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. In contrast, Fluss adopts a Lakehouse-native design with structured tables, explicit schemas, and support for all kinds of data types; it directly mirrors the Lakehouse paradigm.

Kafka 75

Kafka Lambda Architecture SQL Architecture 75

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

Striim

AUGUST 22, 2024

What’s the difference between real-time analytics and streaming analytics? Streaming analytics focuses on analyzing data in motion, unlike traditional analytics, which deals with data stored in databases or data warehouses.

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

Edureka

FEBRUARY 7, 2023

Dynamic data masking serves several important functions in data security. It is possible to use Azure SQL Database, Azure SQL Managed Instance and Azure Synapse Analytics. It can be set up as a security policy on all SQL Databases in an Azure subscription. 7) Describe the Azure Synapse Analytics architecture.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Monte Carlo

MAY 17, 2023

Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly. When there is a schema change in our production database, Fivetran automatically rebuilds or materializes the new piece of data in a new table. ” 36. “Not

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly. When there is a schema change in our production database, Fivetran automatically rebuilds or materializes the new piece of data in a new table. ” 36. “Not

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Data 52