Looking Forward with Legacy Application Logging

4 MIN READ
MIN READ

When developers think of log files and log analysis, their minds typically transports into the world of contributing factors and incident remediation. However, analyzing log events doesn’t always need to be about a specific bug and its corresponding resolution. In fact, log analysis can be a very useful resource for organizations looking to develop a more high-level and large-scale plan for their application moving forward.

Any application that stands the test of time will reach a point in which it needs to be modernized, either partially or in full if it is to remain a viable solution for its end users. The trick is to decide how to revamp the application so that the modernization effort provides great value to both the organization and end-users. Log analysis and the use of a robust log analysis platform can have a large impact on a business’s plan for modernizing an application.

Why is Modernizing an Application Important?

There are a host of important reasons for modernizing an application. These reasons revolve around improving application quality and reliability and revamping potentially outdated development processes used within the team. In doing so, an organization can streamline portions of the application development process and provide themselves with the opportunity to improve and expand their business.

Decreased maintenance leads to increased innovation

Application modernization typically involves moving off of older, antiquated frameworks and technologies and onto newer ones that experience fewer failures and are easier to support. This move results in a reduction in the amount of time that development and operations teams need to spend in order to keep the system up and running. This streamlining, in turn, frees up resources that the business can then leverage to refine functionality, thereby empowering the team to provide greater value to their end-users.

Improved system security and compliance

It’s no secret that outdated code, frameworks, and technologies are more likely to be susceptible to both security vulnerabilities and compliance issues.

For organizations collecting personal and financial information about their customers, modernizing an application represents an opportunity to build with security and compliance in mind by baking it into their application. Rebuilding that application provides developers and operations teams with the opportunity to improve adherence to industry standards for compliance and to utilize libraries, frameworks, and tools that provide an inherently increased level of awareness of security needs than those developed years ago.

This shift is representative of a more complete solution than identifying and reacting to security and compliance shortfalls in a legacy system or attempting to bolt fixes onto an outdated codebase or infrastructure.

Improved development processes lead to an increased speed of delivery

Modernizing an application provides the opportunity for teams not only to upgrade their legacy applications but also to make upgrades to their development processes, undoubtedly leading to an increased speed of delivery. Modernization projects represent a natural entry point for the adoption of modern development practices, including continuous integration, continuous delivery, automated and continuous testing, etc.

These practices help organizations deliver changes to their applications in less time and with an increased level of quality and stability.

How Can Logging in Your Legacy Application Help with Modernization?

So, keeping the advantages of application modernization in mind, how can logging in a legacy system help drive the modernization process? A few uses for legacy application log data and the resulting analysis of this data come to mind.

Identifying system components subject to frequent errors and performance problems

We all know that log analysis is of great use in determining the contributing factors of specific issues occurring within an application. However, it can also be useful when attempting to analyze the bigger picture.

One aspect lending insight to the bigger picture lies in identifying which components of a system are experiencing high error rates or great levels of latency. In the event of a gradual migration to a newer system, such insights can provide development organizations with the information they need to choose which components should be given priority in the modernization effort. Focusing on those elements that are contributing to overall application quality issues such as frequent failures or excessive slowness will help the organization increase the reliability of the product in the fastest manner possible.

Identifying areas in need of an overhaul in user experience

Just because a legacy system component isn’t experiencing obvious shortfalls such as performance issues or frequent runtime errors doesn’t necessarily mean it shouldn’t be an area of focus in an application redesign.

For instance, consider the case of modernizing an application designed for streaming music. In this case, there could exist a feature that allows for building playlists to share with friends. If users frequently begin the process of creating a playlist but fail to follow through, it’s possible this feature could be due for an overhaul to improve intuitiveness and overall user experience.

Log analysis can be utilized to provide insights into user behavior, enabling development organizations to identify features with room for improvement in these areas and thereby helping organizations make decisions about which components of a legacy system require greater attention within the modernization effort.

Choosing the Right Logging Solution

There are several paths available to those teams modernizing legacy systems. There is the all-or-nothing approach of rebuilding an entire system at once, and there is the gradual modernization technique where the system is overhauled on a component-by-component basis.

