Role-Based Programme
RB1509

AI-Powered Database Administration & Data Engineering

Smarter SQL, Data Pipelines, Performance & Reliability

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Database Administration and Data Engineering across SQL development, database operations, data pipelines, monitoring, documentation, and troubleshooting.
  • Apply AI-assisted techniques to analyse database queries, data-quality issues, logs, schema information, pipeline failures, and operational metrics.
  • Use structured prompting for SQL assistance, data transformation, pipeline review, performance investigation, and technical documentation.
  • Explore AI-supported approaches for identifying data-quality problems, recurring failures, performance bottlenecks, and automation opportunities.
  • Build responsible AI-assisted database and data-engineering workflows while maintaining security, privacy, data integrity, access control, and human validation.

Tools covered

Generative AI AssistantsAI Search & ResearchSQL AssistanceDatabase Documentation AIData Pipeline AnalysisData Quality AnalysisPerformance Monitoring AILog AnalysisReporting & Workflow Automation

Who should attend

  • Database Administrators
  • Data Engineers
  • Database Engineers
  • SQL Developers
  • Data Platform Engineers
  • ETL Developers
  • Data Integration Professionals
  • Database Support Engineers
  • Cloud Database Professionals
  • Data Operations Professionals
  • BI & Data Professionals
  • Application Support Engineers
  • Database Managers
  • Database & Data Engineering Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of databases, SQL, or data-processing concepts
  • Familiarity with tables, queries, data pipelines, schemas, or database operations is helpful
  • Basic computer and spreadsheet skills
  • No AI or advanced programming knowledge required
  • No previous AI training required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to databases and data engineering
  • Identifying AI applications across SQL, database administration, pipeline operations, data quality, and documentation
  • Understanding AI assistance versus DBA and Data Engineer judgement
  • Recognising limitations such as incorrect SQL, incomplete context, security risks, and unsupported technical conclusions
Practical activities
  • Mapping a typical database and data-engineering workflow
  • Identifying repetitive and information-intensive activities suitable for AI assistance
  • Comparing a traditional data task with an AI-assisted approach

Scenarios

Slow Query to Performance Investigation

Performance Alert → AI-Assisted Query Review → Execution Evidence → Possible Bottlenecks → Diagnostic Questions → DBA Validation → Optimisation Action

Participants use AI to analyse a sample database-performance issue, organise evidence, identify possible areas for investigation, and prepare a structured troubleshooting plan for technical validation.

Data Pipeline Failure to Reliable Recovery

Pipeline Failure → Logs + Validation Results → AI Analysis → Failure Point → Data Quality Check → Recovery Actions → Validation → Operational Report

Participants use AI to analyse a sample data-pipeline failure, identify likely problem areas, structure recovery steps, and prepare a concise operational report after technical validation.

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