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TDWI Data Virtualization: Solving Complex Data Integration Challenges
Get ready to expand your data integration capabilities, deliver business-speed information, and make the most of recent advances in data integration technology. Through a combination of lecture, exercises, and case study review you will learn how data virtualization works and how to position it in your data integration architecture and processes.
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Modern Data Platforms and Architectures for Advanced Analytics, AI, and Machine Learning at Scale
In this course, you will learn how to select a data platform that meets the unique requirements of your organization for artificial intelligence (AI), machine learning (ML) and advanced analytics.
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Data Science Bootcamp // Modeling Your Data: Building and Assessing Models
Students will receive an overview of common statistical techniques and algorithms that are used in analytic models, how they are matched to business objectives and available data, and how the models are tuned and validated. The course will also cover key technologies that enable model development and management, and examples will reinforce key concepts.
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Data Architecture Essentials: Building a Data Foundation for Enterprise Analytics
This course offers a deep dive into the foundation and architecture of modern data platforms, highlighting key concepts such as data mesh, data warehouse, data lakehouse, and data virtualization.
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Modern Data Engineering for Tomorrow's Enterprise Landscape
This half-day course delves into cutting-edge practices to sculpt and refine robust data pipelines. Immerse yourself in the intricacies of data engineering for data meshes, data warehouses, data lakehouses, and data virtualization.
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An Introduction to Data Fabric Architecture
In this course you will learn about the case for a data fabric, when it makes sense to adopt this architecture, how to implement the data fabric architecture, and the implications of a data fabric model for people, processes, and technology.
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Is a Data Lakehouse in Your Future?
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Active Metadata for Data Fabric: Delivering Data Quality, Observability, and Trust
Understanding the best ways to capture metadata automatically at scale can then be followed by activating metadata to increase data observability, improve data quality and governance awareness, and ultimately build a trusted data architecture that can accelerate and improve decision-making in the business.
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