When you first set up your Anodot account, you create alerts on the KPIs that matter most to you. Advanced alert configurations enable you to define various parameters so that you only get alerts that are important to you: selecting the metric, building a query, grouping the data by dimensions, selecting triggers and conditions, choosing who and where it should be sent to, and so on.
With AI analytics slated as the biggest disruptor to big data and analytics, data leaders are quickly integrating this capability into their data strategy. Itzik Feldman, data engineering manager at Atlassian, the enterprise software company responsible for Jira and Trello, recently credited Anodot with helping keep the company’s 3,000 employees in touch with product performance and customer experience.
All engineering teams strive to build the best product they can as quickly as possible. Some, though, stumble into a false dichotomy of choosing between speed and quality. While that choice may have been necessary in the past, it’s not the case today. What I’d like to do in this article is explain why.
Today we are officially releasing Graylog v3.1.1 This release brings a whole new AWS Kinesis/CloudWatch Input to Graylog. The new input guides the user through the setup process and performs validation checks along the way. It also supports an automated CloudWatch Logs to Kinesis Streams setup, eliminating the complexity of manual setup.
We hope you guys managed to rest over the summer because we sure didn’t. Our engineering team has been working hard on developing new features and enhancements, some of which may have flown under your radar. To help you catch up, here’s a short recap of the latest and greatest from Logz.io with relevant referrals to read up more about the different items.
At Coralogix, we strive to ensure that our customers get a stable, real-time service at scale. As part of this commitment, we are constantly improving our data ingestion pipeline resiliency and performance. Coralogix ingests messages at extremely high rates — up to tens of billions of messages per day. Every one of these records needs to go through our entire pipeline at near real-time rates: validation, parsing, classification, and ingestion to Elasticsearch.
For a lot of DevOps engineers and SREs, a Grafana dashboard is often the beginning of a troubleshooting procedure. It might be an alert in Slack or a colleague pointing out anomalous system behavior. Or maybe it’s just part of your day-to-day monitoring workflow. Whatever the reason, staring at a beautiful Grafana dashboard is the starting point of what can be either a long and excruciating process, or a short and efficient one.
