Data Observability Checklist

We have seen that data observability has the capability to become an important component in your data projects. It enables organizations to gain real-time insights into the behavior of their systems and infrastructure, allowing for faster troubleshooting, improved performance, and better decision-making.

However, implementing data observability can also present a number of challenges and pitfalls. After having conducted several data observability projects across different industry sectors, we wanted to share with you some points of attention to make your journey toward data observability successful.

In this chapter, we will explore the perks and drawbacks of data observability, providing guidance on how to implement it effectively while avoiding common pitfalls. We will see how data observability projects may fail, and what the strategies to overcome this are.

By the end of this chapter, you will have a deeper understanding of the challenges of...

Challenges of implementing data observability

In this section, we will describe the common pitfalls and challenges of the implementation of data observability and how we can overcome them. The concerns we will cover are the following:

• Costs
• Overhead
• Security
• Complexity increase
• Legacy system
• Information overload

Let’s start with the bottom line: the costs.

Costs

Foremost among the concerns surrounding data observability are its associated costs, which can pose a significant financial burden on data projects. These expenses typically encompass the following:

• The acquisition or development costs of a data observability solution, including the investment in research and development and the requisite team training
• Expenses related to the storage and computation of data observations, which can also introduce overhead, as we will elaborate on later in this chapter
• The marginal cost incurred when integrating observability into...

Checklist to implement data observability

In this section, we will delve into a comprehensive list of considerations to keep in mind when you embark on the journey of implementing a data observability solution. These questions will not only guide you through your initial project into data observability but also prove invaluable as you progress to more advanced implementations. By carefully addressing these considerations, you will be able to establish a robust foundation for your data observability initiative, one that not only aligns with your organization’s objectives but also harnesses its full potential for maximum benefit.

The questions we need to answer are the following:

• Which pipeline should I select to start with the implementation?
• How many applications should I include in the scope?
• What criteria are important to select the observability tool?
• How do we define the set of metrics we want to track?
• How will alerts and notifications be configured...

Summary

This chapter delved into the intricate process of implementing and scaling data observability within organizations, emphasizing the common pitfalls faced during the integration of observability.

We have seen the main challenges, which are the control of costs, the overhead with other jobs, the security concerns, the increase in complexity of the architecture, the trade-off to be handled with legacy systems, and finally, the information overload that teams can experience. We have also seen that all these challenges can be overcome and the risks mitigated.

Then, we listed the questions a data team must answer during observability implementation. The list covered the criteria for selecting the appropriate project and observability tool, considering aspects such as security, compliance, cost, integration, data retention, intelligence, and customization. The discussion on costs explored various strategies, including open source solutions, in-house development, vendor solutions...