Advanced Computing in the Age of AI | Tuesday, November 29, 2022

Machine Data Analytics for DevOps: Five Tips 

(Source: Shutterstock/Ton Snoei)

As more organizations adopt a Continuous Integration/Continuous Delivery approach, in which software is developed in shorter cycles and deployed into production at higher velocity, they need a new breed of tools that run at cloud scale and can be seamlessly integrated with a host of DevOps tools across the entire pipeline.

To help achieve this speed and scale, increasing numbers of cross-functional teams (development, operations, customer success/support, SecOps and line of business) are using analytics to monitor, manage and gain insights from user, application, or infrastructure logs. A new class of automated tools is enabling teams to understand error rates, failures and other information in massive amounts of log and machine data.

Machine data analytics is a relatively new field, and it’s important to know what to look for in a powerful, built-for-the-cloud solution. Here are the top five must-haves:

1: New Functionality without the Wait

With a cloud-native, machine data analytics SaaS, you have the simplicity of being up and running in minutes, as opposed to weeks or months with a traditional, enterprise packaged software. Cloud-native solutions are designed for velocity; new features can be added at a much faster rate, enabling faster time-to-value and better quality for the same price. New updates are rolled out to all users at the same time, so no one is left behind on an outdated version. The result is a machine data analytics platform that keeps pace with the shorter development cycles and agile methodologies you employ to drive your own software release velocity.

2: The Elasticity You Need Without Sticker Shock

In a rapid DevOps world of continuous technology and underlying infrastructure changes, problems can surface when you least expect it. Consider the moment when your production applications or infrastructure has a serious problem and it is “all hands on deck” to investigate and resolve. Suddenly, you have your entire team running simultaneous queries with debug mode activated, bringing in more data than usual.

Suku Krishnaraj of Sumo Logic

Suku Krishnaraj of Sumo Logic

From a DevOps perspective, this scenario is where the elasticity of a multi-tenancy platform outshines a single tenant solution. Since only a small percentage of customers have “incidents” at the exact same time, excess capacity is always available for you and your cross-functional team to use in your hour of crisis. This elasticity also comes in handy to address the expected or unexpected seasonal demands on your application environment. Lastly, with a cloud-based metering payment model, your elasticity does not come at an extra price. On the contrary, you pay for your “average” capacity, even though there may be times when you utilize more to handle those bursts that require five, 10 or 100 times more capacity.

3: Scalability Without Performance Hits

For DevOps engineers, machine generated log data is growing exponentially from their IT environments as their infrastructure and application stacks grow in complexity. In fact, by 2020, IT departments will have 50 times more data to index, search, store, analyze, alert and report on. To address this demand, cloud-native solutions have dedicated tiers for each log management function (ingest, index, search, etc.) that scale independently, to keep up with your data’s volume and velocity of change. And there’s no hassle with managing search nodes or heads in order to ensure search performance. Since not all cloud-based solutions are alike, be sure to understand how much data you can use before being locked out. Remember, it’s precisely the moment you have a problem when you’ll need to scale.

4: Reliability. Reliability. Reliability.

Modern cloud-driven strategies, such as aggregating computing resources in a multi-tenant, native cloud architecture, provides built-in availability for peak capacity without incurring payment penalties for bursting. This architectural approach results in always-on service continuity and availability of your data. Additionally, cloud SaaS ensures enterprise-class support for you and your team. Solving customer problems quickly enables SaaS vendors to deliver value across the entire customer base, which is very motivating for the vendor.

5: Never Overbuy or Overprovision

Rapid development cycles will also fluctuate your machine data analytics needs over time (e.g., more usage fluctuations, more cross-functional projects that will bring more data, etc.), thereby complicating planning. Native cloud solutions that include metered billing alleviate the need to think ahead on current and future needs — simply pay as you go for what you need. Additionally, since you will never overbuy or overprovision, your ROI improves over the long term.

As these five qualities show, the right machine data analytics philosophy – with a focus on purpose-built for the cloud, not merely running on the cloud – allows organizations to optimize the joint value of the "dev and ops orchestration" that defines DevOps.

Suku Krishnaraj is the VP of Marketing for Sumo Logic. He most recently headed the Cloud Business Unit at CenturyLink as VP/General Manager managing P&L & GTM of the Cloud and managed hosting businesses.

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