There is a lot of focus on big data, but sometimes it makes sense to look at the small data at hand and explore how to drive value from that. In this webinar, Mike Weston will present how to iteratively work with the data available, from e.g. purchase patterns, web browsing or location data, and gradually grow big insights.
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Lonne Jaffe of Syncsort gives his perspective on Big Data in this interview with SiliconAngle's theCUBE.


Code-First Data Science: Power Your DataRobot Projects Using Cloud-Hosted Notebooks

For ‘code-first’ data scientists notebooks are an essential part of your toolkit, but they have serious limitations when they’re run locally on laptops. In this informative session we will outline how DataRobot’s latest acquisition, Zepl, brings the power of self-service and cloud-hosted notebooks to you and your team.
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3 Critical Skills for Every Accidental MariaDB & MySQL DBA

If you answered YES to any of these questions, this session is for you! There’s never been a more challenging, yet more exciting time to be a MySQL or MariaDB database administrator.
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Data Confidentiality & Utility: A Way Forward Together

Enterprises in data driven fields such as finance and healthcare must balance data utility with confidentiality. Often, enterprises and their partners want to analyze or monetize non-public or personally identifiable information, often at odds with growing privacy regulations and intellectual property concerns. Innovations in bu
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Moving from centralized data platform to a federated data mesh

In this webinar, we will cover the pros and cons of building a centralized data lake vs federated data mesh. Traditionally data warehouses are built on the premise of centralized data. This requires team, process and tool alignment which adds significant complexity and layers of process. Oftentimes the internal conflicts lead to subpar data management and quickly fragments to siloed data processing and insights. We will provide our unbiased view on building federated data mesh and the benefits of building an operational metrics layer.
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