Application modernization

BigPanda supports application modernization

BigPanda helps enterprise IT Operations teams successfully handle the challenges thrown their way by application modernization projects while preserving DevOps velocity. This includes quickly identifying changes that cause incidents and outages (so they can be rolled back), creating a real-time topology model that overcomes the challenges of traditional CMDBs, and giving DevOps teams full-stack visibility for faster incident and outage resolution.

Why enterprises are embracing application modernization

Enterprises gain new capabilities by adopting new ways of delivering applications:

  • Significant improvements in engineering velocity
  • The ability to continuously enhance products and respond rapidly to demands from the business and users in order to remain competitive

The challenges of application modernization

More changes = more incidents and outages

Problem

Identifying changes that cause problems is difficult and slow

More than 80% of IT incidents are caused by changes that have unintended consequences. That presents a serious challenge for enterprises modernizing their applications and embracing CI/CD.   With thousands of changes occurring every week – across infrastructure, code, configuration, applications and systems – there’s a lot of things that can and do go wrong.  Finding changes that may have caused an outage is even more challenging because changes can be driven by various teams through various tools for CI/CD, orchestration, change management, and change auditing.

When an incident occurs, IT teams must manually sift through thousands of changes in several different tools to identify the offending change. This takes an enormous amount of time, during which your critical services are down or performing poorly – impacting your customers and business.

Negative impact on your business:

Performance and availability problems: Manually sifting through thousands of changes to identify root cause wastes precious hours and minutes when you can least afford it: when your critical services are performing poorly, and your customers are unhappy.

Increased operating costs: Gathering multiple teams members for hours-long bridge calls to identify the one change out of thousands that caused an outage is expensive, inefficient and disruptive.

Decreased business velocity: When your applications are affected by frequent incidents and outages, your DevOps teams are constantly pulled away from app development and sucked into troubleshooting sessions. This decreases the resources available for strategic innovation and slows down business velocity.

BigPanda’s solution

Identify root cause changes in real-time

BigPanda connects to all change tools – both traditional and modern sources of changes – gathers their data in real-time and aggregates it in a single place.

Then, BigPanda uses Machine Learning (ML) – the only way to sift through thousands of changes in real-time – correlating and analyzing changes against monitoring alerts and incidents, and surface root cause changes. BigPanda also lets users – IT Ops, NOC, DevOps and SRE teams – mark a specific change as the ‘matched’ root cause change and learns and improves over time.

In the process, BigPanda eliminates the need for expensive bridge calls with tens of people that last several hours/days because teams can now rapidly resolve incidents and outages and reduce MTTR. This makes BigPanda well suited for enterprises that are modernizing their applications.

Positive impact on your business:

Reduced operating costs: BigPanda eliminates the need for long and expensive bridge calls. Operations teams have the root cause changes at their fingertips, in real-time.

Improved performance and availability: By drastically reducing the amount of time required to investigate incidents and outages, application performance and availability is higher, and MTTR is lowered.

Decreased business velocity: Because the frequency and duration of outages is reduced, DevOps teams can focus on their strategic projects and initiatives, accelerating business velocity.

Your CMDB can’t keep up

Problem

CMDBs were not designed for modern application architectures

One of the biggest challenges facing enterprises migrating their traditional three-tier applications to the cloud is that cloud-native, microservice-based architectures are too complex and dynamic to be represented in a traditional CMDB. Traditional CMDBs were simply not designed for the complexity and velocity of modern, ephemeral environments. When the CMDB can no longer serve as a single source of truth, this negatively affects a wide range of dependent capabilities, such as event correlation, root cause analysis and incident automation.

Negative impact on your business:

Performance and availability problems: When your traditional CMDB can’t handle modern apps based on cloud-native architectures, this makes incident management much more cumbersome and time consuming. That, in turn, elongates MTTR, degrades your SLAs, and impacts your customers’ experience.

Increased operating costs: When outages are long lived because your CMDB can’t keep up, this increases downtime-related costs.

BigPanda’s solution

BigPanda’s Real-time Topology Mesh

BigPanda connects to all your topology tools, including traditional CMDBs and modern cloud-based sources of topology data, gathers their data in real-time, and aggregates it in a single place. Then, BigPanda synthesizes all your topology data to create an always up-to-date, full-stack, real-time topology model called the Real-time Topology Mesh. Finally, BigPanda correlates your monitoring alerts against the real-time topology mesh to achieve an accurate, high-quality correlation.

Positive impact on your business:

Reduced operating costs: BigPanda’s Real-time Topology Mesh ensures high-quality, accurate correlation. This results in fewer and shorter incidents and outages, decreasing downtime-related costs.

Improved performance and availability: BigPanda’s Real-Time Topology Mesh handles modern cloud-based apps, and legacy apps alike with ease, enterprises can more easily detect, investigate and resolve incidents and outages. This also lowers MTTR.

Hard to get full-stack visibility

Problem

DevOps teams lack full-stack visibility

DevOps teams responsible for cloud-native, microservices-based applications that are being continually enhanced with a CI/CD pipeline rely on application-specific APM, log, server and infrastructure monitoring tools to manage their own applications. However, these tools are siloed, which means DevOps teams lack the full-stack visibility needed to understand how changes they make impact other parts of the environment or detect and investigate incidents involving other systems or infrastructure.

Negative impact on your business:

Increased operating costs: Because of the lack of full-stack visibility, expensive DevOps resources must waste valuable time investigating and resolving incidents and outages.

Performance and availability problems: Because of the lack of full-stack visibility, investigating incidents takes a long time for DevOps teams. MTTR is high.

BigPanda’s solution

Negative impact on your business:

BigPanda ingests changes generated by DevOps teams’ CI/CD pipelines, along with monitoring data from various monitoring tools, and topology data from various topology sources.

Then, by using ML to correlate these three datasets together, BigPanda can detect application-affecting incidents as they form and surface their probable root cause – both in real-time.

DevOps teams can gain the full-stack visibility they need to keep their applications, and their CI/CD pipelines, performant.

Positive impact on your business:

Reduced operating costs and improved performance and availability: Because highly-paid DevOps teams gain full-stack visibility into their applications and their CI/CD pipelines, they can resolve incidents and outages faster. This improves application performance and availability and increases these teams’ productivity.

So, what are you waiting for?