article thumbnail

Beyond Kafka: Conversation with Jark Wu on Fluss - Streaming Storage for Real-Time Analytics

Data Engineering Weekly

Fluss is a compelling new project in the realm of real-time data processing. Kafka is designed for streaming events, but Fluss is designed for streaming analytics. Architecture Difference The first difference is the Data Model. How do you compare Fluss with Apache Kafka?

Kafka 74
article thumbnail

What is Real-time Data Analytics and Why is it Important?

Knowledge Hut

Real-time data analytics is an essential innovation that enables companies to act quickly on data. By this year, more than half of business systems would base choices on current context data. This demonstrates the rising significance of real-time analytics architecture in the hectic corporate climate of today.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

An In-Depth Guide to Real-Time Analytics

Striim

Streams of data are continuously queried with Streaming SQL , enabling correlation, anomaly detection, complex event processing, artificial intelligence/machine learning, and live visualization. Because of this, streaming analytics is especially impactful for fraud detection, log analysis, and sensor data processing use cases.

article thumbnail

Top-Paying Data Engineer Jobs in Singapore [2023 Updated]

Knowledge Hut

A data engineer is a key member of an enterprise data analytics team and is responsible for handling, leading, optimizing, evaluating, and monitoring the acquisition, storage, and distribution of data across the enterprise. Data Engineers indulge in the whole data process, from data management to analysis.

article thumbnail

Azure Data Engineer Interview Questions -Edureka

Edureka

Users are given the choice to query data on specific terms for using either serverless on-demand or scale-out provisioned resources. 7) Describe the Azure Synapse Analytics architecture. It is intended to process enormous amounts of data, including tables with hundreds of millions of rows.