Role-Based Programme
RB1511

AI-Powered Database Administration & Data Engineering

Intelligent Data Operations, Performance Optimisation & Pipeline Automation

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across database administration, data engineering, data pipelines, performance monitoring, data quality, and operational support.
  • Use AI to analyse database metrics, SQL queries, schemas, logs, pipeline failures, data-quality issues, metadata, and infrastructure information.
  • Build repeatable AI-assisted workflows for query optimisation, troubleshooting, schema documentation, pipeline monitoring, incident analysis, and operational reporting.
  • Apply AI to identify database bottlenecks, inefficient queries, recurring pipeline failures, data-quality problems, capacity risks, and automation opportunities.
  • Design responsible AI-enabled database and data-engineering workflows with appropriate controls for security, privacy, production access, data integrity, change management, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchCoding AssistantsSQL Assistance ToolsDatabase Monitoring & Performance AnalyticsData Quality ToolsETL / ELT & Pipeline ToolsCloud Data PlatformsMetadata & Data Catalog ToolsReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Database Administrators
  • Senior Database Administrators
  • Data Engineers
  • Senior Data Engineers
  • Database Engineers
  • Data Platform Engineers
  • ETL / ELT Developers
  • Data Operations Engineers
  • Cloud Data Engineers
  • Database Support Engineers
  • Data Warehouse Professionals
  • Data Integration Professionals
  • Data Platform Administrators
  • Data Reliability Engineers
  • Database & Data Engineering Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of databases, SQL, data engineering, data integration, or enterprise data platforms
  • Familiarity with schemas, queries, tables, pipelines, ETL / ELT, data quality, logs, and database operations
  • Basic proficiency with spreadsheets, technical documentation, data tools, and workplace productivity applications
  • No previous AI course attendance required
  • No advanced programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, coding assistants, and workflow automation
  • Mapping AI opportunities across database operations, engineering, pipelines, monitoring, and support
  • Understanding AI assistance versus authorised production and data-management decisions
  • Recognising hallucination, data exposure, destructive-query, and production-change risks
Practical activities
  • Mapping an existing database and data-engineering workflow
  • Comparing manual and AI-assisted data operations
  • Creating a Database & Data Engineering AI opportunity matrix

Scenarios

Database Performance Issue to Controlled Resolution

Performance Alert → Metric Analysis → Query Review → Execution Evidence → Bottleneck Identification → Optimisation Recommendation → Validation → Controlled Change → Monitoring

Participants use AI-assisted techniques to investigate a simulated database-performance problem, analyse queries and metrics, propose validated improvements, and prepare a controlled implementation and monitoring plan.

Data Pipeline Failure to Reliable Data Operations

Pipeline Failure + Logs + Data Quality Checks + Dependency Data + SLA Impact → AI Analysis → Root Cause Investigation → Recovery Plan → Automated Monitoring → Data Quality Dashboard → Engineering Review

Participants design an AI-enabled data-engineering workflow that identifies recurring pipeline and data-quality problems, improves recovery and escalation, automates operational monitoring, and strengthens data-platform reliability while retaining production decisions with authorised data and database professionals.

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