Hey everyone, welcome back to My Weird Prompts. I am Corn, and I am sitting here in our living room in Jerusalem with my brother.
Herman Poppleberry, at your service. It is a beautiful day outside, but I have been buried in documentation all morning because of a prompt our housemate Daniel sent over. He is currently obsessed with the idea of architectural minimalism.
Daniel definitely hit a nerve with this one. He was asking about the state of data storage in two thousand twenty-six. Specifically, he wants to know if the modern, high-performance relational databases we use every day, like Postgres, have finally reached a point where they have made data lakes and data warehouses obsolete.
It is the great consolidation question. We have seen this trend lately where developers are trying to put everything into Postgres. There is this meme in the engineering community right now that says, just use Postgres for everything. Need a queue? Postgres. Need a vector store for A I? Postgres. Need a document store? Postgres. It is almost like a religious movement at this point. People are tired of the complexity of the modern data stack, and they want to go back to a simpler time.
It is tempting, right? If you can keep your architecture simple and stick to one battle-tested engine, why wouldn't you? It reduces your operational overhead, your security surface area, and the number of specialized engineers you need to hire. But Daniel's prompt pushes us to look deeper. Are we just over-engineering our storage when we build these massive data lakes, or is there a fundamental architectural limit that even a powerhouse like Postgres eighteen can't cross?
That is exactly the tension. We are going to dive into the evolution of these systems, how A I has completely changed the requirements for what we store, and whether the data warehouse is actually a dying breed or just evolving into something more powerful. We have to look at the physics of data.
We actually touched on the history of how humans organize information back in episode eight hundred sixteen, when we talked about the move from ancient scrolls to Structured Query Language. But today, the scale is just different. We are talking about petabytes of telemetry and A I training data. The sheer volume changes the rules of the game.
Right, and as we discussed in episode one thousand thirty-eight, the secret architecture of the A I age is taxonomy. But taxonomy requires a place to live. So, let us start with the evolution. Why did we even move away from simple relational databases in the first place? To understand where we are going, we have to understand the "Data Gravity" problem.
Data Gravity is such a great term. It is the idea that data has mass. As a dataset grows, it becomes harder and more expensive to move. This creates a pull that attracts applications and services toward the data. If you have a petabyte of data in one place, you aren't going to move it to run a query; you are going to move your query to the data.
And in the early days, we didn't have that much mass. If you go back to the nineteen nineties and early two thousands, everything lived in a relational database. It was the era of the silo. You had your transactional database that handled orders, customers, and inventory. It was built for A C I D compliance, which stands for Atomicity, Consistency, Isolation, and Durability. Basically, it was built so that if a bank transfer happened, the money didn't just vanish into thin air if the power went out.
Those are O L T P systems, or Online Transactional Processing. They are optimized for writing and reading specific rows very quickly. You want to know what customer one hundred two bought yesterday? Postgres will give you that in a millisecond. But then, companies realized they wanted to ask big questions. They didn't want to know just one customer's balance; they wanted to know the average balance of every customer over the last five years, segmented by zip code and age.
And when you run a query like that on a live transactional database, everything grinds to a halt. You are locking tables, you are chewing up Central Processing Unit cycles, and suddenly your customers can't check out because the data scientists are running a massive report. This is the fundamental conflict between transactions and analytics. Transactions want to touch one row at a time. Analytics want to touch every row at once.
That led to the birth of the data warehouse. We started extracting data from the production databases, transforming it, and loading it into a separate system. This was the E T L process. These warehouses, like the early versions of Teradata or later Redshift and Snowflake, were built for O L A P, or Online Analytical Processing.
The big shift there was moving from row-based storage to columnar storage. This is a technical distinction that sounds boring but changes everything. In a standard database, all the data for one customer is stored together on the disk. Name, address, credit card, last purchase. All in one block. In a columnar warehouse, all the names are stored together in one block, all the prices are stored together in another, and all the dates are stored together in a third.
Which is brilliant for analytics. If you only need to calculate the sum of all sales, a columnar engine only has to read the sales column. It ignores the names, the addresses, and the zip codes. It is like having a grocery store where instead of walking down every aisle to find the milk, you can just press a button and all the milk in the store flies toward you. It reduces disk I O by ninety percent for analytical queries.
But then we hit the next wall in the mid-two-thousands, which was the variety and volume of data. Not everything fits into a neat table with rows and columns. We started getting images, audio files, raw logs from servers, and social media feeds. That is where the data lake came in. The philosophy was schema-on-read. You don't worry about the structure when you save it; you worry about the structure when you analyze it.
