DevOps & IT Operations | Resource Center

Today’s modern deployment pipeline is arguably one of the most important aspects of an organization’s infrastructure. The ability to take source code and turn it into a production application that’s scalable, reliable and highly available has become an enormous undertaking due to the pervasiveness of modern application architectures, multi- or hybrid-cloud deployment strategies, container orchestration and the leftward movement of security into the pipeline.

Edge computing is likely the most interesting section of the broader world of IoT. If IoT is about connecting all the devices to the Internet, edge computing is about giving more processing power to devices at the edge. Edge computing views these edge devices as mini clouds or mini data centers. They each have their own mini servers, mini networking, mini storage, apps running on top of this infrastructure, and endpoint devices. Rather than sending data to the cloud for processing and receiving already-processed data from a central hub in the cloud, in edge computing all the processing happens on the edge device itself, or close to the edge device.

An ever-increasing number of organizations are working in the cloud. It depends on their business model what cloud delivery model they use. The three most common deployment models for cloud services are software-as-a-service (SaaS), platform-as-a-service (PaaS) and infrastructure-as-a-Service (IaaS).

At Sumo Logic, we manage petabytes of unstructured log data as part of our core log search and analytics offering. Multiple terabytes of data are indexed every day and stored persistently in AWS S3. When a query is executed against this data via UI, API, scheduled search or pre-installed apps, the indexed files are retrieved from S3 and cached in a custom read-through cache for these AWS S3 objects.

Amazon EC2 offers a flexible and convenient way to run virtual machines in the cloud. With dozens of EC2 instance types available, as well as multiple pricing options, it’s easy to use EC2 to configure the best cloud-based virtual machines for your needs and budget.

Implementing and operationalizing the best practices and capabilities of DevOps into an organization is a key predictor for increased customer satisfaction, organizational productivity and profitability. Doing so successfully can be a challenging endeavour. Implementing DevOps can be particularly difficult because it oftentimes requires technology changes, process changes and a drastic change in mindset. Overcoming all three of these obstacles in a way that knocks down traditional barriers across development and operations teams in each stage of the software delivery lifecycle raises the bar even further.

MySQL has been one of the leading open source databases for the last couple of decades, and it underpins potentially millions of applications, from tiny prototypes to internet-scale ecommerce solutions. The beauty of MySQL is that it can be tuned as the application grows. For example, you can add higher availability options like clustering without having to refactor the application.

Logs are valuable. Logs generated by a major backend resource that provides clients with access to crucial data are more than just valuable; knowing where they are and being able to manage and understand the information that they contain can mean the difference between smooth, secure operation and degraded performance or even catastrophic failure for your application.

As the shift to cloud, modern app architectures and technology stacks continue to accelerate, the demand for real-time analytics to monitor, troubleshoot, secure and speed new innovations to these environments is also accelerating. So, we're not surprised to see demand for continuous intelligence—what we define as: real-time analytics from a cloud-native platform, supporting multiple use cases—is also accelerating.

As service architectures have transitioned from the monolith to microservices, one of the tougher problems that organizations have had to solve is service discovery and load balancing. The advent of service mesh technologies seeks to solve these and other problems exacerbated by the number of hosts that has grown exponentially.

At its core, DevOps is a set of principles. There are certain types of tools and processes that reinforce those principles and put them into practice. But the tools and processes are just a means to an end. The end itself is DevOps principles.

You probably know that automation is an important component of DevOps. But how do you actually put automation into practice in order to advance the goals of DevOps? Let's answer that question by exploring what automation means in the context of DevOps, why automation is important to DevOps and which processes to prioritize to achieve DevOps automation.

DevOps is an approach to software development and delivery that emphasizes collaboration between different stakeholders. DevOps also places priority on automation and the integration of different tools in a single, well-oiled pipeline.