Introduction
In today's era of rapid technological advancement, hardware companies across aerospace, transportation, energy, and beyond are operating increasingly complex machines that generate vast amounts of high-cardinality data. However, the legacy tools used to ingest, explore, and review this data are often better suited for simpler IoT devices. Developing, testing, and operating cutting-edge machines like space rockets, satellites, or autonomous vehicles demands software that goes beyond what's needed for cell phones and smart thermostats.
To properly build and maintain these intricate systems, engineers have been left with no choice but to create their own bespoke software solutions. While these in-house tools may work for their specific current use case, they come at a steep cost. Key engineers are pulled away from their core responsibilities, draining resources and ballooning budgets. Moreover, as a company scales, these brittle tools often break under the strain, forcing the team to start the building and fixing process all over again. This is a major drain on valuable time and resources that shouldn't be wasted on reinventing the wheel for telemetry and data review software.
It's abundantly clear that for machines to continue improving and scaling in ways that are proficient, safe, and cost-effective, a new breed of software is needed. This paper will dive deep into what sets advanced observability software apart from legacy monitoring tools, explore recent developments in AI-powered observability, and examine how Sift's comprehensive end-to-end observability stack tackles the challenges faced by innovators across a wide range of industries.
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Observability 101: More Than Just Monitoring
To understand why comprehensive observability is a game-changer for companies building complex machines, it's crucial to first grasp the nuanced differences between advanced observability and legacy monitoring tools. Without this foundational knowledge, it's all too easy to assume that outdated monitoring solutions are sufficient for the task at hand. In reality, relying on these tools often creates more problems than it solves.
Observability can quite literally make or break a mission, and legacy systems are simply not up to the challenge.
At its core, observability refers to the software tools engineers use to gauge and understand the performance of their hardware systems. As the complexity of these machines grows in lockstep with advancing technology, effective management of the enormous quantities of data they produce becomes an absolute necessity. The right observability tools empower engineers to quickly surface potential issues and resolve them before they cascade into mission-critical failures. Lacking these powerful tools, building the hardware of the future becomes a Herculean task. Observability can quite literally make or break a mission, and legacy systems are simply not up to the challenge.
When you're pushing the boundaries of what's possible with complex machines, the uncomfortable truth is that a million things can and will go wrong – many of them impossible to predict in advance. This is where observability truly shines, providing the real-time alerts and contextual insights needed for engineers to proactively identify and solve problems before they derail a project. As the software component of these machines grows ever more sophisticated, observability becomes the guiding light that keeps engineers on track.
Beyond Basic Monitoring: The Power of Observability
It's important to note that while there are existing monitoring tools on the market, they fall short of true observability. Platforms like Grafana require engineers to anticipate potential issues and anomalies in advance, preconfiguring dashboards to track those specific scenarios.
For controlled environments like server rooms or data centers, this approach can work. But complex machines often operate in unpredictable, real-world conditions filled with unknown variables and harsh environmental factors. In these contexts, it's simply impossible to predict and monitor for every conceivable issue. Monitoring tools lack the capability to surface and alert on the unknown unknowns.
What engineers need are tools that can automatically detect anomalies and aid in troubleshooting failures without demanding tedious, manual data review. This is where observability shines, leveraging logs, metrics, and traces to provide a holistic understanding of system behavior. With observability, teams can adapt to evolving conditions and tackle novel issues as they arise. In complex environments, observability goes beyond basic monitoring to include rich metadata, granular user actions, system architecture, network configuration, and deep code-level insights.
Put simply, observability is a quantum leap beyond monitoring because it empowers engineers to handle unanticipated problems with ease. And when you're building the complex machines that will shape our future, the unknowns far outnumber the knowns – making monitoring tools seem woefully antiquated by comparison.
The Observability Challenge: Why Everyone Isn't Using It
Given the clear advantages of observability, you might expect every hardware company to have already adopted these powerful tools. But the reality is that building an effective observability stack is an incredibly difficult undertaking. Developing the machines themselves is already a monumental challenge – building the requisite observability infrastructure to support that work compounds the complexity immensely.
Developing the machines themselves is already a monumental challenge – building the requisite observability infrastructure to support that work compounds the complexity immensely.
Many companies get mired in the process of cobbling together observability solutions in-house. It requires dedicating sizable teams of engineers to creating and maintaining these tools, often for a single bespoke use case or machine. Moreover, these systems demand multiple layers of fault tolerance to provide any real value.
In the realm of application development and IoT devices, observability has already proven its worth – and the data speaks volumes about its potential impact on complex hardware innovation:
- 90% of IT professionals believe observability is important and strategic to their business, but only 26% said their observability practice was mature. 50% are currently implementing observability (New Relic).
- 91% of IT decision makers see observability as critical at every stage of the software lifecycle, citing the biggest benefits to planning and operations (New Relic).
- 92% of surveyed engineers believe observability tools enable more effective decision-making (Tanzu VMware).
