Based on our record, Apache Storm should be more popular than Azure Event Hubs. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
We're looking into some sort of cloud-based solution to route our Palo Alto firewall logs to across our customer base. I'm with an MSP that manages over a hundred PA firewalls. I was intrigued by the Event Hubs (https://azure.microsoft.com/en-us/products/event-hubs/) solution as a way to push logs to it and then ingest them from there into our SIEM, without having to deal with challenges of multi-tenancy and... Source: over 1 year ago
Microsoft released Azure Stream Analytics no-code editor, a drag-and-drop canvas for developing jobs for stream processing scenarios such as streaming ETL, ingestion, and materializing data to data into general availability. The no-code editor is hosted in the company’s big-data streaming platform and event ingestion service, Azure Event Hubs. Interestingly, the offering follows up after Confluent's recent release... Source: over 1 year ago
Sometimes you don’t need an entire Java-based microservice. You can build serverless APIs with the help of Azure Functions. For example, Azure functions have a bunch of built-in connectors like Azure Event Hubs to process event-driven Java code and send the data to Azure Cosmos DB in real-time. FedEx and UBS projects are great examples of real-time, event-driven Java. I also recommend you to go through 👉 Code,... - Source: dev.to / over 1 year ago
For event infrastructure, we have a bunch of options, like Azure Service Bus, Azure Event Grid and Azure Event Hubs. Like the databases, they aren't mutually exclusive and I could use all, depending on the circumstance, but to keep things simple, I'll pick one and move on. Right now I'm more inclined towards Event Hubs, as it works similarly to Apache Kafka, which is a good fit for the presentation context. - Source: dev.to / about 3 years ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 1 year ago
Storm, a system for real-time and stream processing. - Source: dev.to / over 1 year ago
Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 1 year ago
Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 2 years ago
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Amazon Elasticsearch Service - Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch in the AWS Cloud.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.