How can we become Elastic Certified Engineers?
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Google Cloud Machine Images: Simplifying Data Science VM Deployments
Google Cloud’s Machine Images feature lets system administrators and data scientists capture, clone, and restore full virtual machine environments — including multi-disk configurations and instance metadata — without manual reconfiguration.
Three Keys to a Successful Data-Driven Strategy in the New Decade
Data is no longer generated only in traditional data centres. As edge computing, multi-cloud adoption, and AI-driven analytics reshape the landscape, engineering and operations teams must rethink how they manage data wherever it lives.
Solving the Data Explosion with Fog and Edge Computing
As IoT device counts climb into the tens of billions, centralised cloud architectures struggle to keep pace. Edge and fog computing offer a layered solution that brings processing closer to the data source, cutting latency and improving performance for time-sensitive workloads.
2020 Data Predictions: Hybrid Cloud, Kubernetes Analytics, and the ML Framework Race
A look at the major data engineering trends predicted for 2020: hybrid cloud adoption, Kubernetes-based analytics stacks, the decline of HDFS, the ML framework consolidation, and the growing talent gap in data and AI roles.
Michael Berthold on End-to-End Data Science Using KNIME Analytics Platform
KNIME CEO Michael Berthold outlined a four-phase end-to-end data science cycle at the KNIME Fall Summit 2019, covering data gathering, modelling, deployment, and optimisation—along with three generations of ML automation.
Four Big Factors Shaping the Future of Data Science
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