- Advanced observability deployments can cut downtime costs by 90%, keeping costs down to $2.5M annually versus $23.8 million for observability beginners (Enterprise Strategy Group).
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Observability in the Real World: From Spacecraft to Satellites
To understand how observability applies to the cutting edge of machine innovation, let's examine a few concrete examples. While we'll focus on the space industry to illustrate the power of these tools, the same principles hold true across aviation, transportation, energy, manufacturing, and any other field pushing the boundaries of hardware engineering.
Consider the challenge of building and testing a space rocket. You're dealing with an immense volume of high-frequency, high-cardinality data streaming in from sensors all over the vehicle. This data provides a wealth of mission-critical information – from operational status and navigation to granular telemetry. Operators must be able to leverage this data to make split-second decisions and precisely command the vehicle. If your rocket is designed for reusability, you also need the ability to store and analyze this data for future iterations.
An effective observability stack gives engineers the power to rapidly troubleshoot anomalies without burning precious time, all while storing that data for posterity.
Observability also enables robust validation and verification processes, helping to harden your hardware against errors and ensure launch readiness.
The unique challenges of satellite constellations provide another illuminating example. Each individual satellite needs to maintain a reliable connection to ground stations while autonomously navigating its planned trajectory. They must be able to receive updates and commands from the ground while also sending a steady stream of telemetry and status information back to operators.
Further complicating matters, no two constellation launches are identical. The satellites must be built to handle the ever-shifting conditions of each deployment, which demands rigorous, repeated testing from early R&D through to final orbit. To streamline this process, it's critical that engineers have access to the same observability tools and data at every phase.
The Pitfalls of Building Observability In-House
When companies attempt to develop their own bespoke observability solutions, they typically run headlong into a recurring set of challenges:
- Data Centralization: Your hardware is generating an unrelenting firehose of data from a myriad of sources. Ingesting and aggregating that data is only the first hurdle. You also need to normalize thousands of distinct schemas from different sensors while ensuring everything is properly time-aligned. Your observability stack needs to be able to ingest all of this data at scale, handling high-frequency streams without incurring latency that could impede crucial decision making.
- Usability for All Stakeholders: Off-the-shelf tools are typically designed for software engineers comfortable writing complex SQL queries. But what about your technicians, hardware engineers, or program managers who need to collaborate using a single source of truth? Bespoke observability tools often leave these key stakeholders struggling to access and utilize critical data.
- Testing as You Fly: To truly understand how your hardware and software will perform under real-world conditions, engineers need access to the same observability toolkit throughout the development process as they will have on launch day. Failing to bridge this gap can result in costly blind spots and performance issues.
- Maintainability: Though it may seem like a tractable initiative at the outset, an in-house observability system will inevitably consume more and more resources over time – eroding your ROI. Most companies design these systems for specific use cases, but the fundamental uniqueness of hardware sensor data often causes tools to break down when applied to new situations or when the company tries to scale.
The Limitations of IT Observability Tools
There's a pervasive misconception that existing IT observability tools can fill the gaps left by legacy monitoring solutions. While these tools can offer backend visibility into raw telemetry data, they fail to provide the comprehensive insights needed to effectively develop and operate complex hardware.
The world of cloud computing is undergoing a rapid transformation, with soaring demand for resources that can handle sudden, unpredictable usage spikes. At the same time, the speed of cloud application development has reached a blistering pace, leaving legacy tools struggling to keep up.
To meet the challenges posed by real-world hardware data, engineers need to completely rethink every component of the traditional observability stack:
- Ingestion: Complex machines require the ability to ingest higher-cardinality data at higher sampling rates, even when operating at large scale.
- Storage: Querying high-sampling-rate data or retrieving information across multiple time horizons demands a storage solution optimized for speed and scale. Traditional application performance data stores simply can't keep up.
- Visualization: Empowering all stakeholders to collaborate meaningfully demands advanced visualization tools that don't require writing SQL queries or complex code.
- Alerting: Effective observability for hardware requires stateful alerts and multivariate conditions that surface meaningful, actionable information while reducing alert fatigue.
Sift: Comprehensive Observability for What's Next
Sift is pioneering the next generation of machine development with the first unified observability stack purpose-built for hardware data. Founded by former SpaceX engineers with deep experience building reusable rockets, Sift is tailored for the unique challenges of complex, sensor-rich systems.
Sift is pioneering the next generation of machine development with the first unified observability stack purpose-built for hardware data.
Sift's platform is the only comprehensive solution that mitigates risk, automates data review, and provides complete visibility into operational hardware. Sift goes beyond the limits of traditional monitoring tools, putting previously inaccessible insights and capabilities into the hands of engineering teams.
By leveraging automatic data review, contextual alerting, low-latency ingestion, and the ability to test as you fly, Sift empowers engineers with complete situational awareness across their entire vehicle fleet. When the inevitable anomalies arise, Sift's powerful tooling helps teams quickly identify and resolve issues before they lead to costly mission failures.
