Remove Java Remove Kafka Remove Relational Database
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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. What we’ll cover.

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Internet of Things (IoT) and Event Streaming at Scale with Apache Kafka and MQTT

Confluent

Apache Kafka ® and its surrounding ecosystem, which includes Kafka Connect, Kafka Streams, and KSQL, have become the technology of choice for integrating and processing these kinds of datasets. Microservices, Apache Kafka, and Domain-Driven Design (DDD) covers this in more detail. Example: Severstal.

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Building a Scalable Search Architecture

Confluent

Using SQL to run your search might be enough for your use case, but as your project requirements grow and more advanced features are needed—for example, enabling synonyms, multilingual search, or even machine learning—your relational database might not be enough. Building an indexing pipeline at scale with Kafka Connect.

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

Data Engineering Podcast

How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm? How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm? Can you start by describing what Flink is and how the project got started? What are some of the primary ways that Flink is used? How is Flink architected?

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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. gradlew composeUp. The KSQL pipeline flow.

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Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. Data engineers who previously worked only with relational database management systems and SQL queries need training to take advantage of Hadoop. Just for reference, Spark Streaming and Kafka combo is used by.

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The Evolution of Enforcing our Professional Community Policies at Scale

LinkedIn Engineering

At the heart of this system was a reliance on a relational database, Oracle, which served as the repository for all member restrictions data. Figure 2: Relational database schema We adopted a pragmatic and scalable approach by distributing member restrictions across different Oracle tables.

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