What if I told you that it took me just under 10 minutes, 8 commands and 6 mouse clicks to create this bar chart informing me — big surprise — that I have too many open tabs in Chrome on my Mac? That might sound like a lot to some readers, but if you’re not a stranger to ELK you’ll know that installing the stack, even for testing and development purposes, usually involves a whole lot more than that. ELK can be installed on almost any system and in any environment.

Machine Learning (ML) algorithms are designed to automatically build mathematical models using sample data to make decisions. Rather than use specific instructions, they rely on patterns and inference instead. And the business applications abound. In recent years, companies such Google and Facebook have found ways to use ML to utilize the massive amounts of data they have for more profit.

he Graylog community is what makes the product so exciting. It is awesome to see our community members take the time to help everyone over on our community forums, twitter, reddit or on their own private channels. I wanted to take some time to highlight a blog post by Community member BlueTeamNinja (aka Big Abe) who, after tackling a Graylog deployment shared lessons learned from a non-Linux/non-Elk person.

Logging your Kubernetes clusters to LogDNA is already a breeze, and now the LogDNA Kubernetes agent Helm chart makes it even easier. Helm is the official package manager for Kubernetes. With Helm, deploying and managing Kubernetes applications is as simple as typing a single command. This makes deploying the LogDNA agent across your cluster absolutely effortless.

To keep you up-to-date with what’s going on in anomaly detection, we keep an ongoing list of the biggest glitches happening in the business world. Here is what made waves in June. June 25, 2019 When Dutch telco KPN suffered a major outage on the evening of Tuesday, June 25, the 112 emergency number was also knocked out across the country. “We have no reason to think it was (a hack) and we monitor our systems 24/7,” the company spokesperson told Reuters.

Companies invest in anomaly detection in order to proactively identify risks, such as revenue loss, customer churn and operational performance issues. Anomaly detection essentially enhances traditional BI and visualization tools, venturing beyond a summary view of your data. It constantly scans every metric, at a granular level, to find abnormalities. But in order for this technology to have an impact, you must be able to trust it.

Cloud is driving the way modern software is being built and deployed. At the forefront of this revolution is AWS, holding a whopping 33% of the cloud services market in Q1 2019. Considering AWS had a seven-year head start before its main competitors, Microsoft and Google, this dominance is not surprising. AWS offers, by far, the widest array of fully evolved cloud services, helping engineers to develop, deploy and run applications at cloud scale.

Managing agile software deployment for cloud infrastructure can be challenging. Deployments should be automated whenever possible to ensure consistent version management. Nevertheless, it can happen that identical software versions are not deployed to all servers. Such imperfect version management is a potential time-bomb. Distributed systems and microservices often rely on the deployment of the exact same software version installed on every cluster node.

The Grafana community comes up with some pretty cool stuff, and we’re hoping to spotlight some of it from time to time. Today, we’re starting with the BigQuery datasource plugin developed by the team at DoiT International. DoiT is a reseller of Google Cloud and AWS that helps companies either move from on premise to cloud or move from one cloud provider to another.

The ELK Stack is the world’s most popular open source log analytics and log management platform. Together, the four main components of the stack — Elasticsearch, Logstash, Kibana and Beats, provide users with a powerful tool for aggregating, storing and analyzing log data. In production environments, the ELK Stack requires an infrastructure flexible and powerful enough to power it.

Learn the best Splunk alternative, what to look for in alternative solutions, and other factors like logging features, speed, ease of use, deployment, scalability, and cost.What Does Splunk Do?Since its first release in 2007, Splunk quickly became one of the leading log management solutions. Its focus on enterprise grade log analysis and security incident and event management (SIEM) made it the de facto choice for organizations generating large volumes of log files and machine data.

Presto is an open source SQL query engine that runs analytics on large datasets queried from a range of sources, including Hadoop and Cassandra. Presto was originally developed by Facebook to run queries on its large Apache Hadoop data warehouse and is now used as an interactive analytics tool at companies like Airbnb, Uber, and Netflix.

As you navigate through Datadog, you may find that you want to dive into a graph to explore your timeseries data more deeply, or make quick changes to a graph without permanently altering it. To make it easier to explore the data in your graphs, we’re excited to introduce a newly revamped full-screen view for our timeseries graphs. You can now quickly and easily apply functions, navigate through time to find anomalies, and save and share your work.

