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Best Practices for Analyzing Kafka Event Streams

Rockset

Apache Kafka has seen broad adoption as the streaming platform of choice for building applications that react to streams of data in real time. In many organizations, Kafka is the foundational platform for real-time event analytics, acting as a central location for collecting event data and making it available in real time.

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Kafka Connect Deep Dive – JDBC Source Connector

Confluent

One of the most common integrations that people want to do with Apache Kafka ® is getting data in from a database. That is because relational databases are a rich source of events. The existing data in a database, and any changes to that data, can be streamed into a Kafka topic. Introduction.

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Deploying Kafka Streams and KSQL with Gradle – Part 2: Managing KSQL Implementations

Confluent

In part 1 , we discussed an event streaming architecture that we implemented for a customer using Apache Kafka ® , KSQL from Confluent, and Kafka Streams. In part 3, we’ll explore using Gradle to build and deploy KSQL user-defined functions (UDFs) and Kafka Streams microservices. Introduction. gradlew composeUp.

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Oracle CDC Source Premium Connector is Now Generally Available

Confluent

One of the most common relational database systems that connects to Apache Kafka® is Oracle, which often holds highly critical enterprise transaction workloads. While Oracle Database (DB) excels at many […].

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Turning Streams Into Data Products

Cloudera

In 2015, Cloudera became one of the first vendors to provide enterprise support for Apache Kafka, which marked the genesis of the Cloudera Stream Processing (CSP) offering. Today, CSP is powered by Apache Flink and Kafka and provides a complete, enterprise-grade stream management and stateful processing solution. Who is affected?

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Real-Time CDC With Rockset And Confluent Cloud

Rockset

Breaking Bad… Data Silos We haven’t quite figured out how to avoid using relational databases. Folks have definitely tried, and while Apache Kafka® has become the standard for event-driven architectures, it still struggles to replace your everyday PostgreSQL database instance in the modern application stack.

Cloud 52
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Stateful, Distributed Stream Processing on Flink with Fabian Hueske - Episode 57

Data Engineering Podcast

Summary Modern applications and data platforms aspire to process events and data in real time at scale and with low latency. How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm? How do you handle out of order events in Flink, especially as the delay for a given event increases?

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