今日のビジネス環境では、急速にデータ量が増え、リアルタイムでイベントに対応するという圧力が高まっているため、組織には、貴重な分析情報をより迅速に獲得し、競争上の優位性を高めるためのデータドリブン戦略が必要です。こうしたビッグ データの課題を解決するには、複数のプロデューサーとコンシューマーをサポートし、組織全体でデータ ストリームを接続する、大規模にスケーラブルな分散型のストリーミング プラットフォームが必要です。Apache Kafka と Azure Event Hubs では、分散型のプラットフォームを提供します。 Azure Event Hubs と Apache Kafka の違いは?
IP Filtering feature is now enabled for Azure Service Bus and Azure Event Hubs. This feature allows customers to control which IPs are accessing their resources.
Apache Kafka has changed the way we look at streaming and logging data, and now Azure provides tools and services for streaming data into your big data pipeline in Azure.
Event Hubs is expanding its ecosystem. To support multiple languages, the new additions of Python, Node.js and Go SDK’s add significant value to the service. This blog aims at announcing the public preview of these services.
Event Hubs Capture is the most easiest way to stream data into Azure with its simple setup, fully managed and providing a perfect on-ramp to compose Big data solutions.
Azure Event Hubs Capture is now generally available. This key Event Hubs feature (which was released in public preview as Event Hubs Archive), gives you access to near real-time streams, making it possible to package and transport millions of events per second for further processing.
Azure Event Hubs new auto-inflate lets you automatically scale-up the number of Throughput Units (TUs) on an Event Hubs namespace to meet usage needs. This feature prevents throttling when data ingress rates exceed set Throughput Units or when data egress rates exceed set TUs.
Of the many key scenarios for Event Hubs, are long term data archival and downstream micro-batch processing. Customers typically use compute (Event Processor Host/Event Receivers) or Stream Analytics jobs to perform these archival or batch processing tasks.
Azure Service Bus services will be coming soon to new Azure portal. Learn more about some of these upcoming changes.
