Splunk and Amazon Web Services (AWS) are celebrating 10 years of strategic collaboration — an incredible milestone which demonstrates our commitment to teamwork, co-innovation and exceptional, data-driven outcomes for our joint customers.
Moving to Splunk Cloud Platform has a lot of benefits, including flexibility, agility, and scalability. However, we understand that migrating to cloud is not a trivial task and can also bring up security concerns especially when it comes to having your data traverse the internet.
Every millisecond, humans generate significant volumes of data, from various IoT devices such as our wearable devices to daily activities such as internet surfing and tracking our workouts. Data continues to accumulate. Statista estimates that by 2025, the amount of data will have increased to 180 zettabytes. That's far too much information.
A typical bit of feedback I have had during my time at Splunk is that the Splunk Machine Learning Toolkit (MLTK) looks nice and all, but how are we supposed to get started using it? Choosing the right technique, let alone the right algorithm can be a daunting task for those who are unfamiliar with machine learning (ML). We’ve been thinking long and hard about how we can help offer more prescriptive introductions into using ML at Splunk and I’m pleased to present our set of MLTK deep dives.
Synthetic monitoring can be one of the most powerful tools in your DevOps team’s toolkit, especially for the SRE, yet is one that is often overlooked by people building out a reliability mindset. Synthetic monitoring permits you to simulate any transaction or interaction users can have in your website or app, from places around the world, as often as you’d like.
The question isn't whether an incident will happen: it's when it will happen. Systems will crash. Software will fail. Vendors will suffer an outage of their own. It's your job to be prepared for these problems, and incident severity levels are one of the tools you need. Incidents have varying impacts on your business and customers. Incident severity levels are how you classify their impact and manage your response.
