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Data Overload and Storage Limitations As IoT and especially industrial IoT -based devices proliferate, the volume of data generated at the edge has skyrocketed. Key issues include: A shortage of edge-native dataengineers and architects. High costs of training and retaining talent.
However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.
STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager DataEngineering and Michael Floyd AWS Head of Sustainability Solutions. SUS312 How innovators are driving more sustainable manufacturing — Marcus Ulmefors Northvolt Director Data and ML Platforms and Muhammad Sajid AWS SA.
They require teams of dataengineers to spend months building complex data models and synthesizing the data before they can generate their first report. Let’s walk through some of the core experiences of QuickSight that make it so easy to set up, connect to your data sources, and build visualizations in minutes.
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