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The data lakehouse fosters innovative analytics as it merges elements of the data warehouse and data lake. It enables data engineers to support data analysts that manage business intelligence (BI), data scientists that manage artificial intelligence/machine learning (AI/ML), and business managers that play a rising role in analytics. And now the lakehouse empowers enterprises to reinvent their businesses with generative AI. 

This webinar defines the characteristics, adoption drivers, requirements, benefits, and use cases of a modern lakehouse platform, then explores how data teams should architect and manage the pipelines that deliver data for analytics and AI. Attendees also will learn how to deliver consolidated, accurate, and governed data for AI/ML model training with Databricks and Matillion.

Join this webinar with Kevin Petrie, VP of Research at Eckerson Group; Mark Balkenende, VP of Product Marketing at Matillion; and Denis Dubeau, Lead Solution Architect at Databricks.

Sponsors: Databricks and Matillion

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While nobody can predict the overall future of generative AI, the business case for AI-driven data management is becoming clear. Most early adopters estimate that large language models such as ChatGPT and Bard make them up to 30% more productive, and new domain-specific tools from data pipeline vendors aim to beat these gains. Data teams use language models to document their environments, build pipeline code, run data quality checks, learn new techniques, and more. 

Join this strategic discussion with Kevin Petrie, VP of Research at Eckerson Group, and Heine Iversen, CEO of TimeXtender, to explore the impact of language models on data management.

Sponsor: TimeXtender

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Data mesh and data fabric are modern architectural approaches that aim to solve today’s data management challenges. Data mesh focuses on delivering data products built by the teams that own the data. Data fabric focuses on technologies that obscure the complexities of disparate and distributed data. The proponents of each approach sometimes give the impression that they’re mutually exclusive. However, they are more complementary than competitive, as evidenced by the feature convergence of data mesh and data fabric products.

This 3-hour virtual event is designed to help data leaders evaluate and select data mesh and data fabric tools that deliver and manage discoverable, addressable, and trusted data products. The event compresses the time it takes data leaders to understand an emerging technology, create a short list of products, and hear tips from experts, practitioners, and solutions providers in the field.

Attendees Will Learn:

• Technologies that are powering the rise of data mesh and data fabric

• Why domain ownership and data products are replacing centralized legacy approaches

• How to blend data mesh and data fabric concepts to deliver and manage data products

• The features you should consider when evaluating data mesh and data fabric products

• Tips for success and traps to avoid

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Data catalogs automate the collection, discovery, and curation of information about data assets. They are critical for optimizing data quality, self-service analytics, data operations, data governance, data privacy, and data security. Despite these benefits, many companies have struggled to implement data catalogs and gain a critical mass of users. 

A new generation of data catalog products promise to accelerate adoption while delivering the intelligence that comes from managing metadata in a systematic fashion. Vendors have reinvented their products using AI and active metadata to automate many data-driven activities. And they have doubled down on ease-of-use and fast installs to deliver value quickly. Collectively, these tools increase the business value of data by reducing risk, automating processes, and increasing trust in data throughout the enterprise. 

This topical virtual event is designed to help data and governance leaders evaluate and select next-generation data catalogs and learn best practices for implementing them. The event compresses the time it takes data leaders to understand this new technology, create a short list of products, and hear tips from experts, practitioners, and solutions providers in the field.

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Data products have emerged as a key component in modern analytics strategies, offering tailored, actionable insights and flexibility to cope with ever-changing data complexity and business requirements. But how can organizations adopt a product mindset to drive successful data product development and deployment?

In this webinar, we will explore the best practices for building and scaling data products, from starting small and iterating to establishing robust automated product governance. We will also showcase a live demonstration of Starburst’s data lake analytics platform, on how to accelerate data product development and delivery across your enterprise.

Sponsor: Starburst