Location & Travel

About The Conference

The Data Architecture Summit will cover the most important issues and technologies enabling the design and management of a modern Data Architecture. 
Data Architecture has long been at the intersection of business and technology. The role of the Data Architect is to interpret the language and objectives of business sponsors into designs that can be implemented by the technical experts and vice versa, they translate system designs and technological capabilities to the needs of the organization. Recently, this job has become more challenging (and interesting!) with the emergence of Big Data, the Cloud, Data Science, new Database technologies, and Data Governance.
The goal of the Data Architecture Summit is to provide a comprehensive educational program that defines the essential elements of a modern Data Architecture, and explains how you can create it for your own organization. Whether you’re looking to build or buy your new architecture, the Data Architecture Summit will provide the essential knowledge and language to understand the options. If you know you need to re-tool or upgrade your current architecture, attend this event and you’ll leave with a comprehensive understanding of your technology and design options and real-world advice on planning a smooth migration from industry professionals.

You’ll learn about these critical aspects of modern Data Architecture:

  • What Are the Top Factors Shaping Data Architectures Now?
  • Designing a Data Architecture for Modern Business Analytics
  • Advanced Data Architecture for Big Data, IoT, and the Cloud
  • Components of Data Lake Architecture      
  • How to “Design In” Data Governance and Quality
  • Designing an Adaptable Analytics Ecosystem
  • Introduction to Graph Data Management 
  • Building a Secure Architecture for Sensitive Data
  • Architecting A Big Data Platform
  • Designing the Analytics Organization
  • Innovation-Enabled Governance
  • Next Generation Enterprise Data Models
  • Customer and Business-Friendly Taxonomies             
  • Real-Time Data Ingestion at Scale
  • Data Modeling for Both NoSQL and SQL      
  • Designing for Data Monetization
  • Architecture that Enables Self-Service BI & Analytics              
  • Ontology Engineering for the Enterprise
  • Data Architecture at the Times of "Schema on Read"             
  • Metadata Architecture
  • Positioning MDM as the Centerpiece of Your Data Architecture
  • Architecting for Performance
  • Data-Centric Architecture Based on Semantics
  • Data Architecture for AI and Machine Learning

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