Remove Article Remove Data Processing Remove Modeling Remove Structured Data
article thumbnail

5 Pain Points of Moving Data to the Cloud and Strategies for Success

Alation

Yet increasing complexity of data makes the old “lift-and-shift” model not just unrealistic, but risky. Businesses with complex data environments need a migration method that takes that complexity into account. The Data Race to the Cloud. This recent cloud migration applies to all who use data. Fern Halper, Ph.D.

article thumbnail

How to Take Back 40-60% of Your IT Spend by Fixing Your Data

Ontotext

The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context. However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight.

IT 69
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Migrate Hive data from CDH to CDP public cloud

Cloudera

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. Pre-Check: Data Lake Cluster. Verifying the Replication on CDP.

article thumbnail

Conversational AI: Design & Build a Contextual Assistant – Part 2

CDW Research Hub

A previous version of this article was published on Medium. In this post, we’ll look at structuring happy and unhappy conversation paths, various machine learning policies and configurations to improve your dialogue model, and use a transfer learning-based language model to generate natural conversations.

article thumbnail

Conversational AI: Design & Build a Contextual Assistant – Part 1

CDW Research Hub

Level 5 and beyond : at this level, contextual assistants are able to monitor and manage a host of other assistants in order to run certain aspects of enterprise operations. These deep learning models can analyze large volumes of text and provide things like text summarization, language translation, context modeling, and sentiment analysis.

article thumbnail

On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

Such problems and the complexities related to such computationally-intensive tasks are essential in the fields of weather forecasting, molecular modeling, airplane and spacecraft aerodynamics, personalized medicine, self-driving cars. Behind the scenes of linking histopathology data and building a knowledge graph out of it.

article thumbnail

How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

This blog will focus more on providing a high level overview of what a data mesh architecture is and the particular CDF capabilities that can be used to enable such an architecture, rather than detailing technical implementation nuances that are beyond the scope of this article. Introduction to the Data Mesh Architecture.

Metadata 123