Data Lakes: Ending the Confusion

Data Lakes: Ending the Confusion

Data Lakes: Ending the Confusion

Data lakes evoke technicalites. And tech decision makers don’t know whether to go for the lake, warehouse, or lakehouse. But maybe the question is more personal.

There’s a line in the movie Tron Legacy that perfectly encapsulates data. The line goes: –

“The Grid. A digital frontier. I tried to picture clusters of information as they moved through the computer. What did they look like? Ships? Motorcycles? Were the circuits like freeways? I kept dreaming of a world I thought I’d never see.” – Kevin Flynn, Tron Legacy, Disney, 2010.

Data is this fascinating thing, moving clusters of information that describe behavior, inform choices, move commerce, and shape economies. It is, perhaps, the essence of human knowledge.

And now, data creates the next revolution: AI. But there is so much about data and its storage that is unknown. Engineers can’t agree on definitions and methods.

It is all a bit vague, and the definitions devolve into tech-heavy or too non-technical terms.

With tech, the explanations should be simple and not reductive. Let’s attempt to do that.

What businesses need to understand about data lakes

As a business, you’re probably asking yourself if you need a data lake. So before you or your managers make a decision. You must realize that the data lake is actually a concept- not a storage type.

It is a large bucket of storage that can be scaled as per the needs of the organization. But does every business need it?

Wish there was a clear answer for that, but there isn’t. Ask your devs, your CTOs, and everyone else, you’re not going to come up with a straight answer.

You won’t even get a straight answer on whether you need this technology.

It’s the same answer echoing: it depends on the use cases.

And the main use case for businesses to adopt a data lake is if their databases start crossing a certain threshold.

But what is that threshold? Well, there is a solid answer for that. But it’s a big one to wrap your head around, especially for non-technical people.

The answer is: it depends on the use cases.

Yes, that is not a joke.

Data lakes are more complicated than anyone might think. And see if ChatGPT can give you a straight answer on it. It’ll confuse you even more.

So, do businesses need a data lake?

This is the million-dollar question. Let’s try to answer this question.

There are some things that are pretty common across the board: –

  1. You need data lakes if you’re storing large amounts of data.
  2. But it depends on the structure of your data.

These are two contradictions that plague decision-making regarding buying data. But we can assume that there will come a time when you know the data lake will be more efficient than the warehouse.

But there’s more to this: there’s also the lake-house. It is a middle ground between the warehouse and the lake, offering a more flexible option between the two.

So you think to yourself: Ah, that means well, just go for the lake-house. Well, tough luck, because no one can really make heads or tails of that either. Amazon S3 is both a lake and a lake-house depending on how YOU use it.

Yet, this is a non-answer. There needs to be a method. One that helps CISOs, CIOs, and every other decision-maker make sense of spend. If it’s needed, and how to identify that need.

Let’s give it a shot.

How can business leaders identify if they need a data lake, a warehouse, or a lake-house?

There are a few assumptions we must make here:

  1. Under no circumstances do we want to create a data swamp.
  2. It should not increase budget and overhead costs without adjusting the customer lifetime value.
  3. It should be manageable.

Then, here are some facts that will help you understand the difference between the three.

What data storage architecture matches your needs?

  1. Data Warehouses
    1. Used for structured data, quick insights, and pre-processed data. Perfect for teams not on a budget. But it cannot store unstructured or raw data.
  2. Data Lakes
    1. Used for unstructured, semi-structured, and structured data. It is low-cost and flexible.
  3. Data Lake-houses
    1. A combination of the two. It is lower cost than warehouses, more structured and analytical than lakes. Flexible.

Yes, lakehouses seem like the perfect fit. But not everyone needs them. Sometimes, a warehouse would do. Or, if the data you have is fairly exceeding the bounds, then you should opt for the data lake.

The pro of the data lake and lake house is that they are highly scalable. Warehouses, because of their structured approach, can be tricky.

The Diagnosis

So now that we have enough information. We can map a diagnosis structure onto this:-

The first question leaders should ask themselves is:

  • What does my team think about this?

If they think you need it, what is the reasoning, and how many of them believe it is necessary? There is a good chance you will find that the teams are divided.

The second question then is:

  • What are the clear advantages of having either one of these?

Then: –

  • What are the constraints that each of them offers, and can the hybrid lake house be the fix for it?

Followed by: –

  • Does our budget allow for such changes, and is the trade-off worth the migration and other hassles that come with the decision?

And finally,

  • Will this mitigate any future or present problems?

You may notice that the diagnosis is based purely on a strategic, human-first approach. Because that’s how decisions are made. In the research for the blog, we found that 86% of tech decision makers feel analysis paralysis.

That’s a lot. Although malicious actors and tight budgets have made this easier. Analysis paralysis is the reason why buying committees take time to make decisions.

The ripples of the decisions are too much. Add information from AI and other sources, and leaders’ confusion only grows. But the reason behind it is much simpler and driven by human psychology: the inability to learn from ground-level staff.

