Role-Based Programme
RB1508

AI-Powered Database Administration & Data Engineering

Smarter Data Operations, Query Support & Pipeline Management

IMAGE REQUIRED
Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how AI can support Database Administration and Data Engineering across query development, troubleshooting, data pipelines, documentation, and monitoring.
  • Explore practical prompting techniques for SQL support, schema understanding, error analysis, data-quality checks, and technical documentation.
  • Apply AI to organise database information, explain queries, analyse logs, document pipelines, and support recurring data operations.
  • Identify opportunities to improve technical productivity, troubleshooting efficiency, documentation quality, and data-engineering workflows.
  • Recognise data privacy, access control, production safety, security, validation, and human-review responsibilities when using AI.

Tools covered

Generative AI AssistantsAI-Assisted SQL SupportDatabase TroubleshootingData Pipeline DocumentationLog AnalysisData Quality SupportPerformance AnalysisBasic Workflow Automation

Who should attend

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

Prerequisites & Participant Readiness

  • Basic understanding of databases, data platforms, or data-engineering concepts
  • Familiarity with SQL, tables, schemas, ETL/ELT, or database operations is helpful
  • Basic computer and technical skills
  • No advanced AI or machine-learning knowledge required
  • No previous Generative AI experience required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to database and data-engineering environments
  • Identifying AI applications across SQL, troubleshooting, pipelines, documentation, monitoring, and data quality
  • Understanding AI assistance versus DBA and Data Engineer judgement and accountability
  • Recognising production, security, and data-sensitive activities requiring validated procedures and human oversight
Practical activities
  • Mapping common DBA and Data Engineering tasks to potential AI applications
  • Comparing a traditional database task with an AI-assisted approach

Scenarios

Business Requirement to Validated SQL Query

Business Requirement → AI-Assisted Interpretation → Schema Context → SQL Draft → Logic Review → Test Conditions → Validated Query

Participants use AI to translate a sample reporting requirement into a draft SQL query, review its logic and assumptions, and prepare validation checks before execution.

Data Pipeline Failure to Technical Resolution Summary

Pipeline Alert → Logs & Errors → AI-Assisted Analysis → Possible Causes → Validation Steps → Corrective Action → Documentation

Participants use AI to organise a sample data-pipeline failure, analyse supplied evidence, prepare troubleshooting steps, and create a structured technical resolution summary while retaining final production actions with authorised data professionals.

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