Dash 2024 happened, and we were there! In this article, Federico Toledo shares his vision for the future of observability after the conference. He also talks about his experience and key takeaways.

Several days have passed since DASH, and the excitement is still intact. Attending conferences of such magnitude truly moves me. They are an endless source of opportunities, ideas, connections, and learning.

The DASH 2024 conference took place in New York. It had over 3,500 attendees and featured very relevant announcements for our industry, which I share in this article.

It was an enriching experience full of learning for our team. I learned a lot about the product, trends, partners, and the ecosystem. I had the chance to meet with industry leaders, partners, and clients I was eager to meet in person.

This networking has been invaluable for Abstracta. It has allowed us to better understand the Datadog ecosystem and collaborate more effectively.

The relevance of Dash for the IT industry is undeniable. Datadog is one of the leading companies in observability, and its impact extends to the entire tech community.

In this article, I share what I found to be the most notable announcements, my participation, and the lessons learned.

Need help with observability? Check out our joint solutions with Datadog and contact us to discuss how we can help boost your business.

The Relevance of Observability and the Role of Datadog

Observability in software is crucial for making informed and strategic decisions. It allows development and operations teams to gain a comprehensive view of their systems’ behavior.

It provides deep insights into internal workings, facilitating the detection and diagnosis of potential issues at any stage of the software lifecycle, even in production, and resolving incidents before they affect end users.

With advanced observability tools like those offered by Datadog, with whom we allied in 2023, we can monitor and correlate metrics, logs, traces, and events in real-time. This enhances our systems’ observability, meaning our ability to observe and respond.

This capability for deep, real-time analysis is essential for adjusting strategies, improving operational efficiency, and achieving a high-quality user experience in an increasingly complex, dynamic, and competitive technological environment.

With nearly 20 years of experience working on the performance and quality aspects of various applications, I consider Datadog to be one of the best tools in this sector.

Why do I think this way? Because it incorporates the functionalities of an APM (Application Performance Management) and complements them with a wide range of features that are growing daily. Examples include security controls and user experience metrics. All of this makes it a powerful observability tool.

Datadog is the company that allocates the most resources to R&D in the observability tools niche. Additionally, it is one of the companies with the largest market share, giving them greater insight into their customers’ needs.

These two points mean that Datadog sets trends in the industry, which is why these events are so important. It’s where new trends are announced, based on the new initiatives the company is developing and releasing to the public.

Don’t miss this article! Data Observability: What It Is and Why It Matters

Key Announcements from Datadog

During the event, there were dedicated spaces for partners that I leveraged to learn and network, something that has been invaluable for our growth at Abstracta.

On top of that, Dash featured highly interesting announcements with a significant impact on software development, testing, and production system operations.

I would like to highlight some of them:

1.Observability in Systems Using Large Language Models

Datadog launched LLM Observability to provide deep visibility into generative AI applications. This tool helps diagnose errors, evaluate performance, and mitigate risks like prompt injections and PII (Personally Identifiable Information) leaks.

More systems are using these components, making it increasingly complex to understand what happens during debugging, i.e., identifying, analyzing, and correcting errors. Datadog is investing significantly in this area to anticipate this growth.

Observability will be key to reducing the risks that generative AI applications present, boosting good user experiences, and efficiently resolving issues.

2. Unification of Datadog Agent with OTel Collector

Datadog announced the integration of the Datadog Agent with an embedded OpenTelemetry (OTel) collector.

This new functionality allows Datadog users to leverage its observability solutions while accessing the full capabilities of the OTel collector. Being based on an observability standard, it ensures portability and provides more comprehensive integration with more services.

Thanks to this, it is now possible to easily manage embedded OTel collectors with Datadog Fleet Automation and accelerate onboarding with unified tagging.

3. Expansion of Security Capabilities 

Datadog extended security controls throughout the entire development process. This enables the detection and resolution of issues directly from its console, from development to production, greatly facilitating incident diagnosis and resolution.

Additionally, it’s interesting to see how a shift-left approach is being applied in security, providing more tools for earlier stages of the development cycle. Among other things, static code and dependency analysis are performed to detect vulnerabilities directly in the development pipeline.

Moreover, Datadog significantly expanded its capabilities with the introduction of Log Workspaces. This new functionality allows security, operations, and development teams to take greater control of their log records.

Log Workspaces facilitate the analysis and enrichment of log data from multiple sources, using SQL, natural language, and visualizations in the Datadog console.

4. Kubernetes Autoscaling

Datadog introduced a new Kubernetes autoscaling feature that optimizes performance and infrastructure costs.

This tool automatically adjusts resources based on usage, allowing a balance between cost efficiency and cluster performance.

Datadog’s Kubernetes autoscaling provides multidimensional scaling for applications without affecting stability. It facilitates automated management and complete visibility backed by Datadog’s telemetry.

To learn about all the announcements made, I recommend visiting this summary on Datadog’s Website.

Activities and Networking

As I mentioned in the previous section, in addition to the main sessions, I had the opportunity to participate in several activities that enriched our experience.

It was very interesting to hear real case stories, such as GitHub migrating its observability platform to Datadog and managing to do so easily thanks to using the OpenTelemetry standard.

I was also impressed by what Whoop shared about how they implemented observability with Datadog in their application that uses generative AI.

Here are some of the events I participated in:

• Datadog Partner Connect: A technical and commercial training space for partners, where we learned more about the product and networked.
  
• Datadog Partner Summit: An event dedicated to partners, with announcements, recognitions, and networking opportunities.
  
• Technical Challenge: A team competition to find issues in a simulated environment. Our team won the competition, which was a very rewarding experience. The competition was designed to showcase the potential of Datadog’s platform in incident resolution.

Technical Challenge: A High-Level Competition

The Technical Challenge was one of the most energizing activities at Dash 2024. The competition took place in a simulated environment, with remote access to the console of the system and a Datadog instance where everything was monitored with various challenges based on a storyline.

Challenges were progressively enabled: service outages, security attacks, poor performance, among others, and the urgency to resolve them without additional context.

Each time we resolved one, we earned points, and new challenges were unlocked. The final challenge activated a 10-minute countdown, adding pressure to resolve all issues as quickly as possible.

I was amazed by our team’s ability to accumulate knowledge, divide the work, and complement each other effectively, despite not knowing each other beforehand. Each member brought different perspectives and knowledge, which was key.

I really liked that everything was designed to showcase the potential of Datadog’s platform in incident resolution. Winning the competition was an extremely rewarding experience.

Learnings and Reflections

Taking part in Dash 2024 provided us with several collaboration opportunities with clients and partners, as well as a deeper understanding of the product. This will enable us to better advise our clients and enhance our service offerings.

Learning about partners’ proposals, the challenges they face, and the solutions they offer will help us continue to identify Abstracta’s value proposition to distinguish ourselves and complement other partners.

Datadog is expanding its coverage of the entire development process, and at Abstracta, we are very interested in delving into these aspects to build quality software.

Every conference, whether small or large like this one, offers a lot in terms of opportunities and learning. Dash 2024 was no exception, and we came away with many ideas to experiment with, continue learning, and find better ways to add value to our ecosystem.

Would you like to delve deeper into the meaning and impact of observability? I invite you to listen to this episode of my podcast, Quality Sense, with Lisa Crispin.

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