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 data observability solution that identifies and correlates enterprise data across all layers of a modern data environment.
A data observability platform provides 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.
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.
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:
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.
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:
With a top-end data observability platform:
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.
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:
Niche APM tools are free, but they deal with only a small problem scope.
Some examples of niche APM tools include:
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.
Some examples of free APM tools:
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.
Some of the vendors in this category include:
Enterprise-grade monitoring platforms can offer you more power, flexibility, and insights at your application layer under a single unified view.
Many of the vendors in this category have emerged over the past decade:
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.
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. This is where you need a data observability platform.
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:
At the same time, Acceldata also extends your business abilities to:
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.
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.
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:
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