Data Engineering IoT Transportation 
CIO
SEPTEMBER 12, 2022
The chief information and digital officer for the transportation agency moved the stack in his data centers to a best-of-breed multicloud platform approach and has been on a mission to squeeze as much data out of that platform as possible to create the best possible business outcomes. Data engine on wheels’.
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.
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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.
CTOvision
JUNE 15, 2015
They also launched a plan to train over a million data scientists and data engineers on Spark. As data and analytics are embedded into the fabric of business and society –from popular apps to the Internet of Things (IoT) –Spark brings essential advances to large-scale data processing.
CIO
FEBRUARY 14, 2023
Titanium Intelligent Solutions, a global SaaS IoT organization, even saved one customer over 15% in energy costs across 50 distribution centers , thanks in large part to AI. An enterprise data ecosystem architected to optimize data flowing in both directions.
Altexsoft
FEBRUARY 8, 2021
Supply chain practitioners and CEOs surveyed by 6river share that the main challenges of the industry are: keeping up with the rapidly changing customer demand, dealing with delays and disruptions, inefficient planning, lack of automation, rising costs (of transportation, labor, etc.), Analytics in logistics and transportation.
Confluent
FEBRUARY 6, 2019
This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Impedance mismatch between data scientists, data engineers and production engineers. For now, we’ll focus on Kafka.
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