Sift and the Future of AI-Powered Observability
In the rapidly evolving landscape of hardware engineering, AI holds the key to unlocking unprecedented levels of observability. However, effectively harnessing this power requires a meticulous approach and a robust data infrastructure. Sift, with its unwavering commitment to simplifying complexity, stands at the forefront of this AI-driven revolution.
To lay the groundwork for effective AI observability models, the first crucial step is to centralize, normalize, and time-align data in a format that facilitates seamless model training. Sift's architecture, designed to enable AI-driven observability, achieves this by decoupling compute and storage while leveraging the open-source Apache Parquet format. This innovative approach ensures data interoperability and scalable model training, empowering users to maintain complete control over their data while benefiting from a foundation primed for AI.
Realizing the full potential of AI in observability demands an unbundled database architecture capable of handling diverse workload requirements. With Sift's scalable data foundation in place, AIOps techniques can drive transformative improvements in performance monitoring, data review, and anomaly detection. Sift's AI-powered features, such as automated anomaly detection, predictive maintenance, and assisted rule generation, not only identify anomalies but also empower users to create deterministic rules and probability-based dispositions.
At the heart of Sift's philosophy lies the belief that mission-critical telemetry data should be managed with simplicity and ease. By harnessing the power of natural language, Sift enables users to effortlessly query data and generate visualizations, leveraging the accumulated institutional knowledge. This intuitive approach to data interaction democratizes observability, making it accessible to stakeholders across the organization. By simplifying complex missions and offering a future of streamlined telemetry management, Sift empowers engineering teams to focus on what truly matters: pushing the boundaries of what's possible.
Observability as Ground Truth
When you're building complex, mission-critical hardware, observability is not a nice-to-have – it's an absolute necessity. Engineering teams need a single source of truth to effectively operate and scale these intricate systems. Sift's comprehensive observability platform provides that unified perspective, enabling engineers to quickly get to the bottom of issues in convoluted software/hardware environments.
Rather than wasting precious time cobbling together incomplete data from scattered tools and chasing down crucial insights from siloed team members, Sift empowers engineers with clear, actionable information at their fingertips.
Sift's workflow is designed to capture and centralize institutional knowledge, ensuring that vital information lives alongside the machine data from early R&D through to live production. With Sift, engineering teams can collaborate seamlessly, solve problems rapidly, and proactively identify and mitigate risks before they manifest as catastrophic failures.
- Streamlined Data Management and Accessibility: To truly understand the behavior of your hardware, you need the ability to thoroughly analyze all of the data it generates. This is especially critical when you're building novel systems or exploring uncharted engineering territory. Sift's observability platform gives engineers maximum data visibility without the noise and friction of poorly integrated tooling.
- Risk Mitigation: The specter of a preventable mission failure is what keeps hardware leaders up at night. The more humans you have in the loop relying on legacy tools, the more risk you're carrying. If you're counting on monitoring tools that require preconfigured alert scenarios, you're leaving your system vulnerable to the unknown. With features like automatic data review and smart alerting, Sift enables engineering teams to reduce risk, accelerate issue resolution, and make better decisions, faster.
- Comprehensive Fault Tolerance: Machines bound for space or other similarly demanding environments require a caliber of quality control far exceeding consumer hardware. Achieving mission success requires meticulous and repeated validation of every aspect of system behavior. Sift's end-to-end observability streamlines testing and verification, helping engineers consistently deliver better, more fault-tolerant hardware on compressed timelines.
- Intelligent Data Storage and Retention: The data storage requirements for a complex machine are worlds away from those of a smartphone app or smart thermostat. Attempting to shoehorn hardware data into off-the-shelf storage solutions leads to ballooning cloud costs, especially when long-term retention is a must. Sift's intelligent, scalable storage is designed to capture the full fidelity and depth of hardware data, empowering engineering teams with a rich repository of past learnings to fuel future innovation.
Simplicity Through Observability
Sift's unified observability platform is a force multiplier for hardware engineering teams. By providing a comprehensive, fully integrated stack built on open standards, Sift frees engineers from the burden and inefficiency of piecemeal tooling. With all of their hardware data unified in one intuitive platform, teams can identify and resolve issues faster, and leverage that rich dataset to continuously refine system performance.
By providing a comprehensive, fully integrated stack built on open standards, Sift frees engineers from the burden and inefficiency of piecemeal tooling.
Not only does Sift save precious time and resources when compared to the costly slog of building in-house observability, but the platform's thoughtful design and automated workflows help capture vital institutional knowledge and streamline collaboration. Engineers can spend more of their time focused on innovation, rather than treading water in a sea of noisy data and fractured communication.
In a world where hardware engineering is only getting harder, Sift's observability platform is a critical investment in your company's ability to deliver on its mission. With the right observability tools in place, you can safeguard your projects from the risks of human error, organizational brain drain, and the relentless pace of technological change.
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