Data Engineering Weekly

FEBRUARY 18, 2025

Kafka is designed for streaming events, but Fluss is designed for streaming analytics. Architecture Difference The first difference is the Data Model. The fourth difference is the Lakehouse Architecture. Fluss embraces the Lakehouse Architecture. It excels in event-driven architectures and data pipelines.

Kafka 73

Kafka Lambda Architecture SQL Architecture 73

Striim

AUGUST 22, 2024

“Sometimes there’s so much data that old batch processing (late at night once a day or once a week) just doesn’t have time to move all data and hence the only way to do it is trickle feed data via CDC,” says Dmitriy Rudakov, Director of Solution Architecture at Striim.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

Knowledge Hut

JUNE 23, 2023

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.

Data Analytics 52

Data Analytics IT Transportation Analytics Architecture 52

ArcGIS

FEBRUARY 20, 2025

The integration of AutoCAD, Civil 3D, digital models (Revit), and ArcGIS Pro combines the strengths of each system

Systems 101

Systems Analytics Architecture Data Integration Data Management 101

Rockset

MARCH 19, 2020

With all of these stream processing and real-time data store options, though, also comes questions for when each should be used and what their pros and cons are. I hope by the end you find yourself better informed and less confused about the real-time analytics landscape and are ready to dive in to it for yourself.

Kafka 40

Kafka Database Process SQL 40

Edureka

FEBRUARY 7, 2023

Data entry into PDW is optimized by Polybase, which also supports T-SQL. It allows developers to query external data from supported data stores transparently, regardless of the storage architecture of the external data store. 7) Describe the Azure Synapse Analytics architecture.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Monte Carlo

MAY 17, 2023

System Modernization and Optimization The only constant in data engineering is change. This applies especially to your data architecture. Luckily, data observability can help with migrations, refactoring pipelines, and more. And your data engineers will thank you.” ” 36.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52