Many of you may have seen our State of Data Innovation report that we released recently; what better way to bring data and innovation closer together than through Machine Learning (ML)? In fact, according to this report, Artificial Intelligence (AI)/ML was the second most important tool for fueling innovation. So, naturally we have paired this report with a new release of the Machine Learning Toolkit (MLTK)!
Some things just go better together. Like barbeque and blues, sunsets and beaches, cheese and fine wine — hey, even software and superheroes go better together! That’s why in this blog we are going to look at why IT Operations and Observability just go better together, through a superhero analogy. Enter the Dark Knight himself — Batman! He will represent observability. IT Operations will be represented by Lucius Fox.
For the 7th year in a row, IDC has ranked Splunk as #1 in ITOA*. We’re thrilled with this news, but let me start by saying that our success is due to the continued success of our customers, and we’re very grateful for the opportunity to be a part of it. Need a refresher on ITOA? We know we know, another day another acronym. ITOA is IT Operations Analytics. IDC derived this market from portions of their IT Operations management (ITOM) software market.
You might already be using Splunk to manage your Salesforce environment with the help of the Splunk App for Salesforce and the Splunk Add-on for Salesforce that allows a Splunk administrator to collect different types of data from Salesforce using REST APIs. This solution is great and the events give you an idea of how users interact with Salesforce. These events can range from Apex executions to page views.
It’s never been more important for government agencies to embrace innovation than over the past 18 months. Citizens are relying on our government more than ever to provide critical services during the global pandemic — from developing COVID-19 vaccines at warp speed to extending benefits to the record number of unemployed Americans. The pandemic not only made government services more critical, it also challenged agencies’ ability to deliver those services.
It is no surprise that monitoring workloads are top of mind for many organizations to ensure a successful customer experience. As our applications become more distributed and cloud-native, we find that monitoring can become more complex. A single user transaction fans out to interact with tens or hundreds of microservices, each one requesting data from backend data stores or otherwise interacting with each other and other parts of your infrastructure.
The Cloud Monitoring Console (CMC) lets Splunk Cloud Platform administrators view information about the status of a Splunk Cloud Platform deployment. For workload pricing, the CMC lets you monitor usage and stay within your subscription entitlement. From the CMC you can see both ingest and SVC usage information and can gain insight into how your Splunk Cloud Platform deployment is performing.
A Splunk Virtual Compute (SVC) unit is a powerful component of our workload pricing model. Historically, we priced purely on the amount of data sent into Splunk, leading some customers to limit data ingestion to avoid expense related to high volumes of data with low requirements on reporting. With Splunk workload pricing, you now have ultimate flexibility and control over your data and cost.
Nowadays, the efficiency of a company’s IT infrastructure is commonly measured in how often it can deploy new versions of the software. Faster, better deployments are one of the main goals of the DevOps mindset. Therefore, to not fall behind the competition, one needs to implement DevOps practices. But DevOps isn’t just about deploying fast and often. DevOps is a set of practices and tools that help deliver better-quality software faster. The “quality” is the key here.
The journey to becoming cloud-native comes with great benefits but also brings challenges. One of these challenges is the volume of operational data from cloud-native deployments — data comes from the cloud infrastructure, ephemeral application components, user activity, and more. The increased number of data sources does not only increase datapoint volume – it also requires that monitoring systems store and query against data with higher cardinality than ever before.
