Where database blog posts get flame-broiled to perfection
Ah, wonderful. Just what my morning needed. A fresh-from-the-oven blog post announcing a revolutionary new way to rearrange the deck chairs on my particular Titanic. Let me just top up my coffee and read about this... brilliant breakthrough.
A command line agent, you say? How positively quaint. I do so love a clever command-line contraption, another brittle binary to be lovingly wedged into our already-precarious CI/CD pipeline. I’m sure its dependencies are completely reasonable and won’t conflict with the 17 other "helper" tools the dev team discovered on Hacker News last week. The palpable progress is just… paralyzing.
And it's inspired by Claude Code! Oh, thank heavens. Because what I’ve always craved is a junior developer who hallucinates syntax, has never once seen our production schema, and confidently suggests optimizations that involve locking the most critical table in the entire cluster during peak business hours. I can't wait for the pull request that simply says, "Optimized by Tinybird Code," which will be blindly approved because, well, the AI said so. It's the ultimate plausible deniability. For them, not for me.
The focus on complex real-time data engineering problems with ClickHouse is truly the chef's kiss. My compliments. "Complex" and "real-time" are my favorite words. They pair so beautifully with PagerDuty alerts. I can practically taste the 3:17 AM adrenaline on this upcoming Columbus Day weekend. It will go something like this:
And how will we monitor the health of this new, miraculous agent? Oh, I’m sure that’s all figured out. I'm predicting a single, unhelpful log line that says task_completed_successfully printed moments before the kernel starts sacrificing processes to the OOM killer. Because monitoring is always a feature for "v2," and v2 is always a euphemism for never.
…optimized for complex real-time data engineering problems…
That line is pure poetry. You should print that on the swag. I'm genuinely excited to get the vendor sticker for this one. It'll look fantastic on my laptop lid, right next to my ones from InfluxDB, CoreOS, and that one startup that promised "infinitely scalable SQL" on a TI-83 calculator. They’re all part of my beautiful mosaic of broken promises.
So, go on. You built it.
Now if you'll excuse me, I need to go pre-write the Root Cause Analysis.
Alright, settle down, kids. Let me put on my bifocals and squint at what the internet coughed up today. "The reasons why (and why not) to use Supabase Auth instead of building your own." Oh, this is a classic. It’s got that shiny, new-car smell of a solution looking for a problem it can pretend to solve uniquely.
Back in my day, "building your own" wasn't a choice, it was the job. You were handed a stack of green-bar paper, a COBOL manual thick enough to stop a bullet, and told to have the user authentication module done by the end of the fiscal year. You didn't whine about "developer experience"; you were just happy if your punch cards didn't get jammed in the reader.
So, this "Supabase" thing... it's built on Postgres, you say? Bless your hearts. You've finally come full circle and rediscovered the relational database. We had that sorted out with DB2 on the System/370 while you lot were still figuring out how to make a computer that didn't fill an entire room. But you slapped a fancy name on it and act like you've invented fire.
Let's see what "magic" they're selling.
They're probably very proud of their "Row Level Security." Oh, you mean... permissions? Granting a user access to a specific row of data? Groundbreaking. We called that "access control" and implemented it with JCL and RACF profiles in 1988. It was ugly, it was convoluted, and it ran overnight in a batch job, but it worked. You've just put a friendly JavaScript wrapper on it and called it a revolution.
You get the power of Postgres's Row Level Security, a feature not commonly found in other backend-as-a-service providers.
Not commonly found? It’s a core feature of any database that takes itself seriously! That’s like a car salesman bragging that his new model "comes with wheels," a feature not commonly found on a canoe.
And I'm sure they're peddling JWTs like they're some kind of mystical artifact. A "JSON Web Token." It’s a glorified, bloated text file with a signature. We had security tokens, too. They were called "keys to the server room" and if you lost them, a very large man named Stan would have a word with you. You're telling me you're passing your credentials around in a format that looks like someone fell asleep on their keyboard? Seems secure.
