Apache Spot (incubating) is used to analyze network data to detect infosec threats. This post provides a good overview of the architecture, which is built on Apache Kafka (for ingestion), Apache Spark (for ingestion and ML analysis), Apache Hadoop (for ingestion and storage), and more. Spot also uses the Python Watchdog library, which detects changes on the file system and fires off events.
There are many challenges facing organizations today as they initiate or continue their journey to digital transformation as well as the vital replatforming of the IT infrastructure. Whatever the specific problems that you and your organization may be facing, rest assured: you are not alone. These once-in-a-generation infrastructure overhauls are never easy. Not only does IT management have to deal with traditional and legacy infrastructure that is inadequate for the very high scale and low latency requirements of emerging technologies.
Amazon EMR enables data analysts and scientists to deploy a cluster running popular frameworks such as Spark, HBase, Presto, and Flink of any size in minutes. When you launch a cluster, Amazon EMR automatically configures the underlying Amazon EC2 instances with the frameworks and applications that you choose for your cluster. This can include popular web interfaces such as Hue workbench, Zeppelin notebook, and Ganglia monitoring dashboards and tools. These web interfaces are hosted on the EMR master node and must be accessed using the public DNS name of the master node (master public DNS value).
YARN is adding support for long-lived services in an upcoming release. While Hadoop is well behind Kubernetes when it comes to container deployment, YARN does have some compelling use cases such as Hive and its LLAP. There are a lot more details in the post, but in short YARN’s new service frameworks supports docker and deploying apps via a RESTful JSON API.