With the latter, it will prove extremely valuable to choose a log analysis platform with the ability to monitor the remaining legacy components in conjunction with those that have been moved. By ensuring these logs are available in a centralized location, developers and incident response personnel will have all relevant information at their fingertips. This ability goes a long way in empowering efficient and effective incident response processes.

Mezmo, formerly known as LogDNA, is one platform with these capabilities. A cloud-based log management solution, Mezmo provides mechanisms for easy collection, aggregation, analysis, and visualization of system and application log events. Their platform serves to enable development organizations to derive actionable insights from their logs as quickly as possible. All in all, that capability helps reduce the time it takes to resolve application issues, enabling the tooling development and operations personnel to make the best use of their time and provide value to their end users.

This post is part of a larger series called Logging in the Age of DevOps: From Monolith to Microservices and Beyond. Download the full eBook here.

Table of Contents

    Share Article

    RSS Feed

    Next blog post
    You're viewing our latest blog post.
    Previous blog post
    You're viewing our oldest blog post.
    Mezmo’s AI-powered Site Reliability Engineering (SRE) agent for Root Cause Analysis (RCA)
    What is Active Telemetry
    Launching an agentic SRE for root cause analysis
    Paving the way for a new era: Mezmo's Active Telemetry
    The Answer to SRE Agent Failures: Context Engineering
    Empowering an MCP server with a telemetry pipeline
    The Debugging Bottleneck: A Manual Log-Sifting Expedition
    The Smartest Member of Your Developer Ecosystem: Introducing the Mezmo MCP Server
    Your New AI Assistant for a Smarter Workflow
    The Observability Problem Isn't Data Volume Anymore—It's Context
    Beyond the Pipeline: Data Isn't Oil, It's Power.
    The Platform Engineer's Playbook: Mastering OpenTelemetry & Compliance with Mezmo and Dynatrace
    From Alert to Answer in Seconds: Accelerating Incident Response in Dynatrace
    Taming Your Dynatrace Bill: How to Cut Observability Costs, Not Visibility
    Architecting for Value: A Playbook for Sustainable Observability
    How to Cut Observability Costs with Synthetic Monitoring and Responsive Pipelines
    Unlock Deeper Insights: Introducing GitLab Event Integration with Mezmo
    Introducing the New Mezmo Product Homepage
    The Inconvenient Truth About AI Ethics in Observability
    Observability's Moneyball Moment: How AI Is Changing the Game (Not Ending It)
    Do you Grok It?
    Top Five Reasons Telemetry Pipelines Should Be on Every Engineer’s Radar
    Is It a Cup or a Pot? Helping You Pinpoint the Problem—and Sleep Through the Night
    Smarter Telemetry Pipelines: The Key to Cutting Datadog Costs and Observability Chaos
    Why Datadog Falls Short for Log Management and What to Do Instead
    Telemetry for Modern Apps: Reducing MTTR with Smarter Signals
    Transforming Observability: Simpler, Smarter, and More Affordable Data Control
    Datadog: The Good, The Bad, The Costly
    Mezmo Recognized with 25 G2 Awards for Spring 2025
    Reducing Telemetry Toil with Rapid Pipelining
    Cut Costs, Not Insights:   A Practical Guide to Telemetry Data Optimization
    Webinar Recap: Telemetry Pipeline 101
    Petabyte Scale, Gigabyte Costs: Mezmo’s Evolution from ElasticSearch to Quickwit
    2024 Recap - Highlights of Mezmo’s product enhancements
    My Favorite Observability and DevOps Articles of 2024
    AWS re:Invent ‘24: Generative AI Observability, Platform Engineering, and 99.9995% Availability
    From Gartner IOCS 2024 Conference: AI, Observability Data, and Telemetry Pipelines
    Our team’s learnings from Kubecon: Use Exemplars, Configuring OTel, and OTTL cookbook
    How Mezmo Uses a Telemetry Pipeline to Handle Metrics, Part II