I bet they talk a big game about "Social Logins" and "Magic Links." It's all about reducing friction, right? You're not reducing friction; you're outsourcing your front door to the lowest bidder. You want to let Google, a company that makes its money selling your data, handle your user authentication? Be my guest. We had a federated system, too. It was called a three-ring binder with every employee's password written in it. Okay, maybe that wasn't better, but at least we knew who to blame when it went missing.
This all comes down to the same old story: convenience over control. You're renting. You're a tenant in someone else's data center, praying they pay their power bill. I remember when we had a critical tape backup fail for the quarterly financials. The whole department spent 72 hours straight in the data center, smelling of ozone and stale coffee, manually restoring data from secondary and tertiary reels. You learn something from that kind of failure. You learn about responsibility.
What happens when your entire user base can't log in because Supabase pushed a bad update at 3 AM on a Tuesday?
They'll show you fancy graphs with 99.999% uptime and brag about their developer velocity. Those metrics are illusions. They last right up until the moment your startup's V.C. funding runs dry, and "Supabase" gets "acqui-hired" by some faceless megacorp. Their revolutionary auth service will be "sunsetted" in favor of some new strategic synergy, and you'll be left with a migration plan that makes swapping out a mainframe look like a picnic.
So go on, build your next "disruptive" app on this house of cards. It'll be fast. It'll be easy. And in eighteen months, when the whole thing comes crashing down in the Great Unplugging of 2026, you'll find me right here, sipping my Sanka, maintaining a COBOL program that's been running reliably since before you were born.
Now if you'll excuse me, my batch job for de-duplicating the company phone list is about to run. Don't touch anything.
Ah, another dispatch from the digital frontier. A new version of the "Elastic Stack." It seems the children in Silicon Valley have been busy, adding another coat of paint to their house of cards. One must applaud their sheer velocity, if not their intellectual rigor. While the "dev-ops wunderkinds" rush to upgrade, let us, for a moment, pour a glass of sherry and contemplate the architectural sins this release undoubtedly perpetuates.
First, one must address the elephant in the room: the very notion of using a text-search index as a system of record. Dr. Codd must be spinning in his grave at a velocity that would tear a hole in the space-time continuum. They've taken his twelve sacred rules for a relational model, set them on fire, and used the ashes to fertilize a garden of “unstructured data.” “But it’s so flexible!” they cry. Of course. So is a swamp. That doesn't mean you should build a university on it.
Then we have their proudest boast, “eventual consistency.” This is, without a doubt, the most tragically poetic euphemism in modern computing—the digital equivalent of “the check is in the mail.” They’ve looked upon the CAP theorem not as a sobering set of trade-offs, but as a menu from which they could blithely discard Consistency. “Your data will be correct… eventually… probably. Just don’t look too closely or run two queries in a row.” It’s a flagrant violation of the very first principles of ACID, but I suppose atomicity is far too much to ask when you’re busy being “web-scale.”
Their breathless praise for being "schemaless" is a monument to intellectual laziness. Why bother with the architectural discipline of a well-defined schema—the very blueprint of your data's integrity—when you can simply throw digital spaghetti at the wall and call it a "data lake"? Clearly they've never read Stonebraker's seminal work on the pitfalls of such "one size fits all" architectures. This isn't innovation; it's abdication.
And what of the "stack" itself? A brittle collection of disparate tools, bolted together and marketed as a unified whole. It’s a Rube Goldberg machine for people who think normalization is a political process. Each minor version, like this momentous leap from 8.17.9 to 8.17.10, isn't a sign of progress. It's the frantic sound of engineers plugging yet another leak in a vessel that was never seaworthy to begin with.
Ultimately, the greatest tragedy is that an entire generation is being taught to build critical systems on what amounts to a distributed thesaurus. They champion its query speed for analytics while ignoring that they are one race condition away from catastrophic data corruption. They simply don't read the papers anymore. They treat fundamental theory as quaint suggestion, not immutable law.
Go on, then. "Upgrade." Rearrange the deck chairs on your eventually-consistent Titanic. I'll be in the library with the grown-ups.