The Pigeonhole Problem

Leaders are good at doing their job- managing people and solving problems. This causes decision-fatigue to build up. There’s a reason why you feel hung out to dry, because your nervous system is actually tired from all the mental hard work you do.

It is not a joke. And neither is it disconnected from this conversation. So what happens?

Your vision narrows down, and the sight of what is happening on the ground becomes blurry. You have to manage stakeholders and user expectations- now this?

So the easiest part is to understand your own engineer’s perspective. And then use that to make a decision, using your honed decision-making instinct.

The pigeonhole problem is that you narrow down to a result and forget the process. And add to that your buying committee, which becomes an echo chamber.

Remember, decisions are people-first.

So, what do you do?

The tech community is facing a huge problem. Everyone thinks it’s run by logic. But it is run by experimentation, mistakes, and a whole lot of frustration.

Why does this go unacknowledged?

Think of data architecture, won’t it be personal to your context? Yes, you’re googling or LLMing whether to buy a data lake.

But what do your engineers, devs, and product teams think? And is your business ready for this decision?

Of course, you can hop on the trend and just do it. The lake house is perfect for it. That’s the answer right there. But that does not mean it will eliminate your problems. There is a chance it could add to it.

And don’t you forget the other layer- these are all concepts. They are not actually a thing. When you buy the S3 or Snowflake, you get the option to choose between structured, unstructured, semi-structured, and everything else in between.

The reality is whether you can afford it. But that’s a difficult question to answer. It is a tough decision to make because if you miss a trend or an opportunity, you might fall behind. Isn’t that why you decided to invest in AI?

Data lakes aren’t the problem. It’s not understanding what a business needs.

Your business needs are unique. The reason GPTs and Reddit return the answer, it depends, is because data is molecularly contextual.

And that’s actually the magic of it. Your customer segments, even though the same across your competitors, will show different behavior. Your data point, pulled from the same data pool, will vary across silos.

Customer success, the AI/ML division, marketing, sales, and every other data will point towards a radically different idea. It will confuse you. But the way out of this confusion is understanding where the clarity lies.

Data lakes bring an end to siloed visions, but they can become a swamp- so what do you do?

You create architecture that doesn’t overwhelm your teams. The answer is never in managing complexity but in making complexity easier to understand and translate. In tech, you can’t outrun entropy, but you can make it work for you.

Onto-Project-Suncatcher-Could-Space-Be-The-Answer-to-AIs-Latent-Potential

Onto Project Suncatcher: Could Space Be The Answer to AI’s Latent Potential?

Onto Project Suncatcher: Could Space Be The Answer to AI’s Latent Potential?

Google, in alliance with the company Planet, hopes to launch its first couple of solar-powered satellite prototypes for Project Suncatcher by 2027.

The market is being driven distraught- is the AI bubble finally going to burst? And amidst this frenzy, maybe, just maybe, there’s an answer to all the speculations regarding AI’s true potential.

Since its escalating adoption, the world assumed that we might never decode the modern tech’s maximum potential in our lifetime. Well, we were wrong to presume that.

As the AI race unfolds right in front of us, we are left dumbstruck by Google’s moonshot plan. And while it sounds like the normal next step, it truly is a fascinating step towards opening new avenues for AI-led innovation. And gauge- is sky truly the limit for artificial intelligence?

Google’s Project Suncatcher is understood to be a moonshot research project. The crux? It’s taking AI to space. And honestly, the whole antic sounds exciting on the surface.

The success of this project could push companies to scale ML in space. And this would be powered by the most substantial energy source, our Sun. So, basically, the research project plans to assess whether a constellation of solar power-backed satellites, equipped with Google’s TPUs, can be connected by free-space optical links.

Google calls it the “future space-based, highly scalable future AI infrastructure design.”

And it’s already taking baby steps toward transforming this project into a reality. The first is creating modular designs for the small, interconnected satellites.

If the tech powerhouse figures this out, there’s a bright road ahead. A future where AI relies less on terrestrial resources. And more on the never-ending energy backup- solar power. These models would continuously churn out electricity. And facilitate eight times the productivity of current data centers.

However, there’s more to the story.

The resource-hungry data centers built on space? Could this be what the world needs right now?

Yes. If this is the missing puzzle piece to binding AI and sustainability.

These space-based AI data centers could harness the Sun’s clean energy around the clock. It could dispel the havoc created by the earth-bound data centers.

They’re thirsty for freshwater (but not for salt water, because wouldn’t that make things easier?). They’re driving up the utility bills. And a magnanimous demand for electricity in the surrounding areas.

On Earth, AI and sustainability can’t work in tandem. At least, not yet.

In response, Google’s Senior Director for Paradigms of Intelligence asserts, “In the future, space may be the best place to scale AI compute.”

But for now, Project Suncatcher remains an ambitious research project.

Meta Plans to invest $600bn in the US in Capex

Meta Plans to invest $600bn in the US in Capex

Meta Plans to invest $600bn in the US in Capex

Meta has recently announced that they would be investing $600bn in AI data centers by 2028. This is a bit misleading for many reasons.