    Webinar Recap: 2024 DORA Report: Accelerate State of DevOps
    Kubecon ‘24 recap: Patent Trolls, OTel Lessons at Scale, and Principle Platform Abstractions
    Announcing Mezmo Flow: Build a Telemetry Pipeline in 15 minutes
    Key Takeaways from the 2024 DORA Report
    Webinar Recap | Telemetry Data Management: Tales from the Trenches
    What are SLOs/SLIs/SLAs?
    Webinar Recap | Next Gen Log Management: Maximize Log Value with Telemetry Pipelines
    Creating In-Stream Alerts for Telemetry Data
    Creating Re-Usable Components for Telemetry Pipelines
    Optimizing Data for Service Management Objective Monitoring
    More Value From Your Logs: Next Generation Log Management from Mezmo
    A Day in the Life of a Mezmo SRE
    Webinar Recap: Applying a Data Engineering Approach to Telemetry Data
    Dogfooding at Mezmo: How we used telemetry pipeline to reduce data volume
    Unlocking Business Insights with Telemetry Pipelines
    Why Your Telemetry (Observability) Pipelines Need to be Responsive
    How Data Profiling Can Reduce Burnout
    Data Optimization Technique: Route Data to Specialized Processing Chains
    Data Privacy Takeaways from Gartner Security & Risk Summit
    Mastering Telemetry Pipelines: Driving Compliance and Data Optimization
    A Recap of Gartner Security and Risk Summit: GenAI, Augmented Cybersecurity, Burnout
    Why Telemetry Pipelines Should Be A Part Of Your Compliance Strategy
    Pipeline Module: Event to Metric
    Telemetry Data Compliance Module
    OpenTelemetry: The Key To Unified Telemetry Data
    Data optimization technique: convert events to metrics
    What’s New With Mezmo: In-stream Alerting
    How Mezmo Used Telemetry Pipeline to Handle Metrics
    Webinar Recap: Mastering Telemetry Pipelines - A DevOps Lifecycle Approach to Data Management
    Open-source Telemetry Pipelines: An Overview
    SRECon Recap: Product Reliability, Burn Out, and more
    Webinar Recap: How to Manage Telemetry Data with Confidence
    Webinar Recap: Myths and Realities in Telemetry Data Handling
    Using Vector to Build a Telemetry Pipeline Solution
    Managing Telemetry Data Overflow in Kubernetes with Resource Quotas and Limits
    How To Optimize Telemetry Pipelines For Better Observability and Security
    Gartner IOCS Conference Recap: Monitoring and Observing Environments with Telemetry Pipelines
    AWS re:Invent 2023 highlights: Observability at Stripe, Capital One, and McDonald’s
    Webinar Recap: Best Practices for Observability Pipelines
    Introducing Responsive Pipelines from Mezmo
    My First KubeCon - Tales of the K8’s community, DE&I, sustainability, and OTel
    Modernize Telemetry Pipeline Management with Mezmo Pipeline as Code
    How To Profile and Optimize Telemetry Data: A Deep Dive
    Kubernetes Telemetry Data Optimization in Five Steps with Mezmo
    Introducing Mezmo Edge: A Secure Approach To Telemetry Data
    Understand Kubernetes Telemetry Data Immediately With Mezmo’s Welcome Pipeline
    Unearthing Gold: Deriving Metrics from Logs with Mezmo Telemetry Pipeline
    Webinar Recap: The Single Pane of Glass Myth
    Empower Observability Engineers: Enhance Engineering With Mezmo
    Webinar Recap: How to Get More Out of Your Log Data
    Unraveling the Log Data Explosion: New Market Research Shows Trends and Challenges
    Webinar Recap: Unlocking the Full Value of Telemetry Data
    Data-Driven Decision Making: Leveraging Metrics and Logs-to-Metrics Processors
    How To Configure The Mezmo Telemetry Pipeline
    Supercharge Elasticsearch Observability With Telemetry Pipelines
    Enhancing Grafana Observability With Telemetry Pipelines
    Optimizing Your Splunk Experience with Telemetry Pipelines
    Webinar Recap: Unlocking Business Performance with Telemetry Data
    Enhancing Datadog Observability with Telemetry Pipelines
    Transforming Your Data With Telemetry Pipelines
    6 Steps to Implementing a Telemetry Pipeline