Altexsoft
MARCH 16, 2021
In this article, we will explain the concept and usage of Big Data in the healthcare industry and talk about its sources, applications, and implementation challenges. What is Big Data and its sources in healthcare? So, what is Big Data, and what actually makes it Big? Let’s see where it can come from.
Trigent
APRIL 16, 2021
The role of technology in the education industry has witnessed some monumental trendsetters, right from 2019, which saw the advent of Big Data , Internet of Things (IoT), and Machine Learning. Artificial Intelligence (AI) has also been a significant contributor, revolutionizing education.
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Agile Engine
OCTOBER 24, 2019
Our portfolio includes projects in bleeding-edge industries like aerospace, blockchain, IoT, AR, VR, and machine learning. This year’s 50 on Fire mentions big names like Lyft, and pioneers like Aperiomics, the healthcare startup that is bringing big data to DNA testing. About AgileEngine.
Altexsoft
SEPTEMBER 23, 2020
You don’t see it screaming at you from headlines, startups are not using it on the landing pages, and it’s slowly climbing up Garner’s Hype Cycle for Healthcare Providers , overpassed by medical IoT, Blockchain, and analytics. It exists on the pages of peer-reviewed medical journals and research papers.
Altexsoft
AUGUST 31, 2020
The data is already being stored digitally: more than 95 percent of health facilities report implementing Electronic Health Records (EHR) systems. Large part of data comes from widely adopted IoT devices used in 60 percent of hospitals in the US today. Data analytics can also operate different sources.
Hu's Place - HitachiVantara
APRIL 23, 2019
She said working with Hitachi Vantara on storage, cloud, IoT, and Big Data Analytics, was like discovering a new planet. A recent Wall Street Journal blog reports that: “She (Renee McKaskle) credits the Hitachi Inc. With the help of her Hitachi Manager, she applied to Bo?azici
Confluent
FEBRUARY 6, 2019
And you can use it in any environment: in the cloud, in on-prem datacenters or at the edges, where IoT devices are. Say you wanted to build one integration pipeline from MQTT to Kafka with KSQL for data preprocessing, and use Kafka Connect for data ingestion into HDFS, AWS S3 or Google Cloud Storage, where you do the model training.
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