5 steps to data observability

In the multifaceted realm of data management, aspiring to master proactive data observability often mirrors the meticulous craftsmanship that goes into brewing the quintessential batch of beer. It is a symphonic endeavor that calls for a harmonious blend of varied elements, a craft demanding skill, finesse, and an adept understanding of the underlying balances at play. In an episode of the Lights On Data Show that promises to tantalize both the intellectual and sensory palettes of the audience, George Firican, a data governance industry expert, and Ryan Yackel, the dynamic CMO of IBM Databand, embark on a journey to explore the remarkable parallel between the sophisticated world of data observability and the vibrant culture of craft beer.

Ryan shows us the full path towards proactive data observability, by distilling it into a series of understandable, actionable steps, each mirrored by a distinct type of beer that embodies its unique characteristics beautifully.

Join George and Ryan on this learning adventure as they unfurl the "5 Steps to Achieve Proactive Data Observability", a thematic symphony that melds profound insights with the jubilant camaraderie that accompanies a shared appreciation for good beer. From delving deep into the intricacies of pipeline execution with the refreshing notes of a lager to venturing into the dynamic realm of data access with the tangy resonance of a sour beer, a feast of knowledge and flavors await the audience.

So, prepare a favorite pint and settle in for a journey that not only promises transformative insights into data management but also a delightful excursion into the vibrant world of craft beers. Here’s to a lesson that is poised to be as refreshing and enlightening as a chilled glass, bottle, or can of your favored brew!

The Dawning Era of Data Observability

In the dynamic landscape of data management, a promising frontier has emerged, bearing the name of data observability. Data observability represents a pivotal shift in how companies approach the surveillance and maintenance of their data pipelines and datasets, mirroring the vigilant oversight already prevalent in the realms of software and DevOps engineering.

Stepping into the spotlight, data observability stands as a nascent category within the broader data space, embodying a proactive and unceasing approach to monitoring the vitality of data infrastructures. Much like how platforms such as Datadog, Instana, and New Relic have revolutionized the identification and resolution of issues within cloud architectures and microservices for software reliability engineers, data observability seeks to imbue the data sphere with a similar, always-on vigilance.

By translating the principles of software quality and reliability to the data environment, observability tools are extending their gaze to monitor data in transit, identifying disruptions and anomalies in real-time before they reach the consumption layer. This dynamic shift is marked by a departure from merely overseeing data warehouses to an encompassing scrutiny of data pipelines, utilizing platforms like Apache Airflow and Spark for real-time issue detection and resolution.

As one of the frontrunners in this innovative domain, Databand, now under the aegis of IBM, distinguishes itself with a proactive and continuous approach to data observability. Aiding data engineers and data platform teams in meeting their data SLAs more efficiently, Databand facilitates quicker detection of data discrepancies, empowering teams to resolve them expediently, thereby assuring the delivery of quality data to the consumers.

With a growing consortium of vendors venturing into this space the sector is poised for expansion and refinement. These efforts are geared towards fostering a data landscape where potential hiccups are not just promptly identified but are also rectified before reaching the critical consumption layer, thereby streamlining operations and enhancing the reliability of data processes.

As we navigate through the complexities of modern data management, the emergent field of data observability stands as a beacon of innovation, promising to usher in an era of heightened efficiency and reliability in meeting data delivery standards.

Join us as we delve deeper into this riveting topic, exploring the symbiosis between data observability and craft beer, and unveiling the steps to achieve proactive data observability in the ever-evolving data industry.

5 steps to proactive data observability (over beer)

Step 1: Pipeline Execution (Paired with Lager)

At this initial stage, it is imperative to maintain a keen eye on the multiple pipelines that could be in operation at any given time within an organization. There is a necessity to monitor these pipelines meticulously, connecting directly to the source data through orchestration or ingestion tools like Flow or IBM DataStage. The foremost goal here is to ascertain the status of the pipelines - are they operational, halted, or facing any other disruptions?

