Gravwell recently introduced a new ingester which accepts entries via HTTP POST requests. Now it's easy to send arbitrary data to Gravwell via scripts using only the curl command. In this blog post, we'll use the HTTP ingester to build a weather-monitoring dashboard!
We’re pleased to announce the release of Gravwell 2.2.1! For a point release, it’s got some very cool new features; read on to learn what we’ve added.
Gravwell Community Edition is perfect for monitoring your home network. With a generous 2GB/day ingest quota, you can capture netflow records, DNS requests, WiFi hotspot associations, and more. In this blog post, we’ll show how to ingest and analyze netflow records. We’ll assume you’ve already set up a Gravwell instance as described in the quickstart (https://dev.gravwell.io/docs/#!quickstart/community-edition.md); for this post, we’ll assume the Gravwell instance is at 192.168.1.52. Your instance will almost certainly be different, so be sure to substitute your own information.
Thanks to Gravwell's Google PubSub ingester, it's easy to collect logs and other data from services deployed in the Google Cloud Platform. In this blog post, we'll show how to set up Gravwell in GCP and ingest system logs from your virtual machines.
With the release of Gravwell 2.0, Gravwell customers can now deploy multiple webservers tied to a central storage system. This means you can deploy multiple webservers behind a load balancer for better search performance; the webservers synchronize resources, user accounts, dashboards, and search history behind the scenes so users don’t need to worry about which server they’re actually using.
Amazon’s Kinesis Streams service provides a powerful way to aggregate data (logs, etc.) from a large number of sources and feed that data into multiple data consumers. For instance, a large enterprise might use one Kinesis stream to gather log data from their cloud infrastructure and another stream to aggregate sales data from the web services running on that infrastructure. Once the data is in the stream, it remains available for up to a day (or optionally longer) for any number of applications to read it back for processing and analysis. This is particularly useful to customers that want to deploy and destroy virtual machines on a whim; data is stored in the stream, rather than the ephemeral VMs.