When it comes to dashboarding, Kibana is king. Since its release Kibana has changed the way businesses visualize data. Kibana is a fairly intuitive platform and offers some seriously impressive ways to visualize data. In this kibana dashboard tutorial, we are going to help you unlock the full potential of the platform and help you become a Kibana guru. When it comes to visualizing data Kibana is well suited for monitoring logs, application monitoring, and operational intelligence.

You may have noticed how on sites like Google you get suggestions as you type. With every letter you add, the suggestions are improved, predicting the query that you want to search for. Achieving Elasticsearch autocomplete functionality is facilitated by the search_as_you_type field datatype. This datatype makes what was previously a very challenging effort remarkably easy.

After installing the Coralogix Security Traffic Analyzer (STA) and choosing a mirroring strategy suitable for your organization needs (if not, you can start by reading this) the next step would be to set the mirroring configuration in AWS.

AWS Elasticsearch is a common provider of managed ELK clusters., but does the AWS Elasticsearch pricing really scale? It offers a halfway solution for building it yourself and SaaS. For this, you would expect to see lower costs than a full-blown SaaS solution, however, the story is more complex than that.

Whenever you build a service and expose a set of endpoints to provide API access to that service, you’ll likely need to track their availability and response times, aside from ensuring their functionality. But to actually know that “something is down” or just “not performing” you need to consistently monitor your services day in day out and that’s how Heartbeat from the Elastic Beat family helps you with Uptime Monitoring.

Millions of people already use Kibana for a wide range of purposes, but it was still a challenge for the average business user to quickly learn. Visualizations often require quite a bit of experimentation and several iterations to get the results “just right” and this Kibana Lens tutorial will get you started quickly.

With the new Streama capability announced today, you no longer have to choose what to monitor and what to drop to manage your logging costs. For years, our customers have enjoyed the benefits of a log analytics platform that enables them to autonomously manage and analyze data in their cloud applications. Our machine learning engine empowers users to improve their system stability and accelerate their release cycles.

Running an ELK stack provides unrivaled benefits for your organization, however, ELK issues will inevitably crop up. ELK is scalable, and largely agnostic of internal infrastructure, making it a great asset for SMEs and enterprises. However, successfully deploying and running an ELK stack is not without its difficulties. In order to keep your ELK stack running at optimum performance, you need to familiarize yourself with some of the most common ELK issues.

Ingesting various events and documents into Elasticsearch is great for detailed analysis but when it comes to the common need to analyze data from a higher level, we need to aggregate the individual event data for more interesting insights. This is where Elasticsearch Data Frames come in. Aggregation queries do a lot of this heavy lifting, but sometimes we need to prebake the aggregations for better performance and more options for analysis and machine learning.