Data Engineering Weekly

FEBRUARY 18, 2025

Fluss is a streaming storage specifically designed for real-time analytics. It addresses many of Kafka's challenges in analytical infrastructure. The combination of Kafka and Flink is not a perfect fit for real-time analytics; the integration of Kafka and Lakehouse is very shallow.

Kafka 73

Kafka Lambda Architecture SQL Architecture 73

Data Engineering Podcast

AUGUST 21, 2022

Can you describe what is driving the adoption of real-time analytics? Can you describe what is driving the adoption of real-time analytics? Contact Info LinkedIn @shrutibhat on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?

Lambda Architecture 100

Lambda Architecture MongoDB MySQL Scala 100

Rockset

MARCH 19, 2020

Intro In recent years, Kafka has become synonymous with “streaming,” and with features like Kafka Streams, KSQL, joins, and integrations into sinks like Elasticsearch and Druid, there are more ways than ever to build a real-time analytics application around streaming data in Kafka.

Kafka 40

Kafka Database Process SQL 40

DataKitchen

AUGUST 19, 2021

Some designs perform process linkage with an event bus that marks the completion of a DAG by putting an event on a Kafka queue, using a publish/subscribe model. Data mesh is a powerful design pattern that leading enterprises are using to organize their enterprise analytics architectures. Finally, there is development linkage.

Pharmaceutical 98

Pharmaceutical Data Lake Data Warehouse Raw Data 98

Data Engineering Weekly

NOVEMBER 13, 2022

link] Uber: Uber Freight Near-Real-Time Analytics Architecture Uber writes about its Uber Fright architecture highlighting how it archives data freshness, latency, reliability, and accuracy. Swiggy writes its adoption of CDC with Schema evolution and reconciliation engine to handle the late-arriving & unordered data.

Data Engineer 52

Data Engineer Data Engineering Engineering Kafka 52

Knowledge Hut

FEBRUARY 27, 2023

Some of the most common responsibilities of data engineers include Data collection Matching the architecture to the business his needs Discovering tasks that can be automated using data Using advanced analytics programs, machine learning, and statistical techniques Updating stakeholders based on analytics Architecture development, building, testing, (..)

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52