated data pipelines, orchestrate complex workflows, implement real-time Change Data Capture (CDC), and administer enterprise ODI repositories and agent topologies to ensure high availability, security, and scalability.
Core Learning Objectives & Syllabus
Day 1: ODI 12c Architecture, Repositories & Topology Management
ODI 12c Architecture Overview: Understanding the declarative ELT paradigm and how ODI 12c optimizes performance across heterogeneous systems.
Master & Work Repositories: Creating, configuring, and managing Master and Work repositories for metadata storage and execution history.
ODI Studio & Graphical Interfaces: Navigating Topology, Designer, Operator, and Security Navigators.
Managing Topology: Configuring Physical and Logical Architectures, Data Servers, Physical/Logical Schemas, Connection Pools, and Contexts across disparate database platforms.
Day 2: Data Models, Component Mappings & Knowledge Modules
Model & Datastore Creation: Reverse-engineering metadata, establishing datastores, and defining primary, foreign key, and check constraints.
Designing Component-Based Mappings: Constructing transformations using ODI 12c mapping components (Joins, Filters, Lookups, Aggregators, Sets, and Subqueries).
Knowledge Modules (KMs): Selecting, configuring, and customizing Loading (LKM), Integration (IKM), Check (CKM), and Reverse-Engineering (RKM) Knowledge Modules.
Data Quality & Flow Control: Configuring static and dynamic checks to redirect erroneous records (E$ tables) without interrupting pipeline execution.
Day 3: Advanced Orchestration: Packages, Procedures & Variables
Package Design & Flow Control: Assembling complex integration workflows, handling conditional branching, looping, and error catching.
Variables & Sequences: Defining dynamic, refreshable variables and managing system state and sequences.
Custom Procedures & Scripting: Writing reusable procedures using native SQL, Jython, Groovy, and operating system commands for advanced automation.
Day 4: Real-Time CDC, Web Services & High Availability
Change Data Capture (CDC): Implementing Journalizing Knowledge Modules (JKMs) to capture delta changes for near-real-time data streaming and continuous loading.
Web Services & Service-Oriented Architecture: Exposing ODI scenarios as Web Services and integrating pipelines with SOA/REST endpoints.
Agent Architecture & Scheduling: Configuring Standalone, Collocated, and JEE Agents for load balancing, high availability, and automated job scheduling.
Day 5: Security, Monitoring & Lifecycle Management
Enterprise Security Governance: Configuring Role-Based Access Control (RBAC), user authentication, privilege profiles, and object-level permissions in multi-developer teams.
Operator Studio & Diagnostics: Monitoring execution logs, analyzing session details, debugging failure points, and executing performance tuning.
Deployment & Lifecycle Management: Generating and executing Scenarios, managing version control, exporting/importing deployment units, and migrating artifacts across Dev, Test, and Production environments.
6. Oracle Database AI/ML: Autonomous In-Database Machine Learning & Vector Search
Duration: 3 Days (6 Hours / Day) | 18 Instructional Hours
Delivery Formats: Live Virtual, On-Site Corporate Cohort, or Hybrid Blended
Course Overview
Modern data architecture requires bringing artificial intelligence directly to where the data lives. This 3-day hands-on course equips data engineers, database administrators, and data scientists to harness Oracle’s cutting-edge in-database AI and Machine Learning (OML) capabilities, including Oracle Database 23ai AI Vector Search.
Participants will learn how to build, train, and deploy machine learning models directly inside the Oracle Database engine using SQL, PL/SQL, Python, and R without extracting data to external systems. The course covers generating vector embeddings, executing similarity searches for Retrieval-Augmented Generation (RAG) architectures, and automating predictive analytics using Oracle Autonomous Database. By keeping AI processing inside the database tier, organizations achieve unprecedented execution speed, enterprise-grade security, and simplified data governance.
Core Learning Objectives & Syllabus
Day 1: In-Database Machine Learning & Oracle Machine Learning (OML) Ecosystem
Oracle AI/ML Architecture: Overview of Oracle Machine Learning (OML) algorithms embedded in Oracle Database and Autonomous Database.
OML for SQL & PL/SQL: Building predictive models (classification, regression, clustering, anomaly detection) directly via SQL algorithms.