It sounds messy, and for a while, it was. People called them data swamps because you could never find anything. But it was cheap. Storing a petabyte in Amazon S three is a fraction of the cost of storing it in a high-performance database. You are basically paying for the electricity to keep the hard drives spinning, rather than the high-end C P U power required to manage a relational index.
This brings us to the present day and Daniel's question. We now have things like the Lakehouse architecture, which tries to combine the two. And we have Postgres eighteen, which was released just this past January. It has massive improvements in parallel query execution and adaptive query optimization. It can handle much larger workloads than it could even two years ago. So, Corn, is the dream of the single-database architecture finally real? Can we just put that petabyte into Postgres eighteen and call it a day?
I think we have to look at the specific performance bottlenecks. Even with Postgres eighteen, you are still dealing with the overhead of the Write-Ahead Log, or W A L. Every time you write to Postgres, it has to ensure that the data is safely logged to disk to maintain those A C I D guarantees. That is a lot of overhead if you are just trying to dump ten billion rows of sensor telemetry into a bucket.
And then there is the Vacuum problem. In Postgres, when you update or delete a row, the old version stays on the disk until a process called Vacuum cleans it up. At petabyte scale, the Vacuum process itself can become a massive resource hog. In a data lake, you are usually dealing with immutable files. You write a Parquet file or an Iceberg table, and it stays there. You don't update it; you just write a new version. This makes scaling much, much easier.
Let's talk about that "Compute-Storage Decoupling" mechanism. This is really the secret sauce of why cloud-native warehouses like Snowflake or BigQuery still win on elasticity. In a traditional database, your compute power and your storage are usually tied together. If you want more disk space, you often have to upgrade the whole server, which gives you more C P U and memory you might not need. Or vice versa.
Cloud-native warehouses decoupled those. You can store ten petabytes of data for very little money on cheap object storage, and you only pay for the massive compute power during the twenty seconds it takes to run a query. You can spin up a cluster of a hundred servers to crunch a massive join, and then turn them off immediately. Postgres, even with all its modern bells and whistles, still struggles with that specific elastic scaling because it assumes it owns the local disk.
There are extensions, though. We should mention things like Citus, which turns Postgres into a distributed database, or pg analytics, which adds columnar capabilities. But at a certain scale, you are essentially rebuilding a data warehouse inside a relational database. You are fighting the original design of the system. It is like trying to turn a bicycle into a jet engine by adding more and more parts. Eventually, it is just better to buy a jet.
And let us look at the storage formats. Modern data lakes aren't just piles of files anymore. We have Apache Iceberg, which has become the industry standard for open table formats. Iceberg gives you the reliability of a database, like snapshots and transactions, but it sits on top of cheap cloud storage. It allows different engines to talk to the same data.
That is a huge point. If you put everything in a single Postgres instance, you have created a new silo. You have to go through the Postgres A P I to get any of that data out. With an open format like Iceberg, you could have a Spark job processing data, a Snowflake warehouse querying it, and an A I model training on it, all looking at the exact same files simultaneously.
That is a perfect transition to the second part of Daniel's prompt: the role of A I. Because A I is now the primary consumer of this data. It isn't just humans looking at dashboards anymore. A I models have very different appetites than human analysts.
When we talk about A I and these large repositories, we are looking at a few specific functions. Data mining, context extraction, pattern analysis, and anomaly detection. These all require different things from the underlying storage. Let us start with context extraction and Retrieval-Augmented Generation, or R A G.
R A G is how we make A I models smart about a specific company's data. You take your documents, your emails, and your manuals, you turn them into vector embeddings, and you store them. Now, Postgres has pgvector, which is great. It allows you to store those embeddings and do similarity searches right next to your relational data. For a lot of companies, that is all they need.
But if you are a massive enterprise with billions of vectors, the indexing becomes a nightmare. The H N S W indexing, which stands for Hierarchical Navigable Small World, is very memory-intensive. It is essentially a giant graph that the A I uses to find related concepts. If you try to run a massive vector index on your production database, you are going to starve your transactional queries of memory.
This is where the specialized data lake or a dedicated vector database still wins at scale. They are optimized for the high-dimensional math that A I requires. And then there is the training aspect. If you are fine-tuning a model or training a small language model from scratch, you need to feed it raw data at an incredible rate. You need massive throughput.
A data lake is built for that. It can stream data into a G P U cluster much faster than a relational database can pipe it through a traditional S Q L interface. Think about the "Ground Truth" problem. We talked about this in episode five hundred thirty-nine, about the A I pipeline and scaling curiosity. If your ground truth is scattered across fifty different Postgres databases, your A I is going to have a very fragmented view of the world.
