De-Mystifying the Data Mesh

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Presented by

Michele Goetz, VP/Principal Analyst, Forrester, and Steve Sarsfield, Director of Product Marketing, Vertica

About this talk

Data mesh is not something enterprises can buy off the shelf. Data mesh is a sociotechnical approach to share, access, and manage analytical data in complex and large-scale environments — within or across organizations. The technical aspect is more architecture than tool or platform, with almost a religious mantra of, “Data mesh is not about technology.” But the number one question data architecture and data engineering teams have about data mesh is, what are the strategies I use to implement it? Tune into this webinar to learn from guest speaker Michele Goetz, VP/Principal Analyst at Forrester: • some of the principles behind the data mesh concept • how to translate data mesh soft artifacts to deployable products • what data capabilities exploit the decoupled nature of compute, storage, and state • and where a scalable, high value for performance database like Vertica fits in a data mesh implementation

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The Vertica Unified Analytics Platform is built to handle the most demanding analytic use cases and is trusted by thousands of leading data-driven enterprises around the world, including Etsy, Bank of America, Uber, and more. Based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and built-in machine learning capability, Vertica enables data analytics teams to easily apply these powerful functions to large and demanding analytical workloads. Vertica unites the major public clouds and on-premises data centers, as needed, and integrates data in cloud object storage and HDFS without forcing any data movement. Available as a SaaS option, or as a customer-managed system, Vertica helps teams combine growing data siloes for a more complete view of available data. Vertica features separation of compute and storage, so teams can spin up storage and compute resources as needed, then spin down afterwards to reduce costs.