Enterprise applications are like finely tuned cars. When all the components work the way they’re supposed to, the results are incredible -- they produce an elegant combination of speed and performance. But when any one of the many pieces fails, it throws everything out of whack and you may as well be driving a Honda minivan.
Today, enterprise applications are like Formula 1 racing cars. So much depends upon these applications performing effectively, so businesses can’t afford for them to break down. To ensure they are revving at optimal levels, data ops teams are taking an active approach to data management. This new approach requires predicting, preventing, and resolving data issues before they happen.
The only way to achieve that is with a multi-dimensional data observability solution that identifies and correlates enterprise data across all layers of a modern data environment.
Data observability platforms provide the tools needed to make data layers more observable. They help users gain more control over data pipelines, achieve SLAs, and make better data-driven decisions. Unlike APM tools, which mostly monitor only the application layer, data observability platforms can extend real-time monitoring capabilities all the way down to the data and infrastructure layers.
What is data observability?
Data observability is an approach and a solution for data operations that enables monitoring, detection, prediction, prevention, and resolution of problems across your infrastructure, data, and application layers in real-time.
Observability has its roots in control theory, proposed in the early ’60s as a way to guide the management of dynamic systems. The approach is based on storing and analyzing data in it’s various states so it can be observable, which ultimately gives administrators more control over the system.
However, it wasn’t until 2013 that engineers at Twitter began to apply observability principles to solve the problems of building high-performance, scalable enterprise applications.
The more observable an enterprise application is, the easier it is to determine the root cause of any problems that affect it. As issues are identified and fixed, the application becomes more reliable and efficient.
Application performance monitoring (APM) tools first helped enterprise applications become more observable, but they mainly focused on application layer capabilities.
Today, more than ever before, enterprise applications handle more volumes of data from a wide range of sources, all of which is continuously changing. In addition to APM capabilities, data observability can help make your data and infrastructure layers more observable.
What can application performance monitoring (APM) tools do?
APM tools are one-size-fits-all solutions that monitor the application layer within an enterprise infrastructure. They keep track of the health of applications via output logs and traces, and alert data teams about problems, bottlenecks, and downtime issues. They have two distinguishing features:
- APM tools first adopted observability principles by making the output of the application layer more observable.
- They can identify which API service request failed, and they can highlight where computing resources are getting locked up.
But beyond these capabilities, it’s important to note that APMs are limited to only the application layer. This means that APM tools don’t have the capabilities needed to monitor the data and infrastructure layers.
More specifically, APM tools can’t validate the quality of data pipelines. Because APMs are often limited to trace sampling, they can’t analyze complete datasets, avoid data skews, and correlate root causes, so data teams will struggle to identify and fix root cause problems.
Why you should invest in a data observability platform (even if you have APM tools)
Unlike APM tools, which only monitor the application layer, data observability platforms extend monitoring capabilities all the way down to the data and infrastructure layers. Data observability improves control over data pipelines, creates better SLAs, and provides insights to data teams that can be used to make better data-driven business decisions. Data observability solutions provide clear advantages over APM tools in these ways:
- Better data layer observability gives DataOps teams more control over data pipelines.
- Improved infrastructure layer observability gives ITOps teams more control over infrastructure resources.
With a top-end data observability platform:
- ITOps teams can monitor key infrastructure layer metrics such as memory availability, CPU storage consumption, and cluster-node status at a granular level that APMs cannot offer, so they can troubleshoot and resolve data congestion and outages faster than any other type of solution.
- DataOps teams can ensure high-quality data standards by automatically inspecting data transfers for accuracy, completeness, and consistency. These quality checks result in healthier data pipelines.
- Data engineers can automatically collect thousands of pipeline events, correlate them, identify anomalies or spikes, and use these findings to predict, measure, prevent, troubleshoot, and fix problems.
- Business leaders can work with BI analysts to create accurate capacity estimates as well as more informed SLAs that meet the needs of business objectives.
Overall, data observability helps data teams prevent, identify, and fix root causes before they occur which is important because mission-critical enterprise applications can’t afford outages or downtime.
How to evaluate APM and data observability solutions - The six key parameters
A DataOps team should select a solution that meets the scope, scale, budget, usability, reliability, and automation needs of their business. To save time, we’ve categorized a broad spectrum of APM and data observability solutions into five categories based on these six parameters:
#1 Niche APMs
Scope: Limited at the application layer
Budget: Free or cheap
Niche APM tools are free, but they deal with only a small problem scope. Examples:
- Stagemonitor is an APM tool for Java server applications
- Scouter is an open-source standalone client application performance monitoring (APM) tool
#2 Freebie APMs
Scope: Limited at the application layer
Free versions of popular full suite APM tools such as AppDynamics and SolarWinds offer a limited entry set of functionality, delivered with the hope that customers will upgrade later.
- AppOptics is an APM tool for testing and troubleshooting applications before moving them into production.
- AppDynamics Lite offers code-level visibility into your application. It also provides operational dashboards that track your application performance in real time and claims to help you detect usage anomalies.
#3 Open-Source APMs
Scope: Application layer
Usability: Low, due to complexity
Reliability: Low, due to complexity
Open-source APMs offer a wide range of functionality but cannot help you much in terms of customization, deployment, and maintenance. In some cases, data teams lean on developer communities for crowdsourced support.
- Pinpoint can help you trace transactions across all your system components to give you an overview of how they are interconnected.
- Icinga gives you the power to watch any host and any application. It can help you draw conclusions and gain more confidence from your data. It also collects and sends data both from and to many of your existing DevOps tools, so you can create a tailored monitoring solution that perfectly fits your needs.
