The days of monolithic applications and simple monitoring tools are gone. With the arrival of public and private cloud infrastructure and hyperconnectivity on Edge devices, organizations struggle to scale their applications, identify issues before it affects their customers, and to maintain their SLA s. Enter a pplication p erformance m onitoring(APM), a game-changer in the realm of IT operations.

In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: Bonus Myth: I’ve done everything I can. The rest of the application waste is just the cost of running Apache Spark. Unfortunately, many companies running cloud environments have come to think of application waste as a cost of doing business, as inevitable as rent and taxes.

GitOps is a framework that combines the principles of software development and continuous integration, continuous delivery (CI/CD) to automate and manage application configurations and infrastructure using Git.

Touching every life across the population—right down to the last mile—city councils provide digital access to essential services and information systems and ensure they are easily accessible to safeguard civic well-being, law and order, and quality of life for everyone.

Improving operational efficiency and reducing the time it takes to resolve incidents are big goals. New options to customize your incident feed view in BigPanda allow you to highlight the most relevant context upfront, making a big difference. Reducing data visibility issues and redundant data can give operators greater control. The BigPanda Incident 360 Console is where ITOps teams and NOC operators receive the first notification and ongoing updates for all incidents.

Understanding the state of your network and infrastructure is a critical responsibility for operations teams. Without their ever-watchful eye, network issues can cause problems ranging from annoying performance issues to downtime. To detect, prevent, and address these issues, operations teams have relied on a combination of monitoring and manual correlation, leveraging whatever tools were available.

Microservices architectures have revolutionized software development, enabling scalability and flexibility. However, they also introduce complexities in system monitoring and troubleshooting. Effective logging is crucial for maintaining visibility and diagnosing issues in these distributed environments. This comprehensive guide explores best practices for microservices logging, helping you navigate the challenges and implement robust logging strategies.

Elasticsearch is primarily a search engine optimized for fast, complex search queries, especially text searches, and is often used for log and event data analysis. MongoDB, on the other hand, is a general-purpose, document-oriented database that excels in storing and retrieving structured and semi-structured data. It is commonly used for mobile, social, and IoT applications. While Elasticsearch provides superior search capabilities, MongoDB offers more robust data processing and storage features.

Distributed tracing is becoming a critical component of any application's performance monitoring stack. However, setting it up in-house is an arduous task, and that's why many companies prefer outside tools. Jaeger and Zipkin are two popular open-source projects used for end-to-end distributed tracing. Let us explore their key differences in this article.