OML Notebooks & OML4Py: Environment setup and executing Python-based machine learning directly against database-resident data.
Feature Engineering & Data Preparation: In-database data preparation, binning, normalization, and automated feature selection.
Day 2: AI Vector Search & Enterprise RAG Architectures
Oracle AI Vector Search Fundamentals: Vector data types, embeddings, and high-dimensional space indexing (HNSW and IVF indexes).
Generating & Storing Embeddings: Integrating external LLMs (e.g., OCI Generative AI, OpenAI) and internal ONNX models to generate vector embeddings.
Vector Distance Functions & Similarity Search: Executing L2, Cosine, and Dot Product similarity queries across unstructured and structured business data.
Building RAG Pipelines: Combining Oracle AI Vector Search with Generative AI models to power enterprise Retrieval-Augmented Generation systems.
Day 3: Model Deployment, MLOps, & Security Governance
OML AutoML: Leveraging automated machine learning for rapid algorithm selection, feature tuning, and model building.
Model Deployment & REST Endpoints: Registering models into OML Services and exposing them via secure REST APIs.
AI Data Governance & Security: Enforcing Virtual Private Database (VPD), Data Redaction, and Database Vault permissions across AI/ML pipelines and vector indexes.
Performance Optimization: Tuning vector indexes, parallelizing OML workloads, and monitoring AI query execution logs.
7. Oracle Cloud Infrastructure (OCI) AI Services & Cloud Data Architecture
Duration: 3 Days (6 Hours / Day) | 18 Instructional Hours
Delivery Formats: Live Virtual, On-Site Corporate Cohort, or Hybrid Blended
Course Overview
To compete in an AI-first economy, enterprises must leverage scalable cloud platforms that combine robust data management with managed artificial intelligence services. This 3-day intensive course provides a strategic and technical deep dive into Oracle Cloud Infrastructure (OCI) AI, Generative AI, and Cloud Data Services.
Designed for cloud architects, data engineers, and technical leaders, this course explores how to build end-to-end cloud data pipelines and intelligent applications using OCI Generative AI, OCI Data Science, OCI Speech/Vision/Language APIs, and OCI Data Lakehouse architectures. Participants will gain practical expertise in orchestrating cloud data, fine-tuning foundation models, and integrating cloud-native AI microservices into enterprise workflows.
Core Learning Objectives & Syllabus
Day 1: OCI Data Lakehouse & Cloud Data Foundation
OCI Data Architecture Overview: Integrating OCI Autonomous Data Warehouse, OCI Data Catalog, Object Storage, and OCI Data Flow (Apache Spark).
Cloud Data Ingestion & Integration: Building cloud-native pipelines using OCI Data Integration and OCI Streaming (Kafka-compatible).
Data Lakehouse Governance: Implementing centralized security, identity and access management (IAM), and data lineage across cloud storage and database tiers.
Auto-scaling & Storage Optimization: Managing elastic compute, auto-scaling storage, and tiering cold vs. hot data in OCI.
Day 2: OCI Generative AI & Pre-trained Cognitive Services
OCI Generative AI Service: Deploying managed foundation models (Llama, Command, Mistral) for summarization, generation, and chat applications.
Customizing & Fine-Tuning LLMs: Fine-tuning pre-trained models on enterprise datasets using T-Few and LoRA techniques within OCI dedicated AI clusters.
OCI Applied AI Services: Integrating OCI Language, OCI Vision, OCI Speech, and OCI Document Understanding into automated business workflows.
Vector Databases in the Cloud: Integrating OCI AI Services with OCI Search with OpenSearch and Autonomous Database Vector Search.
Day 3: OCI Data Science & Enterprise AI Application Deployment
OCI Data Science Platform: Managing collaborative JupyterLab environments, Conda environments, and shared model catalogs.
MLOps & Pipeline Automation: Automating model training, testing, versioning, and deployment using OCI Data Science Pipelines.
Deploying AI Microservices: Hosting ML model endpoints via OCI Functions, API Gateway, and OCI Container Engine for Kubernetes (OKE).
Enterprise Cloud AI Security & Cost Management: Ensuring data privacy, zero-data-retention compliance in OCI AI, and optimizing cloud compute costs (GPUs/RDMA networks).