As organizations scale, the sheer number of pipelines can grow exponentially, sometimes reaching into the thousands. This proliferation can, unfortunately, give rise to blind spots where pipelines falter unnoticed until it's too late. A lapse in the pipeline's functioning could remain undetected until end consumers point it out, a scenario that no data team wants to find themselves in.

To counteract this, one can have a system akin to having "a thousand different cameras" vigilantly monitoring the diverse pipelines in real-time, ensuring they function smoothly and signaling promptly when disruptions occur. The key here is not just to detect whether a pipeline has ceased to function but to prioritize the pipelines based on their criticality to the business operations. This way, teams can focus on resolving issues that might have a significant impact, without being overwhelmed by alerts from less crucial pipelines.

And Databand can alleviate these common hiccups. How? Databand aids in filtering the noise in the alerting systems, enabling teams to allocate their attention and resources more effectively. It helps identify the critical pipelines that need to remain operational at all times, distinguishing them from those that might not require immediate attention. By doing so, it prevents data teams from being mired in a perpetual firefighting mode, allowing for a more focused and strategic approach to pipeline management.

Reflecting on the essence of Lager - a beer that undergoes a longer fermentation process, offering a clean, crisp taste – one can see it can easily be tied to the first step in the data observability journey. Just as a Lager needs time to ferment to perfection, setting up pipelines correctly is a meticulous process, laying the foundation for smooth operations down the line. With Lager dominating the global market with popular brands like Budweiser and Heineken, it serves as a fitting analogy for the prevalence and importance of effective pipeline execution in the data space.

As we toast to Lager and its embodiment of patience and precision, we recognize its parallel in the world of data observability - a reminder to meticulously set the stage right, ensuring a seamless flow in the data pipelines, much like the smooth, satisfying sip of a well-brewed Lager.

Step 2: Pipeline Latency (Paired with Wheat Beer)

As we move to the second step in the data observability journey, the focus sharpens on pipeline latency, a critical aspect that demands meticulous attention and proactive management. Analogous to this is the embracing of wheat beer, a beverage that combines lightness with subtle complexities, offering a delightful yet discerning drinking experience.

Just as wheat beers entice with their cloudy appearance and airy mouthfeel, borne from a higher proportion of wheat malt, pipeline latency reveals the depth of data flow within an organization. It brings forth the necessity to monitor and manage the fluctuations in data processing times, ensuring that the data 'train' not only moves but does so at an optimal pace. It's a process replete with intricate details, requiring a nuanced approach akin to appreciating the gentle symphony of flavors in a glass of wheat beer.

We need to emphasize the power of observability in managing pipeline latency. It's not just about identifying whether the data is moving, but understanding the speed and efficiency of its movement. The intricacies lie in setting up thresholds that flag any anomalies promptly, allowing timely intervention before any potential snowballing of issues. This proactive approach ensures the swift delivery of data without compromising on its accuracy and quality.

Databand rises to the occasion here, offering a solution that alerts teams to any discrepancies in real-time, not post-facto. It empowers organizations to gauge the performance of their pipelines accurately, modifying resources dynamically based on the throughput of data. This active monitoring helps in averting breaches in data SLAs, thereby avoiding setbacks that could hinder business decisions and operations.

The potential impact of neglecting pipeline latency is significant. A breach in data SLA could lead to delayed deliveries, which might further escalate to issues with data product consumption or analyses. So a proactive stance is recommended - engaging in detailed discussions on data SLAs, setting up alerts, and leveraging the dashboarding capabilities of Databand to keep track of possible breaches. The goal is to deliver data with precise accuracy within stipulated timelines, honoring the commitments made in the data SLAs.

Drawing parallels with wheat beer, we need to point out the rich, layered flavors that invite drinkers to sit back and appreciate the subtleties embedded within. Likewise, managing pipeline latency demands patience and a keen eye to grasp and address the underlying complexities that govern the data flow in an organization.

So, as we raise a toast to wheat beer, celebrating its vibrant and crisp character, let us also embrace the vital step of monitoring pipeline latency. With a strategic and proactive approach, organizations can ensure a harmonious data flow, reminiscent of the satisfying and refreshing taste of a well-crafted wheat beer.