- SigNoz is a full-stack, open-source APM and observability tool that can serve all your application monitoring needs. Because it is based on OpenTelemetry, you don’t get locked into any one product.
- SkyWalking is an application performance monitoring tool for distributed systems that is designed for microservices and cloud-native and container-based (Docker, Kubernetes, Mesos) architectures.
#4 Enterprise-Grade APMs
Scope: Mostly at the application layer and may have some functionality at the data layer
Reliability: Medium, due to complexity
Enterprise-grade monitoring platforms can offer you more power, flexibility, and insights at your application layer under a single unified view.
- AppDynamics claims to offer visibility into every aspect of code and every transaction. It offers end-user monitoring, infrastructure visibility, and business performance monitoring capabilities.
- Datadog claims to seamlessly aggregate metrics and events across the full DevOps stack. It collects, searches, and analyzes traces and also claims to support production environment problem analysis.
- Dynatrace monitors microservices running inside containers. The platform supports cloud-native and distributed architectures.
- Splunk boasts advanced machine learning capabilities that can help you run forecasting, predictive analytics, outlier detection, and event clustering. The platform also supports cloud-native and distributed architectures.
- New Relic offers some observability capabilities. It provides a real-time view of operational data in one place.
If you run a mission-critical enterprise application, you need to go beyond observing the application layer. You’ll also need to observe your data and infrastructure layers. To do this, you need a data observability platform.
#5 Data Observability Platforms
Scope: App, data, and infra layers and most value for mission-critical enterprise apps
Scalability: Very high
If you run a mission-critical cloud-native or hybrid enterprise application that uses Spark, Kafka, or Kubernetes, you cannot afford any outages or downtime. You need to be able to automatically predict and prevent anomalies or usage spikes.
AI automation is only as good as the data you collect. If garbage data goes in, you only get garbage analysis out. So, you need a top-end data observability platform like Acceldata to gain more control over your data pipelines and ensure that they are healthy.
To achieve both, you need a full suite data observability platform such as Acceldata to help you analyze complete datasets, avoid data skews, drill down to the necessary information to identify root cause problems, and improve the health of your data pipelines.
Data observability platforms such as Acceldata extend your data capabilities to:
- Give you more control over your data pipelines.
- Enhance the quality and health of your datasets.
- Analyze complete data, avoid skews, and drill down to the necessary information.
At the same time, Acceldata also extends your business abilities to:
- Leverage AI automation to build models that help you cut through the noise.
- Identify root cause problems, gain real-time insights, and make data-driven decisions.
- Reduce costs by optimizing resource usage and avoiding manual coding/configuration changes.
The pros and cons of using data observability platforms in addition to APM tools
Top-end data observability platforms aren’t cheap or easy to implement, but they more than make up for these trade-offs when it comes to meeting the complexity, scalability, reliability, and automation needs that mission-critical enterprise applications demand.
Pros of using data observability
- Scope: Makes your infrastructure, data, and application layers more observable. Helps you optimize resources, maintain effective data pipelines, and make better data-driven business decisions. Can also help you observe all of the services, APIs, and SDKs that your application works with.
- Scalability: Can serve distributed enterprise applications using microservices, even at the scale of 200 billion daily impressions.
- Complexity: Can serve enterprise applications running on cloud-native as well as hybrid infrastructures. Can give you insights across your infrastructure, data, and application layers.
- Reliability: Can improve the quality and reliability of your data pipelines. Also allows you to analyze complete datasets without any data skews, so you can identify and fix root cause problems.
- Usability: Similar to enterprise APMs, top-end data observability platforms like Acceldata offer top-notch customer and onboarding support to help your team get the best out of your data pipelines.
- AI automations: Supports AI automation that filters out terabytes of noise and brings actionable insights to your attention. By preventing problems even before they occur, instead of reactive firefighting as and when they appear. So that your team can spend more time optimizing and scaling the application.
Other considerations of using data observability
Data observability adds another solution into your tech stack
No one wants to add another application or layer to the tech stack and face new integration, communication and migration problems. Because this can mean more work and coordination.
However, a top-end data observability solution can help you tie up these loose ends quickly. It can offer robust APIs that can integrate with all your existing applications.
Data observability costs can be compared to the cost of a full-time employee
Depending on your business needs, data observability solutions can cost thousands of dollars per year, and compared to the cost of enterprise-grade APMs, data observability solutions may appear to cost more.
However, this price isn’t high when you consider what a multi-dimensional data observability solution can offer. For instance, data observability can reduce your annual licensing costs by up to $ 5 million and help you scale data infrastructure by 10X.
Data observability needs an implementation champion within your organization:
Depending on the state of your existing infrastructure and data pipelines, it may take anywhere between a few days to a few weeks before a data observability platform can help you scale, optimize resources, and cut costs.
However, in the context of optimizing your data and application layers, a few weeks isn’t very long. Especially when you consider that a multi-dimensional data observability solution can help you identify bottlenecks and create effective data pipelines across your entire data landscape, and can ultimately save millions of dollars.
But you still need a champion within your organization, who can take ownership of implementing data observability across the DevOps, ITOps and DataOps teams.
Data observability offers enterprises a real competitive advantage
For mission-critical enterprise applications, data observability already gives you a real competitive advantage. This advantage is set to further increase and become a key differentiator of organizational success as:
- More enterprise applications move to more complex architectures in the cloud
- More management/leadership teams begin to make more data-driven business decisions