I want to talk about a specific case study here. Imagine a global manufacturing company. They have ten thousand machines in factories all over the world, each with a hundred sensors. Every second, those sensors are reporting temperature, vibration, and power consumption. That is billions of data points a day.
If you try to put that into a standard Postgres database, you are going to spend a fortune on high-end storage and C P U just to keep up with the writes. And for what? Most of that data is "cold." You only need it if something goes wrong or if you are training a predictive maintenance model.
Right. So the smart architecture is to use a data lake for the raw telemetry. It is cheap, it is durable, and it is ready for the A I to mine for patterns. But you use Postgres for the "hot" metadata. The Postgres database knows which machine is in which factory, who the manager is, and what the maintenance schedule looks like. The A I acts as the bridge, pulling the "what" from the lake and the "who" and "where" from the database.
That leads us to anomaly detection. In two thousand twenty-six, we are seeing this shift from batch processing to real-time streaming ingestion. If you are a credit card company or a cybersecurity firm, you are looking for patterns across billions of events per second. You need a system that can look at the current stream of data and compare it to the historical baseline stored in the warehouse.
Doing that comparison in a standard database is slow because you are often comparing a single event against a multi-year history. A data lakehouse can handle that because it can keep the hot data in memory for immediate analysis while the cold data stays in Parquet or Iceberg files on disk. The A I can then scan those files using massively parallel processing to find that needle in the haystack.
It is really about the shape of the work. A I models don't think in rows. They think in tensors and high-dimensional spaces. The data warehouse of today is essentially becoming the feature store for the A I of tomorrow. It is where you store the ground truth that the A I uses to make decisions.
And let's not forget the "Point-in-Time" problem in A I training. When you are training a model to predict the future, you have to be able to show it exactly what the world looked like at a specific moment in the past. You can't show it the data as it exists today; you have to show it the data as it existed on March twelfth, two thousand twenty-four.
That is incredibly hard to do in a live relational database without massive complexity. But in a data lake with a format like Iceberg, you have built-in snapshots. You can literally tell the system, "Show me the entire state of the world as of two years ago," and it can do it instantly without affecting production performance.
That is the organizational impact Daniel was asking about. What are the tangible benefits for a company using these massive systems? I think the biggest one is the democratization of insight. When you have a unified data lake or warehouse, the A I can act as a bridge between the data and the non-technical employees.
Right, we are seeing these A I agents now that can write their own S Q L or crawl a data lake to answer a question. If a marketing manager wants to know why sales dropped in a specific region, they don't have to wait for a data analyst to write a report. They ask the A I, and the A I goes into the warehouse, performs the pattern analysis, and gives them a natural language answer.
But that only works if the data is accessible. If it is locked in a production database that the A I isn't allowed to touch because it might crash the system, then that insight remains hidden. The warehouse acts as a safe playground for A I. It is a place where the A I can explore, experiment, and fail without taking down the company's website.
There is also the cost factor. For a large organization, the practical benefit is the ability to store everything forever. We talked about this in episode six hundred seventy-four, the quest for data permanence. In the past, we had to delete old logs or archive them to tape because it was too expensive to keep them online.
Now, with cheap cloud storage and metadata layers like Iceberg, you can keep every single click, every sensor reading, and every transaction indefinitely. And three years from now, when a new A I model comes out that is even better at pattern detection, you can turn it loose on that historical data to find insights you missed the first time. The data you are collecting today is an investment in the A I of the future.
So, who is best positioned to derive value from this? Is it just the tech giants?
Definitely not. I think we are seeing a middle-market shift. Any company that has a high volume of transactions or a complex supply chain needs this. If you are a mid-sized manufacturing company in the Midwest, and you have sensors on all your machines, you are generating terabytes of telemetry.
If you just store that in a standard database, you can see if a machine is broken right now. But if you store it in a data lake and use A I for anomaly detection, you can predict that the machine is going to break three weeks from now. That is a tangible, multi-million dollar benefit. It is the difference between being reactive and being proactive.
It is also huge for any company dealing with compliance or regulation. If you are in healthcare or finance, being able to prove exactly what happened five years ago by querying a massive, immutable data lake is a lifesaver during an audit. You have a perfect, unchangeable record of every event.
I think we should get into some practical takeaways for the people listening who might be making these architectural decisions right now. Because the Postgres-as-everything movement is very seductive, but it has its limits. We need to define the "Complexity Threshold."
