Data Architecture, Modernization & Engineering
Build the foundations for modern data-driven organizations. Learn how to design scalable data platforms, plan migrations/modernizations, optimize pipelines, and enable seamless access to trusted data. This domain focuses on topics like automated data processing, DataOps, data lakehouses, lineage tracking, and transformation frameworks like dbt , Spark or DuckDB. Talks explore real-world strategies for streamlining pipeline operations, solving data integrity issues, and optimizing engineering workflows. Ideal for engineers and architects working to modernize and monitor data infrastructures.
AI Native Data Engineering
Accelerate data and AI development with AI-powered engineering tools. This track explores how AI copilots and coding assistants are transforming the Analytics Development Lifecycle (ADLC) - from data pipeline development to ML model deployment to platform engineering. Learn about tools like Claude Code, Cursor, and GitHub Copilot for analytics engineering, AI agents in notebooks, AI-powered SQL generation, and spec-driven frameworks for streamlined pipelines. Sessions cover real-world integrations, productivity gains across the ADLC, and best practices for AI-assisted engineering.
Data Privacy, Security & Compliance
Navigating privacy and security in regulated environments. This track addresses the business and architectural challenges of data privacy, security, and regulatory compliance. Sessions cover regulatory frameworks (GDPR, CCPA, sector-specific regulations), risk management strategies, compliant data architectures, cross-border data governance, and balancing innovation with regulatory requirements. Ideal for business leaders, architects, and teams operating in highly regulated industries like healthcare, finance, and government.
Using AI for Data
AI is not just the output — it's part of the process. This track focuses on using AI to enhance data engineering, governance, and analytics workflows. Learn how AI-driven tools can extract structured data from unstructured sources, automate data processing, perform intelligent data quality checks, streamline governance workflows, and enable natural language interfaces for complex business questions. Explore practical applications where AI becomes an embedded processing layer in your data value chain.
ML/AI & Data Science: from Research to Production
The complete machine learning lifecycle. Sessions cover traditional ML algorithms, statistical modeling, experimentation, and modern MLOps practices. Learn how organizations develop, deploy, monitor, and scale ML systems in production environments. Topics include model development workflows, deployment strategies, A/B testing, reproducibility, and bridging the gap between data science research and reliable operational systems.
Platform Engineering
Self-service cloud infrastructure for data and AI teams. Explore how platform engineering enables data and analytics engineers to deploy, scale, and secure their workloads through automated, governed infrastructure. Topics include container orchestration, infrastructure automation, developer portals for data teams, IAM and access controls, security frameworks for AI systems, and multi-cloud strategies that balance autonomy with governance.
Operational Data, Streaming & Real-time analytics
Data in motion drives decisions. This track explores real-time and streaming architectures for processing operational data at scale. Learn how organizations are enabling real-time analytics, customer-facing dashboards, and operational intelligence systems. Talks highlight streaming technologies, event-driven design, and best practices for delivering insights when they matter most.
Analytics, BI & Visualisation
urn data into insights that matter. This track showcases modern BI platforms, data storytelling techniques, and advanced visualisation approaches for impactful decision-making. Expect practical guidance on building dashboards, communicating with stakeholders, and driving adoption of analytics tools.
Data & AI Governance & Quality
Building trust through governance and quality. This track focuses on the business frameworks and practices that ensure data and AI systems are reliable, compliant, and trustworthy. Sessions cover data governance strategies, quality assurance processes, regulatory compliance, bias detection and mitigation, and organizational practices for responsible AI. Learn how to establish governance frameworks, implement quality controls, and build stakeholder confidence in your data and AI initiatives.
Generative AI, LLMs & Agents
Explore the power of generative models and intelligent agents. From LLM applications to agents & agentic frameworks, see how these technologies are transforming industries and reshaping the way humans and machines collaborate. Sessions blend technical depth with applied use cases across domains.
Data and AI Strategy
Align data and AI initiatives with business goals. Learn how to drive innovation, measure impact, and build sustainable strategies that scale across organizations. Sessions highlight leadership perspectives, change management, and success stories from enterprises putting strategy into action.
Data & AI Across Industries
See data and AI in action across industries. Real-world stories from diverse sectors including healthcare, finance, manufacturing, retail, public sector, and beyond. This track highlights lessons learned, cross-industry patterns, and the unique challenges of applying data and AI in regulated or specialized domains.