Step 3: Data Sanity (Paired with Pale Ale)

In the thrilling journey of data observability, we now arrive at a pivotal juncture, the realm of data sanity, a stage exquisitely paralleled with the balanced nuances of a Pale Ale. Just as the Pale Ale boasts a harmonious blend of flavor, aroma, and bitterness, maintaining data sanity necessitates a balanced approach that takes into consideration the schema and its accuracy, ensuring the data retains its integrity throughout the pipeline. It is an endeavor to cultivate a sensorium that can discern the subtleties of data dynamics, mirroring the nuanced palate that appreciates the rich complexity of a Pale Ale.

The parallels between the balance found in a Pale Ale and the equilibrium required in data management are hard to ignore. A beer that presents a harmonious amalgamation of various characteristics, Pale Ale stands as a symbol of the delicate balance necessary in the sphere of data sanity. Because an equilibrium is needed. Why? Because we need to take into account the schema and its correctness, while tirelessly working to safeguard the precision of data flowing through the pipelines. This balanced approach is the cornerstone in achieving reliable insights from data analytics, paving the path for successful end results.

No one wants to encounter the unfortunate case where a slight deviation in schema that went unnoticed, would spiral into potential inaccuracies and inefficiencies. That’s where observability plays that pivotal role again, in circumventing such mishaps, offering the assurance that while we may not have control over external data influx, a vigilant system can detect discrepancies, effectively preventing the propagation of inaccurate data.

But what about the potential degradation of data quality over time? Can safeguards help to prevent data corruption and degradation? Yes, but we also need to acknowledge necessity for safeguards that go beyond merely monitoring the pipeline, urging organizations to immerse themselves into the metadata surrounding the actual data in motion. We need to still have our data quality checks, a process that extracts critical metadata and institutes alert mechanisms, essentially creating a protective shield against data degradation. And an integration with data quality and catalog solutions would also foster a cohesive approach in maintaining data sanity.

As we take a moment to appreciate the Pale Ale and its embodiment of harmony and equilibrium, we find a resonant echo in the world of data observability. A timely reminder to forge ahead with a balanced perspective, nurturing data environments that are as robust and harmonious as a well-crafted Pale Ale, offering a promising pathway to achieving data sanity in an ever-evolving data landscape.

Step 4: Data Trends (Paired with IPA)

We now find ourselves embarking on the vibrant path of data trends, an adventure wonderfully mirrored by the intricate layers and evolving palette of an IPA. Just as an IPA undergoes a transformative journey, blossoming with a variety of flavors and aromas over time, the sphere of data trends is constantly in a state of evolution, adapting to the changing tides and offering rich insights that steer the direction of business intelligence and analytics. It is a pursuit to develop a keen sense of understanding that can unravel the intricate patterns within data trends, much like the cultivated taste buds that savor the intricate layers of an IPA.

The resonance between the evolving complexity found in an IPA and the dynamic shifts in data trends is undeniable. An IPA, with its diverse spectrum of flavors and aromatic notes, serves as a metaphor for the rich and varied landscape of data trends. The necessity for adaptation and innovation is paramount. Why? Because, in the rapidly transforming world of data analytics, recognizing and adapting to trends is not just a strategy but a necessity to foster growth and innovation, akin to how brewing techniques have revolutionized the world of IPAs, catering to a diverse range of preferences.

As we immerse ourselves on the path to proactive data observability, we encounter the critical stage of trend observation, where meticulous attention to detail comes to play. Much like an IPA connoisseur who cherishes the evolving notes and complex flavors, businesses must cultivate a profound appreciation for the subtleties within data trends. This involves a continuous effort in monitoring and analyzing the performance trends of data pipelines, effectively identifying and rectifying recurring issues, and thereby, maintaining a robust analysis mechanism.

We cannot overlook the monumental role of trend analysis in the management of data pipelines. Just as the IPA has woven itself into the rich tapestry of brewing history through continuous innovation, organizations must embrace analytics as a vital component in their pipeline management strategy, an endeavor that goes beyond mere setup to encompass constant monitoring and adjustments, aligned with the organization's KPIs, thus paving the way for a prosperous future trajectory in data analytics and business intelligence.

