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.

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).

Many data science discussions focus on model development. But as any data scientist will tell you, this is only a small—and often relatively quick—part of the data science pipeline. An important, but often overlooked, component of model stewardship is monitoring models once they’ve been released to the wild. Here we’ll aim to convince any unbelievers that monitoring deployed models is as important as any other task in the data science workflow.

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.

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.

Logs Monitoring plays an important role in analyzing, troubleshooting and alerting on problems. Organizations have lots of data in logs that should be mined to get valuable insights on users, applications and systems behavior. Real time alerting from logs monitoring does help in identifying the problems early. It also provides security related insights when someone is trying to break into your organization.

Last week I was at Monitorama and it was an amazing time! Not only where the talks fantastic, but I also had the opportunity to join Liz Fong-Jones from Honeycomb.io for a podcast with The New Stack to dig a little deeper into some of the topics in monitoring and site reliability engineering.

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.