Reuters recently covered that Meta will be investing around $600bn in the US via AI infrastructure in the next three years. This includes jobs and data centers, as well as other auxiliary infrastructure needed to grow their division.

This has been misconstrued by a lot of media giants. Some think that Meta is raising or investing $600bn in direct cash. No, this is capex.

Meta does not have $600bn. What they are instead promising is, through their initiatives, to create a system that injects that value of that amount into the US economy.

The key here is that through relatively small investments into its own AI projects, Meta will create economic incentives worth $600bn. But the question is: can they make do on their promise?

The AI bubble.

Yes, let’s talk about the AI bubble. The circular economy has been haunting the world for quite some time. The top AI companies have been under fire for allegedly moving money within their own ranks.

And the fear of the AI bubble rises among investors and stakeholders- including employees. Then there’s the doomsayers, scientists among them, warning of the emergence of superintelligence.

They make it sound like science fiction, but a growing school of thought believes not. This change in technology doesn’t just herald a change in our economic systems but also the way of life of many communities.

It is a disruption on a scale not known to our economies.

Based on these fears, apprehensions, and misinformation, do organizations know what they are doing with this tech?

Let’s hope that they do because the alternative is a scary one.

Things Marketers can learn from Zohran Mamdani

Things Marketers can learn from Zohran Mamdani

Things Marketers can learn from Zohran Mamdani

From “please stop sending us money” to the cost-of-living crisis- Zohran’s campaign struck an emotional chord like no other.

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Zohran Mamdani’s exhilarating mayoral campaign had almost everyone stop in their tracks. And there are some indispensable lessons marketing leaders can learn and integrate to connect better with their audience- to spur a real moment of change and have fun doing that.

Mamdani, the youngest mayor in a century, the first-ever Muslim mayor, and the first South Asian mayor, built visibility and coherence. He was out there talking directly to voters, often using native languages, which proved a point: he’s in it for everyone, not just representing one segment.

Comments from voters substantiate this- with many highlighting his authenticity, how they felt represented by him, and the way he appeared to be telling the truth- something past mayors and candidates completely missed.

Zohran built real excitement among the people of New York and also received global recognition and support, as the tone of his campaign touched hearts everywhere.

His campaign visibility showed the impact of meeting your audience where they are. Shooting videos in public spaces without censoring every word he spoke, his campaign demonstrated care and consideration built on genuine communication. In a world where everything is inordinately scrutinized and scripted, his campaign embodied sincerity. He made his voters feel an emotion many fail to evoke- trust.

Zohran Mamdani made this campaign about New Yorkers and what he could do to make their lives easier- the other candidates made it about themselves. And that’s the difference. People respond to values and alignment. Anything that feels performative rings hollow and fails to build the resonance campaigns of such stature need to be successful.

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The biggest takeaway is that Zohran built a movement grounded in real effort to connect with voters

– and he had a smile on his face the whole time. People could sense his intention- and that was enough to land him a victory and one of the highest-status jobs in U.S. politics.

It always boils down to knowing the audience, being where they are, and speaking to them in a language they prefer while also being yourself both as a brand and individual. Because often the simplest of things make the biggest impact.

Apple to Revamp Siri with Google's 1.3 Trillion Parameter AI Model

Apple to Revamp Siri with Google’s 1.3 Trillion Parameter AI Model

Apple to Revamp Siri with Google’s 1.3 Trillion Parameter AI Model

Apple is finalizing a deal to leverage Google’s 1.2-trillion-parameter Gemini model to power the much-awaited Siri upgrade.

The thing about Apple’s famously self-reliant ecosystem? Sometimes it needs to call in the neighbors for help. And by “help,” I mean a cool $1 billion per year to rent Google’s brain.

For context, that’s roughly eight times more complex than Apple’s current 150-billion-parameter models. Eight times. Let that sink. And remember, every time Siri confidently misunderstood your simple request.

There’s an irony hidden here.

This is the same company that built its brand on doing things “the Apple way.” Privacy and control. The same company that’s been promising us a smarter Siri since, well, since Siri became a punchline. And now? They’re running Google’s models behind the scenes while marketing it all as Apple technology.

Honestly, Apple did test alternatives- OpenAI’s ChatGPT and Anthropic’s Claude before choosing Google. But here’s where it gets interesting: what reportedly tipped the scales wasn’t superior performance, but price. Nothing says “innovation leader” quite like shopping for the budget AI option.

Apple insists this is temporary.

The company claims it’s developing its own 1-trillion-parameter model that could be ready as soon as 2026. Sound familiar? It’s the same playbook they used with maps, weather data, and chips. Lean on someone else until you catch up.

Except this time, there’s no guarantee users will embrace the new Siri or that it can undo years of damage to the brand.

The planned spring 2026 launch gives Apple just enough time to slap its design language on Google’s tech and call it revolutionary. Gemini will handle the summarizing and planning functions- the parts that actually require intelligence.

Meanwhile, Apple already pays Google around $20 billion annually to be the default iPhone search engine. Now add another billion for AI assistance.

At this rate, Google isn’t just inside Apple’s walled garden. It’s paying the mortgage.