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Ubiquitous (Augmented) Analytics

Analytics today is a necessity that has re-defined traditional ways to make decisions by enabling making inferences from massive amount of relational as well as non-relational data sources. Augmented analytics is a next-generation data and analytics paradigm that uses ML to automate data preparation, insight discovery and insight sharing for a broad range of business…

Storage for unstructured data (I/O) processing

In the last few blogs we have discussed variety of analytics related problems, edge-side processing & use-cases. The kind of data that we talked about was primarily unstructured data – one without schema like video, audio, images, social media texts, docs etc. This data keeps on growing as we all know thereby resulting in the…

Edge is Cool ($) but Cloud is Hot ($$$)

This is becoming more real. Here Coolness of Edge & Hotness of Cloud refers to the tax in terms of Cost we need to incur to get the capabilities of Smart Edge & Smart Cloud. Let me expand Tax part here with an example. I’ve taken variable cost into account here given the scale we…

5 (IO) Senses matrix – Proactive Anomaly Detection

We can have complimenting capabilities using Smart Cloud & Edge technologies powered by Machine Learning (ML)/Deep Learning (DL)/Smart Algorithms to assist, extend & enrich 5 basic human senses’ capabilities even further. We can call such capabilities as digitized (electronic) version of human senses. Let’s restrict to fundamental 5 human senses here – Vision, Hearing, Taste,…

Quality of Service (QoS) for Edge

We are at an interesting juncture for Edge services getting platforms to become more intelligent! One recent example of this is Google’s TPU (https://cloud.google.com/edge-tpu/) on edge giving us capability to do AI! Other great examples are – AWS Greengrass (https://aws.amazon.com/greengrass/) enabling us to run compute, caching, ML capabilities for edge side computing, as well as…

Use CasesBelow Unified Data & Cloud Use Cases are being addressed

Data Insights

Insights for semi-structured data such as CSVs, JSON by detecting their respective schema to help Data Engineers, Analysts for further data processing


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