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How to Use KSQL Stream Processing and Real-Time Databases to Analyze Streaming Data in Kafka

Rockset

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.

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An Exploration Of The Expectations, Ecosystem, and Realities Of Real-Time Data Applications

Data Engineering Podcast

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?

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

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.

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

Knowledge Hut

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, (..)

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Data Engineering Weekly #107

Data Engineering Weekly

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.