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Customers vary widely on the topic of public cloud – what data sources, what use cases are right for public cloud deployments – beyond sandbox, experimentation efforts. More and more business people want the access to that data to slice and dice as they have business ideas and assumptions that they want to explore.
A daily marketing report will also allow you for faster experimentation: running small operations to answer small questions. These reports are slicing, dicing, and analyzing data, while connects the dots between your marketing activities and the goals originally set. Why so much data analysis, in the end?
Adoption of AI/ML is maturing from experimentation to deployment. You also need visibility into prediction requests and the ability to slice and dice prediction data over time to have a complete understanding of the internal state of your AI/ML system. Model Observability Features. Manage Unpredictability in Active Deployments.
" In service of analysis the job includes: Pulling data, segmentation, slicing and dicing, drilling-up, drilling-down, drilling-around, modeling, creating unique datasets, answering business questions, writing requirements for data sources and structures for Reporting Squirrels to work with IT teams to create, etc.
It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. As you can see from the tiny confidence intervals on the graphs, big data ensured that measurements, even in the finest slices, were precise.
Druid hosted on Amazon Elastic Compute Cloud (Amazon EC2) integrates with the Kinesis data stream for streaming ingestion and allows users to run slice-and-dice OLAP queries. Spark Structured Streaming continuous processing is an experimental feature and provides at-least once guarantees.
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