Kyvos Insights, a big data analytics company, today announced that it will host a webinar “Implementing BI on Big Data Best Practices” on Wednesday, December 13, 2017 at 1 p.m. Eastern time/10 a.m. Pacific time. Drawing on examples from the many enterprises that rely on Kyvos, the webinar will show how new technologies and practices enable organizations to provide self-service, interactive business intelligence (BI) on big data for thousands of concurrent users, including business users with little or no data science skills.

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“The problem with self-service BI has always been two-fold: business users lack the technical acumen to parse data as they wish and SQL on Hadoop solutions typically slow to a crawl as concurrent access increases,” said Ajay Anand, vice president of products at Kyvos. “We’ve solved these issues and radically changed how our customers use their big data. Now thousands of business users can simultaneously, and quickly and easily, analyze massive data sets with hundreds of billions of rows and hundreds of dimensions – all with the nearly instantaneous responses times needed to drive business decisions that make an impact.”

Many Fortune 100 firms rely on Kyvos. Its breakthrough technology makes it possible to create data cubes with near limitless scalability and performance, and a BI Consumption Layer that enables them to be stored and managed with ease for unmatched levels of scalability, performance and support. Kyvos also provides row-and-column level access control to ensure the security of enterprises’ big data, and works with a wide range of BI tools, including Tableau, Microsoft Power BI, Microsoft Excel, Qlik, MicroStrategy, IBM Cognos and SAP Business Objects.

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Presented by Anand, the webinar will be of interest to developers, data scientists, information technology (IT) professionals and any business users that need to interactively explore and analyze big data to develop real-time business insights. Some of the topics Anand will cover include:

• The BI components of a big data solution, including data ingestion, ETL, governance, security, and visualization;
• Key performance metrics and service level agreements (SLAs) for a successful deployment; and
• Real world examples of how enterprises have implemented a BI Consumption Layer to drive enterprise wide adoption of big data.

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