It becomes evident that being attuned to the current shifts and potential future trends, even within our data pipelines, is a critical aspect, enabling businesses to remain agile, ready to navigate the fluctuating landscapes with foresight and preparedness.

Step 5: Data Access (Paired with Sour Ale)

Lastly, we need to venture into the complex terrain of data access, a theme that mirrors the daring essence of a sour ale. Just as a sour ale challenges the palate with its bold and often divisive flavors, navigating the intricacies of data access demands a dynamic approach, keen observance, and the readiness to uncover unexpected insights.

There is definitely a dual perspective on data access, paralleling the dichotomy of responses a sour ale elicits (do you love it or hate it?). On one hand, there's the traditional stance of safeguarding data through privacy and security measures, akin to the discerning brewer meticulously crafting a sour ale to possess the right blend of flavor and tartness. On the other hand, there’s an evolving narrative that emphasizes the significance of impact analysis and data lineage, inviting organizations to embrace a comprehensive view that encapsulates the entire journey of data from inception to consumption.

Luckily we also have the innovative role of data observability tools that can help foster an automated environment for data lineage and impact analysis. Much like the complex fermentation process of sour ales, this automated approach allows for a seamless visualization of data flow and interaction, enabling organizations to pinpoint discrepancies swiftly and comprehend the downstream impacts holistically.

But the tale does not end at mere observation; it extends into the realms of governance and data quality, highlighting the symbiotic relationships formed between observability tools and governance solutions. This union facilitates an encompassing view, ensuring a harmonized approach to data management where observability meets quality control, akin to the perfect balance struck in a well-crafted sour ale where tart meets sweet in a delightful embrace.

We need to accentuate the integral role of partnerships in fostering a comprehensive suite that addresses the myriad facets of data access management. Much like the brewing community continually seeks innovation and collaboration to perfect the art of sour ale brewing, the fields of data governance, data analytics, and data engineering leans on partnerships to achieve a cohesive and forward-thinking approach to data access and observability.


As we draw the curtain on this vibrant and invigorating journey, we cannot help but feel grateful for enriching our palates and our minds in equal measure.

Over the course of our conversation, we have traversed the intricate landscapes of data observability, uncovering the depths of knowledge that reside within this dynamic field. With Ryan leading the way, we have navigated through the essential steps vital to steering your data pipelines towards success, blending technical acumen with a passion for innovation. Each step, like a finely brewed beer, brimming with a unique complexity and a story to tell.

And oh, what a sensational journey it has been, coupling each pivotal step in achieving proactive data observability with the vibrant world of craft beers! From the refreshing clarity of Lagers, the comforting embrace of Wheat Beers, the bold strides represented by Pale Ales, to the striking notes of IPAs and the daring complexity of Sour Beers; each pairing has offered us a delightful analogy, making the exploration of data observability not just insightful, but utterly enjoyable.

As we swirled, sniffed, and sipped our way through the rich tapestries of beer histories and brewing secrets, we found ourselves immersed in a conversation that was as deep as it was delightful, a testimony to the harmonious union of craft and technology.

Remember, in the world of data observability, much like in brewing the perfect beer, it's about nurturing with care, innovating with passion, and savoring the fruitful outcomes of one's labor. Let us toast to the amalgamation of data wisdom and craft beer appreciation, a blend that promises to offer many more intoxicating conversations in the future.

Until next time, keep brewing ideas, nurturing insights, and savoring the finest brews life has to offer. Cheers to data, cheers to beer, and cheers to the wonderful intersections where knowledge meets pleasure!

Video version: https://www.youtube.com/watch?v=wfo-YqHh5hU

Podcast version: https://podcasters.spotify.com/pod/show/lightsondata/episodes/5-Steps-to-Achieve-Proactive-Data-Observability-Over-Beers-e2946it

More about Databand: https://www.ibm.com/products/databand

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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