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Data modeling has been a data analysis and design discipline for decades, but in recent years it has become a lost art for many data teams. Attention to modeling diminished with the adoption of big data, data lakes, data pipelines, schema-on-read, and other modern data trends. Still, data modeling is a core data engineering skill—one that is deficient or missing in many data engineering teams. It is time to rediscover the lost art of data modeling. 

You Will Learn: 

• Modern uses for older techniques – relational and multi-dimensional data modeling

• Rethinking the roles of logical and physical data modeling 

• Higher levels of abstraction – conceptual and semantic data modeling 

• Sematic data models a data translators for APIs, data services, and data products 

Consultant: Dave Wells, Analyst, Eckerson Group 

About the What Works Series: 

The What Works Series provides data and analytics leaders practical advice about how to succeed in key areas: data strategy, data architecture, data governance, data literacy, self-service analytics, and emerging technology approaches. These interactive sessions are delivered by Eckerson Group consultants who collectively have 200+ years of data & analytics experience. The sessions are unsponsored, enabling our experts to offer the unvarnished truth about trends, tools, and technology unfiltered by software vendors.

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Dremio, with Eckerson Group analyst, Sumit Pal, will share insights on how businesses are confronting new analytics challenges. This information is derived from a recent survey report based on a comprehensive study of 500 organizations. Learn how these organizations are embracing data lakehouses to combine the scalability of data lakes with the capabilities of data warehouses.

Attendees Will Learn:

● Benchmark your organization by learning about the recent data and AI trends from a new survey of over 500 organizations: Includes the current and future State of the Lakehouse, table format, data mesh, and AI adoption

● Data Analytics industry trends and top predictions for 2024

● Discover cost-effective data management practices to boost enterprise data adoption and maturity

Sponsor: Dremio

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This webinar offers a guide for data engineers, data scientists, and ML engineers that must design and manage these pipelines. It explores three primary approaches to supporting domain-specific language models: prompt enrichment, one-shot prompting, and retrieval-augmented generation (RAG). In each case data teams aim to provide language models with the facts and context they need to generate accurate, trustworthy, and governed responses that drive value for the business.

Join our webinar with Kevin Petrie, VP of Research at Eckerson Group, and Cyril Sonnefraud, Principal Product Manager of Matillion, to learn:

• What generative AI is and how it works 

• Why companies are adopting domain-specific language models 

• How data pipelines support these GenAI initiatives 

• How prompt enrichment, one-shot prompting, and RAG compare with one another 

• What criteria to use when evaluating these approaches

Sponsor: Matillion

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At Eckerson Group, we love the end of the year. It gives us a chance to step aside from the daily cadence of creating data & analytics research and advising consulting clients. We can sit back and reflect on the events of the past year and prognosticate on what may transpire in the months ahead. 

Our predictions webinar will be a community discussion where we bring your thoughts and ideas into the conversation. Don’t be shy! Bring your eggnog or beverage of choice and celebrate the holidays and end of the year with your favorite Eckerson research analysts and consultants.

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Data products promise to deliver high-quality data sets to business users on demand, fostering greater trust in data and higher levels of empowerment and self-service. But many companies struggle to understand not only what data products are, but how to create, govern, and manage them.

This 3-hour virtual event is designed to dive into the practical implications of running an organization using data products. It will describe how a data product is different from a data asset and then describe how to create data products from data assets using a variety of tools and techniques. It will address organizational, architectural, and process considerations for delivering data products at scale. It will also review commercial products that help data producers create, publish, and monitor data products and data consumers browse, evaluate, and subscribe to data products.

Attendees Will Learn:

• The key components of a data product

• How to build data products from data assets

• How to create a data product mindset in your organization

• How to select a platform to publish and consume data products

• How to create data contracts and deliver on promises