Simplified Machine Learning, Text, and Graph Analytics with Pivotal Greenplum

May 25, 2018
Data is at the center of digital transformation; using data to drive action is how transformation happens. But data is messy, and it’s everywhere. It’s in the cloud and on-premises. It’s in different types and formats. By the time all this data is moved, consolidated, and cleansed, it can take weeks to build a predictive model. Even with data lakes, efficiently integrating multi-structured data from different data sources and streams is a major challenge. Enterprises struggle with a stew of data integration tools, application integration middleware, and various data quality and master data management software. How can we simplify this complexity to accelerate and de-risk analytic projects? The data warehouse—once seen as only for traditional business intelligence applications — has learned new tricks. Join James Curtis from 451 Research and Pivotal’s Bob Glithero for an interactive discussion about the modern analytic data warehouse. In this webinar, we’ll share insights such as: - Why after much experimentation with other architectures such as data lakes, the data warehouse has reemerged as the platform for integrated operational analytics - How consolidating structured and unstructured data in one environment—including text, graph, and geospatial data—makes in-database, highly parallel, analytics practical - How bringing open-source machine learning, graph, and statistical methods to data accelerates analytical projects - How open-source contributions from a vibrant community of Postgres developers reduces adoption risk and accelerates innovation We thank you in advance for joining us. Presenter : Bob Glithero, PMM, Pivotal and James Curtis Senior Analyst, 451 Research
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