Analytics Vidhya

SEPTEMBER 22, 2022

Dale Carnegie” Apache Kafka is a Software Framework for storing, reading, and analyzing streaming data. The Internet of Things(IoT) devices can generate a large […]. Introduction “Learning is an active process. We learn by doing. Only knowledge that is used sticks in your mind.-

Data Pipeline 225

Data Pipeline Apache Kafka Internet of Things Data Science 225

Pickl AI

JULY 24, 2023

Real-Time Data Ingestion Examples Here are some examples of real-time data ingestion applications: Internet of Things (IoT) Devices: IoT devices generate a vast amount of data, such as temperature, humidity, location, and sensor readings.

Internet of Things 52

Internet of Things Apache Kafka ETL Azure 52

ODSC - Open Data Science

MAY 31, 2023

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

Data Lakes 131

Data Lakes Machine Learning Machine Learning Apache Kafka 131

Pickl AI

JULY 20, 2023

IoT (Internet of Things) Analytics Projects: IoT analytics involves processing and analyzing data from IoT devices to gain insights into device performance, usage patterns, and predictive maintenance. Implement real-time analytics to monitor trends or anomalies in the data.

Analytics 40

Analytics Analytics Big Data Big Data 40

The MLOps Blog

FEBRUARY 5, 2023

There are a number of tools that can help with streaming data collection and processing, some popular ones include: Apache Kafka : An open-source, distributed event streaming platform that can handle millions of events per second. It can be used to collect, store, and process streaming data in real-time.

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

Data Science Dojo

JULY 24, 2023

Internet of Things (IoT) Data Processing: Stream processing is vital for handling continuous data streams from IoT devices, enabling real-time monitoring and control. Fraud Detection: Stream processing allows the identification of fraudulent activities in real-time, helping prevent financial losses and ensuring data security.

Big Data 195

Big Data Big Data Data Engineering Data Engineer 195