Smart Data Collective

SEPTEMBER 23, 2020

Type of Data: structured and unstructured from different sources of data Purpose: Cost-efficient big data storage Users: Engineers and scientists Tasks: storing data as well as big data analytics, such as real-time analytics and deep learning Sizes: Store data which might be utilized.

Data Lakes 135

Data Lakes Data Warehouse Big Data Big Data 135

IBM Journey to AI blog

SEPTEMBER 4, 2024

Producers and consumers A ‘producer’, in Apache Kafka architecture, is anything that can create data—for example a web server, application or application component, an Internet of Things (IoT) , device and many others. Here are a few of the most striking examples.

Apache Kafka 58

Apache Kafka Internet of Things Data Pipeline Clustering 58

IBM Journey to AI blog

AUGUST 6, 2024

Big data analytics Serverless dramatically reduces the cost and complexity of writing and deploying code for big data applications. Additionally, serverless’ always-on capabilities mean data pipelines can be designed in a way to react to real-time changes in data and change application logic accordingly.

Cloud Computing 45

Cloud Computing Internet of Things Big Data Big Data 45

AWS Machine Learning Blog

NOVEMBER 12, 2024

Discoveries and improvements across seed genetics, site-specific fertilizers, and molecule development for crop protection products have coincided with innovations in generative AI , Internet of Things (IoT) and integrated research and development trial data, and high-performance computing analytical services.

AWS 101

AWS AI AI Data Lakes 101