My first rule would be the "Terabyte Wall." If your data fits on a single large drive, say under ten terabytes, you probably don't need a data warehouse. Modern servers can have dozens of terabytes of R A M and massive N V M e drives. If you are in that range, Postgres is your best friend. It is simple, it is fast, and the tooling is incredible.
But once you hit the point where you need to scale compute independently of storage, or once you have multiple different teams who all need to access the same data using different tools, that is when you need to look at a Lakehouse or a Warehouse. That is the "Polyglot Persistence" rule. Use the right tool for the job.
And don't fight the architecture. If you find yourself writing incredibly complex S Q L with fifteen joins over tables with hundreds of millions of rows, and you are spending all your time tuning indexes and vacuuming the database, that is a signal. Your database is telling you it wasn't built for this. You are trying to use a Ferrari to pull a plow.
I love that analogy. A Ferrari has a lot of horsepower, sure. And if you weld a hitch to the back and put some knobby tires on it, you might get it to pull a plow across a field. But you are going to ruin the engine, the transmission is going to explode, and the field isn't going to look very good. Meanwhile, a tractor might have less top speed, but it will pull that plow all day without breaking a sweat.
Another takeaway is to embrace open formats. Whether you use Postgres or Snowflake or Databricks, try to keep your underlying data in an open format like Parquet or Iceberg. It prevents vendor lock-in and it makes it so much easier for A I tools to plug in later. You want your data to be "liquid," not locked in a proprietary vault.
Before you decide to move from a warehouse back to a monolithic database, you really need to audit your query patterns. Are you doing point-lookups? Use Postgres. Are you doing massive aggregations and joins over historical data? Stick with the warehouse. Don't let the "minimalism" trend blind you to the physical realities of disk I O and C P U cycles.
I think the future is going to be a much tighter integration between these two. We are already seeing Postgres eighteen adding features that make it easier to query external tables in S three. Eventually, the line between the database and the warehouse will blur, but the underlying mechanisms—row-based for transactions and columnar-based for analytics—will still be different.
It is funny, Daniel’s prompt really gets at the heart of this human desire for a silver bullet. We want one thing that does everything. We want the Swiss Army Knife that is also a chef's knife and a chainsaw. But the reality of the digital world is that specialization usually wins at scale.
It is a reflection of how our own brains work, actually. We have short-term memory for immediate tasks, which is like our relational database. It is fast, it is volatile, and it is focused on the "now." And we have long-term memory where we store decades of patterns and context, which is like our data lake. You wouldn't want to try to run your daily life using only your long-term memory, and you can't build a life story out of just short-term memory.
That is a great analogy. You need both to be a functional human, and a modern company needs both to be a functional organization. The "Postgres-as-everything" movement is a great way to start, but it is not where you end up if you want to build something that lasts.
Before we wrap up, I want to mention one more thing about the A I side. We are starting to see A I agents that don't just consume the data, but actually manage the schema. They look at how people are querying the data and they automatically suggest new indexes or reorganization of the tables.
That is the next frontier. We spend so much time as humans trying to impose order on our data. We talked about this in episode four hundred ninety-two, the quest for a graph-based Operating System. Imagine an A I that just watches the chaos of a data lake and builds its own invisible taxonomy so that it can answer any question we throw at it.
It turns the data swamp back into a crystal-clear lake without a human ever having to write a single line of E T L code. That is the dream. The A I becomes the ultimate librarian, finding the connections between the "hot" transactional data and the "cold" historical archives.
Well, we have covered a lot of ground today. From the silos of the nineties to the decoupled clouds of today, and the A I-driven future. I hope this gives Daniel some clarity on why we aren't quite ready to throw away our data warehouses just yet.
Even if Postgres eighteen is an absolute beast of an engine. It is all about the right tool for the right job. Don't fight the architecture; understand the trade-offs.
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Thanks for joining us today in Jerusalem. This has been a lot of fun. I think I need to go read some more about those Iceberg table specs now.
Definitely. Until next time, keep asking those weird questions. This has been My Weird Prompts.
Take care, everyone.
I think we hit the mark on that one, Herman. Although, I wonder if we should have spent more time on the specific latency differences between B-tree and L S M trees.
Maybe in episode eleven hundred two. Let's not give Daniel too many ideas at once. His head might explode if we start talking about log-structured merge-trees.
Fair enough. Let's go see if there is any of that hummus left in the kitchen.
I think Daniel might have finished it while he was researching Postgres extensions, but it is worth a look.
Typical housemate behavior. Alright, signing off. Bye everyone.