Oh, look. A new version. And they recommend we upgrade. That's adorable. It’s always a gentle "recommendation," isn't it? The same way a mob boss "recommends" you pay your protection money. I can already feel the phantom buzz of my on-call pager just reading this announcement. My eye is starting to twitch with the memory of the Great Shard-ocalypse of '22, which, I recall, also started with a "minor point release."
But fine. Let's be optimistic. I’m sure this upgrade from 8.18.4 to 8.18.5 will be the one that finally makes my life easier. I'm sure it's packed with features that will solve all our problems and definitely won't introduce a host of new, more esoteric ones. Let’s break down the unspoken promises, shall we?
The "Simple" Migration. Of course, it's just a point release! What could go wrong? It’s a simple, one-line change in a config file, they'll say. This is the same kind of "simple" as landing a 747 on an aircraft carrier in a hurricane. I'm already mentally booking my 3 AM to 6 AM slot for "unforeseen cluster reconciliation issues," where I'll be mainlining coffee and whispering sweet nothings to a YAML file, begging it to love me back. Last time, "simple" meant a re-indexing process that was supposed to take an hour and instead took the entire weekend and half our quarterly budget in compute credits.
The "Crucial" Bug Fixes. I can't wait to read the release notes to discover they’ve fixed a bug that affects 0.01% of users who try to aggregate data by the fourth Tuesday of a month that has a full moon while using a deprecated API endpoint. Meanwhile, the memory leak that requires us to reboot a node every 12 hours remains a charming personality quirk of the system. This upgrade is like putting a tiny, artisanal band-aid on a gunshot wound. It looks thoughtful, but we're all still going to bleed out.
The "Seamless" Rolling Restart. They promise a seamless update with no downtime. This is my favorite fantasy genre. The first node will go down smoothly. The second will hang. The third will restart and enter a crash loop because its version of a plugin is now psychically incompatible with the first. Before you know it, the "seamless" process has brought down the entire cluster, and you’re explaining to your boss why the entire application is offline because you followed the instructions.
We recommend a rolling restart to apply the changes. This process is designed to maintain cluster availability. Ah, yes. "Designed." Like the Titanic was "designed" to be unsinkable. It's a beautiful theory that rarely survives contact with reality.
So yeah, I’ll get right on that upgrade. I'll add it to the backlog, right under "refactor the legacy monolith" and "achieve world peace."
Go ahead and push the button. I'll see you on the post-mortem call.
Alright, settle down, kids. Rick "The Relic" Thompson here. I just spilled my Sanka all over my terminal laughing at this latest dispatch from the "cloud." You youngsters and your blogs about "discoveries" are a real hoot. You write about upgrading a database like you just split the atom, when really you just paid a cloud vendor to push a button for you. Let me pour another lukewarm coffee and break this down for you.
First off, this whole "Amazon Aurora Blue/Green Deployment" song and dance. You discovered... a standby database? Congratulations. In 1988, we called this "the disaster recovery site." It wasn't blue or green; it was beige, weighed two tons, and lived in a bunker three states away. We didn't have a fancy user interface to "promote" the standby. We had a binder full of REXX scripts, a conference call with three angry VPs, and a physical key we had to turn. You've just reinvented the hot-swap with a pretty color palette. DB2 HADR has been doing this since you were in diapers.
And you're awfully proud of your "near-zero downtime." Let me tell you about downtime, sonny. "Near-zero" is the marketing department's way of saying it still went down. We had maintenance windows that were announced weeks in advance on green bar paper. If the batch jobs didn't finish, you stayed there all weekend. You lived on vending machine chili and adrenaline. We didn't brag about "near-zero" downtime; we were just thankful to have the system back up by Monday morning so the tellers could process transactions. Your carefully orchestrated, one-click failover is adorable. Did you get a participation trophy for it?
Oh, the scale! "Tens of billions of daily cloud resource metadata entries." That's cute. It really is. You're processing log files. Back in my day, we processed the entire financial ledger for a national bank every single night, on a machine with 64 megabytes of memory. That's megabytes. We didn't have "metadata," we had EBCDIC-encoded files on 3480 tape cartridges that we had to load by hand. You're bragging about reading a big text file; we were moving the actual money, one COBOL transaction at a time.
