Build the Data Lakehouse of your dreams

Picture a serene resort nestled beside a glistening lake, where the ambiance exudes tranquility and the possibilities for relaxation and adventure are limitless. Now, envision your organization embarking on an exhilarating journey towards embracing the transformative potential of a Data Lakehouse – a digital haven that mirrors the perfect resort, a harmonious blend of functionality, scalability, and boundless opportunities for data-driven innovation.

In the world of resorts, the promise of a perfect retreat lies in the seamless fusion of architectural splendor and captivating amenities. From opulent accommodations to captivating leisure facilities, every aspect is meticulously designed to offer an unparalleled experience, catering to the diverse needs and desires of its patrons. In a parallel realm, the concept of a Data Lakehouse embodies a vision of unparalleled data management. Much like the luxurious resort, the Data Lakehouse stands as an architectural masterpiece, seamlessly integrating the vast expanse of data streams from diverse sources into a unified, scalable environment. It offers a harmonious blend of storage, processing, and analytics capabilities, setting the stage for organizations to unlock the full potential of their data assets.

As a steward of your organization's data strategy, envision the possibilities that unfold when embracing the promise of the perfect resort and Data Lakehouse. Embrace the compelling vision of a digital sanctuary, where the convergence of architectural elegance and data management excellence converge to propel your organization towards a new era of insight, agility, and competitive advantage.

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It all sounds dreamy, doesn’t it? Well… Let’s dream some more, shall we?

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?As the idyllic days at the resort transpired, peculiar occurrences began to taint the once pristine setting. The tranquil lake, which was once the epitome of serenity, abruptly emanated a pungent odor reminiscent of a sewer. The foul stench wafted through the air, disrupting the harmonious ambiance that had captivated guests. It turns out that one of the streams fueling the lake with water is used to dump wastewater by a hotel somewhere upstream and when they do that, it reaches the beautiful lake, spoiling the scene.

How does that relate to the Data Lakehouse? The principle of building logic on top of unstructured data means you can’t check the data quality directly in the data lake, and some of that data is only kept in the data lake because the originating data source no longer needs to store it. What people don’t seem to realize, is that in a Data Lakehouse implementation you need to control the source system as well. You need to make sure you understand the processes that generate this data and establish data contracts with the owners so that whatever they deliver is consistent.

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In another bizarre episode, lifeless fish surfaced atop the once crystal-clear waters, their presence an unsettling omen amidst the resort's picturesque backdrop. The unexplained demise of these aquatic inhabitants instilled a sense of unease among the resort's visitors, casting a shadow over the previously unmarred paradise. It turns out that the lake has some CO2 pockets at the bottom and, from time to time, a burst happens suffocating the fish in the vicinity. I guess that by this time you already know what the dead fish are… they represent data quality issues. The sad part about this one is that even though those fish die of natural causes, who would want to keep fishing in a lake where fish are floating belly up, every now and then. Data quality issues, even if they are isolated, they erode the overall trust in the system.

In another day, tensions mounted as conflicting desires clashed within the resort's confines. A discordant scene unfolded as some patrons sought to indulge in the tranquil art of fishing, only to be thwarted by the fervent whirring of speedboats piloted by exuberant young people who were there for the thrills. The clash of interests sowed discord, disrupting the once-harmonious pursuits of the resort's diverse clientele. In the world of the Data Lakehouse, this means you need to control the users and accommodate the use case scenarios to the proper consumption interface. You can’t use the same dataset for a high level overview as well as for a drill down scenario where users need to go to transaction level. Also, you can’t train a ML model with real time incoming data. There are also naming conventions that need to disambiguate between similar business metrics but with a certain twist to them. Operational Margin is not using the same source data as the Financial Margin because one of them serves for adjusting strategy on the go and the other one serves for measuring financial performance.

Amidst the resort's array of entertainment, a curious shift towards late-night fun and games began to overshadow the wholesome activities the resort had in mind. Despite a variety of leisure options, guests were drawn to drinking and gambling, steering them away from the resort's intended pursuits. Back on the Data Lakehouse, users don’t really understand what’s the big fuss and all this waiting for this dreamy new solution. All they do is export data to Excel and try to fit it in the source sheet of their existing excel reports they all know and love. It actually is a little harder for them to do that because it takes ages to adapt to their familiar format. Also, this new solution doesn’t fix their previous system issue… the 1 million rows limit in Excel. That actually means that the business users were not involved in the design phase, and no one bothered to include the new system in their daily business processes. It’s not clear to them how this new platform will change the way they get work done and what mindset revolution they need to go through in order to fully capitalize on it.

The symphony of dissonance now reverberated through the heart of the once-tranquil resort, casting doubt upon the future of this once-promising haven. What could have been a chance to step into a new era where the company becomes truly data driven, employing digital twins, real time analytics and AI, became another missed opportunity because this was treated as an IT project when it is in fact a business transformation endeavor.

PLEASE get proper data architects! And PLEASE listen to them! Don’t go for the cheaper ones because you stand to lose millions!