Responsible for collecting various system and service metrics and forwarding them downstream to a backend storage system, the role metric collectors play in monitoring pipelines is crucial. Despite this fact, they often get left in the shadows cast by the beautiful frontend analysis tools like Kibana or Grafana. In the world of open source monitoring stacks, Metricbeat and Telegraf stand out as the most popular metric collectors. The truth is that they do much more than simply collect metrics.

Cloud environments are becoming increasingly complex, with applications and even infrastructures changing constantly. Despite their dynamic nature, these environments must be monitored constantly for teams to ensure the stability, security, and performance of workloads running in them. Tracking these infrastructure changes is one of the most important—and one of the most difficult—parts of maintaining a cloud environment.

One of the oldest (but often neglected) security vulnerabilities is SQL injection. One common scenario goes like this: An unsuspecting programmer writes an application that accepts input from the user which serves as a parameter to retrieve or store data from a database (e.g., a web login form). The programmer writes a dynamically populated SQL query inside the app, based on user input like username and password (see Image 1 for reference).

Monitoring an Azure environment can be a challenging task for even the most experienced and skilled team. Applications deployed on Azure are built on top of an architecture that is distributed and extremely dynamic. But all is not doom and gloom. Azure users have a variety of tools they can use to overcome the different challenges involved in monitoring their stack, helping them gain insight into the different components of their apps and troubleshoot issues when they occur.

Reporting software is a part of a Business Intelligence or BI suite and is used for analysis in early data processing. The purpose of self-service reporting software is to help deliver interactive information that can be put into action. Self-service reporting software allows the user to connect data sources, extract data and present it in various formats of visualization, including charts, tables, and spreadsheets.

AWS CloudWatch Logs gives you full visibility into your AWS infrastructure, from individual workloads to the services that bind them. Monitoring these logs helps ensure their smooth and continued operation, ongoing stability, and performance. Integrating CloudWatch Logs with LogDNA makes it easier to parse, search, and analyze AWS logs in order to detect anomalies and troubleshoot problems faster.

As Stephen Marsland once said, “if data had mass, the earth would be a black hole.” A vast part of the immense amount of structured and unstructured data that we call “Big Data” is nothing but machine-originated log data. Logs are generated for a lot of different purposes – from security to debugging and troubleshooting. They constitute a gold mine of useful information and actionable insights if properly stored, managed, and analyzed.

What are the essential things to monitor in your infrastructure? Sure, CPU utilization, memory usage, and IO throughput. However, once you notice a significant load somewhere in your infrastructure you want to know what is causing it, and that typically boils down to needing to find the process that’s using too much CPU or memory or that’s doing disk or network IO like there’s no tomorrow.

In the new report, “Analytics is making its security operations mark ahead of schedule,” analyst firm 451 Research details the accelerating transition happening in the security information and event management (SIEM) space. The report underscores how new cloud-native analytics solutions are displacing traditional SIEMs at the heart of the defense.

Pulling in data exposed via API is not one of the most common use cases for ELK Stack users but it is definitely one I’ve come across in the past. Developers wrapping their database services with REST API, for example, might be interested in analyzing this data for business intelligence purposes. Whatever the reason, the ELK Stack offers some easy ways to integrate with this API. One of these methods is the Logstash HTTP poller input plugin.

The ELK Stack, which traditionally consisted of three main components — Elasticsearch, Logstash and Kibana, has long departed from this composition and can now also be used in conjunction with a fourth element called “Beats” — a family of log shippers for different use cases. It is this departure that has led to the stack being renamed as the Elastic Stack.

As you may very well know, Kibana currently has almost 20 different visualization types to choose from. This gives you a wide array of options to slice and dice your logs and metrics, and yet there are some cases where you might want to go beyond what is provided in these different visualizations and develop your own kind of visualization.

On February 3, 2019, the Sumo Logic platform experienced its biggest ever spike in incoming data and analytics usage in the company’s history. On this day, close to everybody in the U.S., and many more people across the world, experienced a massive sports event: Super Bowl LIII. The spike was caused by viewers across the world tuning into the football game using online streaming video.

Kibana is the visualization layer of the ELK Stack — the world’s most popular log analysis platform which is comprised of Elasticsearch, Logstash, and Kibana. This tutorial will guide you through some of the basic steps for getting started with Kibana — installing Kibana, defining your first index pattern, and running searches. Examples are provided throughout, as well as tips and best practices.