Prerequisite: Knowledge of Hadoop.

Building a data warehouse has always been an essential enterprise project, one that is theoretically well understood but in practice has left enterprises in a state of disarray. At the same time, the transition of analytics to an enterprise capability generates new pressure for governance and support—both from IT and from the business. How do you avoid a culture clash between analysts and IT which has previously led to six iterations of “AI winters?” How can you enable capabilities such as edge analytics and interactive visualizations?

In this course for enterprise architects and technology planners, Krish Krishan will provide an in-depth survey of the major platforms for data management and advanced analytics, their capabilities, and their technical architectures. Krishnan will also provide frameworks for matching solutions to enterprise needs, factoring in business objectives, legacy infrastructure, and existing staff and capabilities.

In recent years, there has been a tremendous increase in infrastructure and platform innovation. Many of these evolved platforms, when interconnected, can deliver on the enterprise mission of a modern data warehouse.

A modern data platform is designed to be democratic, proactive, scalable, and flexible to respond to future technologies and evolving needs of modern production and consumption teams. We are driven by more microservices and serverless architectures. We are orchestrated for scale up and scale out. Do we know there are limitations? What kind of data lifecycles work? What analytics lifecycles sustain? What machine learning best practices thrive?

Attend this session to learn all about modern data platforms, advanced infrastructures, and the architecture of the modern data warehouse.

You Will Learn

• The foundational requirements of modern data warehouse and analytics solutions
• Overview of major platforms for data management, including Snowflake, Azure, AWS, GCP, Oracle Cloud, and Firebolt
• Pitfalls, risks, and mitigation strategies of modern data platforms for data and analytics
• Overview of major platforms for analytics, including Qlik Sense, Looker, Klipfolio, Zoho Analytics, and Domo
• Architectural frameworks and system interfaces for modern data, analytics, and machine learning
• Facilitating key functions including data ingestion, data storage and processing, data transformation and modeling, business intelligence and analytics, data observability, data cataloging, and data discovery
• Best practices for establishing the best-fit architecture for your business

Geared To

• Enterprise architects
• API designers and developers
• Full stack engineers
• Product managers
• UI/UX practitioners