Earlier this year Python 2 entered End of Life — and Splunk has already released versions of Splunk Cloud and Splunk Enterprise that provide a Python 3 runtime. As the developer of an app that is published to Splunkbase, if your app contains Python code, you need to update it to work with Python 3 and Splunk Enterprise 8.0 by July 1, 2020 as the Splunk Enterprise and Splunk Cloud releases after that date will no longer support the Python 2 runtime.

Platforms like Heroku give you the freedom to focus on building great applications rather than getting lost setting up and maintaining infrastructure. One of the many great features of working with it is the Heroku logs that enable monitoring your stack error troubleshooting. It helps speed up the process when things go wrong. In this Heroku tutorial, we’ll uncover best practices for making the most of Heroku logs.

Logs are an invaluable source of information, as they provide insights into the severity and possible root causes of problems in your system. But it can be hard to get the right level of visibility from your logs while keeping costs to a minimum. Systems that process large volumes of logs consume more resources and therefore make up a higher percentage of your overall monitoring budget. Further, log throughput can be highly variable, creating unexpected resource usage and financial costs.

We’ve heard from our customers that you need visibility into metrics and logs from Google Cloud, other clouds, and on-prem in one place. Google Cloud has partnered with Blue Medora to bring you a single solution to save time and money in managing your logs in a single place. Google Cloud’s operations management suite gives you the same scalable core platform that powers all internal and Google Cloud observability.

What I love about our free and open Elastic SIEM is how easy it is to add new data sources. I’ve learned how to do this firsthand, and thought it’d be helpful to share my experience getting started. Last October, I joined Elastic Security when Elastic and Endgame combined forces. Working with our awesome security community, I’ve had the opportunity to add new data sources for our users to complement our growing catalog of integrations.

AWS Lambda is the leading serverless computing solution and is one of AWS’ most successful products, to date. Its popularity is in large part because of the way it makes development easier and faster. Lambda completely abstracts away the maintenance of underlying infrastructure including compute, storage, memory, and networking. Developers simply upload their code in the form of Lambda functions and the service fully handles the execution of these functions.

Monitoring all DNS requests in your network, including those that were blocked by (e.g., by a firewall) is a great way to increase visibility, enforce compliance and detect threats. A common problem with collecting DNS logs is that DNS server logs are notoriously hard to parse. Also, parsing only the logs of your DNS servers leaves a blind spot when it comes to usage of, or the attempt to use, an external DNS server like Google's 8.8.8.8.

If you're reading this, you're probably wondering how to get data from various Microsoft Azure services into Splunk. With the growing list of Azure services and various data access methods, it can be a little cloudy (pun intended) on what data is available and how to get all that data into Splunk. In this blog post, I'm going go over how Microsoft makes Azure data available, how to access the data, and out-of-the-box Splunk Add-Ons that can consume this data. So let's dive right in.

Monitoring began by using software agents to capture data from infrastructure, operating systems, and applications. These agents would collect metrics and events from these systems to understand the health of the underlying system and the applications. This is what infrastructure monitoring is today.

In sync with the upcoming release of Splunk’s Machine Learning Toolkit 5.2, we have launched a new release of the Deep Learning Toolkit for Splunk (DLTK) along with a brand new “golden” container image. This includes a few new and exciting algorithm examples which I will cover in part 2 of this blog post series.