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Kafka

Hot and cold data with Apache Kafka, Tiered Storage, and Iceberg

Utilizing the true potential of data streaming is key to business success. In this Data (R)evolution episode, we're joined by Josep Prat and Filip Yonov to dive into the transformative features of Apache Kafka and its evolving role in data architecture. They discuss the critical importance of collaboration and feedback in enhancing Kafka's capabilities, the future of "lake house" technology, exciting updates from the Open Source Program Office (OSPO), and the importance of Kafka's readiness to support evolving data formats—making it a backbone for modern data ecosystems.

The Top 8 Kafka Monitoring Tools

Apache Kafka has risen as a pivotal element in modern distributed systems, transforming data processing, storage, and distribution across diverse applications. Kafka, developed by Kafka, is an open-source distributed event streaming platform. It is designed to efficiently manage high volumes of real-time data, acting as a distributed messaging system.

The Challenges of Partition Rebalancing in Kafka Brokers and Effective Monitoring Strategies

Apache Kafka has become an essential component in data streaming and processing architectures due to its high throughput and scalability. However, as organizations scale up their Kafka usage, they often encounter challenges such as partition rebalancing across different brokers. This imbalance can lead to significant issues, including overloaded partitions that jam traffic, affecting performance and reliability.

Investigating Mysterious Kafka Broker I/O When Using Confluent Tiered Storage

Earlier this year, we upgraded from Confluent Platform 7.0.10 to 7.6.0. While the upgrade went smoothly, there was one thing that was different from previous upgrades: due to changes in the metadata format for Confluent’s Tiered Storage feature, all of our tiered storage metadata files had to be converted to a newer format.

Fixing Kafka Streams Uneven Tasks Distribution at Logz.io

At Logz.io we provide an observability platform with the ability to ship logs, metrics, and traces and then interact with them using our app. LogMetrics is an integral part of our observability offering, which bridges the gap between logs and metrics. It provides the seamless conversion of one type of signal to another. It empowers our customers to gain critical insights faster while also reducing their monitoring bill.

Secure, Segregated Multi-tenant Analytics in PostgreSQL using Aiven for Apache Kafka, Debezium, and Aiven for ClickHouse

Enabling secure and performant multi-tenant analytics for your PostgreSQL® deployments on Aiven's data platform. In the realm of multi-tenant Software-as-a-Service (SaaS) applications, managing a centralized PostgreSQL® database for multiple customers can present challenges in maintaining secure segregation of their data. While a single database offers infrastructure efficiency, it becomes crucial to ensure each organization has isolated access and control over their information.

Introduction to Apache Kafka

Have you heard about Apache Kafka but aren’t quite sure about its functions or applications? This webinar is tailored for you. Apache Kafka is more than just a buzzword in the tech community; it’s a critical tool for data processing and management. Join our expert-led webinar to explore the world of Apache Kafka, a powerful distributed event streaming platform. Learn how Canonical simplifies Kafka operations, offering secure, automated deployments and maintenance across various clouds.

Canonical announces the general availability of Charmed Kafka

27 February 2024: Today, Canonical announced the release of Charmed Kafka – an advanced solution for Apache Kafka® that provides everything users need to run Apache Kafka at scale. Apache Kafka is an event store that supports a range of contemporary applications including microservices architectures, streaming analytics and AI/ML use cases. Canonical Charmed Kafka simplifies deployment and operation of Kafka across public clouds and private data centres alike.