Cloud Seer

Integrates anything, anywhere, on-demand and as-is.

Cloud Seer is an intelligent data pipeline engine that allows data to be transformed and integrated so its “AI Ready.” Cloud Seer can process structured, unstructured and IoT data from internal systems or external sources such as websites. Cloud Seer identifies and enriches any data source in minutes and makes these details available for use by the other products in the App Orchid platform.

Components of Cloud Seer

Cloud Seer Key Features

Wide Range of Data Connectors

Out of the box data connectors for structured, unstructured, time series and experimental data sources empowers users to prototype and deploy production grade applications with little effort and governance

Data Processing Layer

A visually rich data pipeline processor allows connectors to a wide range of sources, enables a wide range of analytical and AI based transformations and provides for write options to a range of cloud and on-premise data sinks

Native and Hybrid-Cloud Ready

Native capabilities purpose-built for cloud providers like AWS & Azure, help to exploit the home-grown advantages of popular cloud vendors without sacrificing the cross-purpose abstraction and portability across a hybrid ecosystem

“Data-As-A-Service”

Built on top of popular open source options like Apache Dremio, the Data- as-a-service stack provides a multi-modal, SQL-like query framework that cuts across multiple, disparate data sources, providing a homogenous, democratized data analytics layer across large scale heterogenous data silos

Data Tiering

A Big Data scale, homogenous data tiering architecture that provides a “hot, warm, cold” delineation of storage options that helps optimize technological and economic advantages of various cloud and on-premise storage options

Auto-Modeling

A cloud ready workbench that uses Machine learning and AI to convert business questions and analytical requirements into semantic, logical and physical models based on a Natural Language framework that interprets intent and translates it into an optimum execution plan

Auto-Preparation

A business friendly cloud ready workbench for sampling of large-scale data to help with data preparation for various machine learning and statistical modeling functions. Ready access to hundreds of quality, audit, data-shaping and data profiling transformations that help to convert raw data into business and semantic artifacts

Schema On Read

A dynamic SQL plan fabrication framework that can be containerized in a scalable way in order to translate the query intent into an optimum schema

To learn more about the App Orchid platform

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