2018 Cloud Computing Database 
Smart Data Collective
JANUARY 22, 2020
In recent years, cloud computing has gained increasing popularity and proved its effectiveness. There is no doubt that cloud services are changing the business environment. Small companies value the ability to store documents in the cloud and conveniently manage them. Risks Associated with Cloud Computing.
Smart Data Collective
NOVEMBER 6, 2019
The world’s entire datasphere will grow from 33 to 175 zettabytes between 2018 and 2025, according to a Seagate and IDC white paper. Edge computing facilitates real-time alerts without the high prices and latency of streaming every byte of data to a server farm. Each smart system within a city relies on its own database.
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ODSC - Open Data Science
JULY 11, 2023
Hybrid data centers: This refers to a combination of different data center solutions such as using a mix of on-premises, co-location, and cloud-based data centers to meet specific needs. Not a cloud computer? Photo from unsplash.com Is cloud computing just using someone else’s data center? On [link] 9.
phData
MAY 25, 2023
Snowflake is a cloud computing–based data cloud company that provides data warehousing services that are far more scalable and flexible than traditional data warehousing products. Snowflake was originally launched in October 2014, but it wasn’t until 2018 that Snowflake became available on Azure.
IBM Journey to AI blog
FEBRUARY 9, 2024
Prior to the current hype cycle, generative machine learning tools like the “Smart Compose” feature rolled out by Google in 2018 weren’t heralded as a paradigm shift, despite being harbingers of today’s text generating services.
APRIL 16, 2025
Solution overview The AI-powered asset inventory labeling solution aims to streamline the process of updating inventory databases by automatically extracting relevant information from asset labels through computer vision and generative AI capabilities. The function updates the asset inventory database with the new extracted data.
AWS Machine Learning Blog
NOVEMBER 18, 2024
Large language models (LLMs) can help uncover insights from structured data such as a relational database management system (RDBMS) by generating complex SQL queries from natural language questions, making data analysis accessible to users of all skill levels and empowering organizations to make data-driven decisions faster than ever before.
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