Remove Data Engineering Remove Innovation Remove Logistics
article thumbnail

These 7 Edge Data Challenges Will Test Companies the Most in 2025

VoltDB

Data Overload and Storage Limitations As IoT and especially industrial IoT -based devices proliferate, the volume of data generated at the edge has skyrocketed. Managing and storing this data locally presents logistical and cost challenges, particularly for industries like manufacturing, healthcare, and autonomous vehicles.

IoT 52
article thumbnail

5 key areas for tech leaders to watch in 2020

O'Reilly

Along with R , Python is one of the most-used languages for data analysis. From pre-built libraries for linear or logistic regressions, decision trees, naïve Bayes, k-means, gradient-boosting, etc., there’s a Python library for virtually anything a developer or data scientist might need to do. This follows a 3% drop in 2018.

article thumbnail

Expanding the Cloud: Introducing Amazon QuickSight

All Things Distributed

We live in a world where massive volumes of data are generated from websites, connected devices and mobile apps. In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data.

Cloud 112