Role-Based Programme
RB1510

AI-Powered Database Administration & Data Engineering

Smarter Data Operations, Performance Optimisation & Pipeline Management

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Database Administration and Data Engineering activities including database operations, SQL support, performance analysis, data pipelines, monitoring, and documentation.
  • Use AI-assisted techniques to analyse queries, database metrics, logs, pipeline failures, data-quality issues, schemas, and operational records more efficiently.
  • Develop structured workflows for database monitoring, troubleshooting, capacity planning, data integration, pipeline management, and incident resolution.
  • Improve data-platform reliability through AI-assisted anomaly identification, performance analysis, quality monitoring, documentation, and operational reporting.
  • Apply responsible AI practices covering sensitive data, credentials, access controls, query validation, production safety, data privacy, and human oversight.

Tools covered

Generative AI AssistantsSQL AssistanceDatabase Performance AnalysisData Pipeline AnalysisData Quality SupportMetadata & Documentation SupportSpreadsheet AnalysisMonitoring & Incident AnalysisReporting AssistanceWorkflow Automation

Who should attend

  • Database Administrators
  • Data Engineers
  • Database Engineers
  • Data Platform Engineers
  • SQL Developers
  • ETL / ELT Developers
  • Data Operations Professionals
  • Cloud Database Professionals
  • Data Infrastructure Engineers
  • Data Warehouse Professionals
  • Application Database Support Professionals
  • Data Reliability Professionals
  • Technical Data Analysts
  • Information Technology Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of databases, SQL, data pipelines, or data-platform operations
  • Familiarity with relational databases, schemas, queries, ETL / ELT, logs, or monitoring is helpful
  • Basic understanding of data security and access-control principles
  • Basic awareness of Generative AI is helpful
  • No advanced AI or machine-learning knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, analytics, automation, and their role in database and data-platform operations
  • Identifying AI applications across query support, performance analysis, monitoring, pipelines, and documentation
  • Understanding AI assistance versus DBA and Data Engineer accountability
  • Recognising risks related to production data, credentials, destructive queries, and unsupported recommendations
Practical activities
  • Mapping database and data-engineering activities to AI-assisted opportunities
  • Identifying repetitive operational tasks suitable for AI support
  • Comparing traditional and AI-assisted database workflows

Scenarios

Database Performance Issue to Optimisation Plan

Performance Alert → AI-Assisted Evidence Review → Query & Resource Analysis → Bottleneck Identification → Optimisation Options → Validation Plan → Controlled Implementation → Monitoring

Participants analyse a simulated database-performance issue, identify likely bottlenecks using supplied query and resource data, develop optimisation options, and prepare a controlled validation and monitoring plan.

Failed Data Pipeline to Data Reliability Improvement Plan

Pipeline Failure → Log Review → Dependency Analysis → Data Validation → Root-Cause Investigation → Recovery Actions → Quality Checks → Monitoring & Management Report

Participants investigate a simulated data-pipeline failure, assess downstream data impact, identify evidence-supported causes, define recovery and prevention actions, and prepare a structured reliability improvement report.

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