September 2016 Summaries

The latest release of Confluent Enterprise simplifies running Kafka in production at scale by introducing multi-datacenter replication, auto data balancing, and enhancements to Confluent Control Center. These new capabilities address the challenges of deploying Kafka across multiple data centers, enabling enterprises to improve durability, fault tolerance, security, and compliance while reducing costs. The Multi-DC Replication feature simplifies deploying Kafka for streaming analytics in a central data center, while auto-data balancing ensures optimal performance by automatically adjusting workload distribution across nodes. Additionally, Confluent Control Center now offers multi-cluster monitoring and alerting features to ensure data is being consumed within latency constraints. This release also marks the separation of Confluent Platform into two packages: pure open-source and enterprise offerings, providing easier access for those who want a production-ready Kafka-based streaming platform.

AWS is doing extremely well, with $2.57 billion in Q1 2016 revenue, a 64% increase from the previous year, indicating widespread adoption of its cloud services. Companies typically start by using cloud services for new applications or use-cases and then gradually migrate existing applications to the cloud, often requiring data synchronization between on-premises and cloud environments. This process can take years in mature organizations, highlighting the importance of planning and strategy. A common issue is having multiple, independent pipelines for different teams and applications, leading to increased operational costs and data quality concerns. To address this, a central-pipeline architecture using Apache Kafka can be implemented, streamlining data synchronization between on-premises and cloud deployments, reducing costs, accelerating migration times, and making the process more manageable and safer.

Confluent will be attending Strata + Hadoop World in NYC from September 26-29, 2016, with speaking sessions, tutorials, happy hours, book signings, and office hours. They will have a booth where attendees can connect with Confluent experts and learn about the future of streaming data. Confluent's activities include a tutorial on Apache Kafka, a talk by Neha Narkhede on the rise of real-time data and stream processing, office hours with Neha Narkhede, a book signing event, and a reception at Casa Nonna. Additionally, Confluent will explore resilient multi-data center architecture with Apache Kafka, securing Apache Kafka, and other topics relevant to streaming data applications. The company is also attending Current 2024 in Austin, Texas on September 17-18, where they will discuss Apache Kafka and Apache Flink with tech leaders, industry giants, and startups.

Apache Kafka 0.10.0.1 has been released with bug fixes, and Apache Kafka 3.8.0 is now available with many new features and improvements, including a new confluent-kafka-javascript client that will be used to build an app to produce and consume vote counts from a Kafka topic in JavaScript.

The Connect API in Kafka is a scalable and robust framework for streaming data into and out of Apache Kafka, powering modern streaming platforms. DataMountaineer has worked on numerous big data projects across various industries, using multiple components to handle diverse data sources. The Connect API simplifies this process by allowing developers to focus on moving data to and from Kafka. It keeps the ingest/egress layers clean and unpolluted by functional requirements, leaving business logic to stream processing engines like Streams API in Kafka, Apache Flink or Spark Streaming. The DataStax Certified Connector simplifies writing data from Kafka into Cassandra, supporting features such as field selection, topic-to-table routing, error policies for handling failures, and secure connections via SSL.

The text discusses the concept of event sourcing and its integration with Apache Kafka to build resilient and scalable application architectures, emphasizing its advantages and challenges. Event sourcing models changes in application states as immutable events, which are stored in logs like Kafka topics, allowing applications to subscribe and create materialized views. This approach provides benefits such as a complete audit log, independent scaling of reads and writes, and a loosely coupled architecture conducive to microservices. However, it also introduces a steep learning curve and complexity in querying event logs. The text further explores the Command Query Responsibility Segregation (CQRS) pattern, which complements event sourcing by separating the command side, responsible for state updates, from the query side, focused on efficient data retrieval. Kafka Streams plays a crucial role in enabling CQRS by providing stream processing capabilities and supporting both external and local state storage. The local state storage option in Kafka Streams offers improved performance and flexibility by keeping data close to the application, although it requires careful management. The text concludes by highlighting the benefits of using Kafka and Kafka Streams for event sourcing and CQRS, such as enhanced scalability, fault tolerance, and easier application upgrades, ultimately promoting a loosely coupled and forward-compatible application architecture.

The text discusses the release of Confluent Platform 3.0.1 and Apache Kafka 0.10.0.1, highlighting the introduction of Kafka Streams and the Confluent Control Center as major features in these versions. The new release, which includes several bug fixes and performance improvements, is recommended for users to upgrade for enhanced efficiency and functionality. Key enhancements include the ability to delete running connectors, monitor secure Kafka clusters, and utilize the new Application Reset Tool in Kafka Streams, which simplifies the process of resetting applications for various purposes such as A/B testing and development iteration. The release also addresses security, reliability, and performance issues, with contributions from 45 developers. Additionally, the document announces the general availability of Confluent Platform 7.7, which features enhanced security with OAuth support, integration with Apache Flink, and new connectors, alongside discussing the integration of Confluent with Amazon OpenSearch using OpenSearch Ingestion.

The comparison between Apache Flink and Kafka Streams API highlights their differences in deployment, management, and coordination models. Flink is a cluster framework that takes care of deploying the application, while the Streams API is an embeddable library that can be integrated into any standard Java application. This difference affects the ownership perspective, resource isolation, and separation of concerns between the two systems. The Streams API relies on Kafka's consumer group protocol for distributed coordination and fault tolerance, whereas Flink has a dedicated master node for global coordination. While both systems provide strong guarantees for stream processing, they cater to different use cases and architectural requirements, with Flink being suitable for applications deployed in existing clusters and the Streams API being more accessible as an application programming model for microservices.