Data Engineering Scalability Transportation 
Altexsoft
JUNE 25, 2019
If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.
Altexsoft
AUGUST 25, 2020
Big data is tons of mixed, unstructured information that keeps piling up at high speed. That’s why traditional data transportation methods can’t efficiently manage the big data flow. Big data fosters the development of new tools for transporting, storing, and analyzing vast amounts of unstructured data.
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Apiumhub
JANUARY 26, 2022
Data engineer roles have gained significant popularity in recent years. Number of studies show that the number of data engineering job listings has increased by 50% over the year. And data science provides us with methods to make use of this data. Who are data engineers?
CIO
FEBRUARY 14, 2023
Supply chain With companies trying to stay lean with just-in-time practices, it’s important to understand real-time market conditions, delays in transportation, and raw supply delays, and adjust for them as the conditions are unfolding. It’s also used to deploy machine learning models, data streaming platforms, and databases.
CIO
FEBRUARY 13, 2023
Besides surgery, the hospital is also investing in robotics for the transportation and delivery of medications. Massive robots are being used in pharmacies to automate processes such as pulling pills, ointments, and creams, putting them into packs, sealing them, and transporting them to floors, he says.
Mobilunity
AUGUST 3, 2021
Technologies that have expanded Big Data possibilities even further are cloud computing and graph databases. The cloud offers excellent scalability, while graph databases offer the ability to display incredible amounts of data in a way that makes analytics efficient and effective. Who is Big Data Engineer?
Confluent
FEBRUARY 6, 2019
Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way.
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