And this database is apparently serving "hundreds of microservices." You know what we called a system that did hundreds of different things? A single, well-written monolithic application running on CICS. You didn't need "hundreds" of anything. You needed one program, a team that knew how it worked, and a line printer that could handle 2,000 lines per minute. You kids built a digital Rube Goldberg machine and now you're writing articles about how you managed to change a lightbulb in one of its hundred little rooms without the whole contraption collapsing. Bravo.
In this post, we share how we upgraded our Aurora PostgreSQL database from version 14 to 16...
Anyway, thanks for the trip down memory lane. It's good to know that after forty years, the industry is still congratulating itself for solving problems that were already solved when Miami Vice was on the air.
I’ll be sure to file this blog post in the same place I filed my punch cards. The recycling bin.
Ah, another masterpiece from the content marketing machine. I was just thinking my morning coffee needed a little more... corporate wishful thinking. And here we are, celebrating the "enthusiasm" for UUIDv7. Enthusiasm. That's what we're calling the collective sigh of relief from engineers who've been screaming about UUIDv4's index fragmentation for the better part of a decade.
Let's dive into this "demo," shall we? It’s all so clean and tidy here in the "lab."
-- reset (you are in a lab) ! pkill -f "postgres: .* COPY"
Right out of the gate, we're starting with a pkill. How... nostalgic. It reminds me of the official "fix" for the staging environment every Tuesday morning after the weekend batch jobs left it in a smoldering heap. It’s comforting to see some traditions never die. So we’re starting with the assumption that the environment is already broken. Sounds about right.
And the benchmark itself? A single, glorious COPY job streaming 10 million rows into a freshly created table with no other load on the system. It's the database equivalent of testing a car's top speed by dropping it out of a plane. Sure, the numbers look great, but it has absolutely no bearing on what happens when you have to, you know, drive it in traffic.
Look at these UUIDv7 results! "Consistently high throughput, with brief dips likely due to vacuum, background I/O or checkpoints..." Brief dips. That’s a cute way to describe those terrifying moments where the insert rate plummets by 90% and you're not sure if it's ever coming back. I remember those "brief dips" from the all-hands demo for "Project Velocity." They weren't so brief when the VP of Sales was watching the dashboard flatline, were they? We were told those were transient telemetry anomalies. Looks like they've been promoted to a feature.
And the conclusion? UUIDv7 delivers "fast and predictable bulk load performance." Predictable, yes. Predictably stalling every 30-40 seconds.
Now for the pièce de résistance: the UUIDv4 run. The WAL overhead spikes, peaking at 19 times the input data. Nineteen times. I feel a strange sense of vindication seeing that number in print. I remember sitting in a planning meeting, waving a white paper about B-Tree fragmentation, and being told that developer velocity was more important than "arcane storage concerns." Well, here it is. The bill for that velocity, payable in disk I/O and frantic calls to the storage vendor. This isn't a surprise; it's a debt coming due.
But the best part, the absolute chef's kiss of this entire article, comes right at the end. After spending paragraphs extolling the virtues of sequential UUIDv7, we get this little gem:
However, before you rush to standardize on UUIDv7, there’s one critical caveat for high-concurrency workloads: the last B+Tree page is a hotspot...
Oh, is it now? You mean the thing that everyone with a basic understanding of database indexes has known for twenty years is suddenly a critical caveat? You're telling me this revolutionary new feature, the one that’s supposed to solve all our problems, is great... as long as only one person is using it at a time? This has the same energy as the engineering director who told us our new, "infinitely scalable" message queue was production-ready, but we shouldn't put more than a thousand messages a minute through it.
And the solution? This absolute monstrosity: (pg_backend_pid()%8) * interval '1 year'.
Let me translate this for the people in the back. To make our shiny new feature not fall over under the slightest hint of real-world load, we have to bolt on this... thing. A hacky, non-obvious incantation using the internal process ID and a modulo operator to manually shard our inserts across... time itself? It's the engineering equivalent of realizing your car only has a gas pedal and no steering wheel, so you solve it by having four of your friends lift and turn it at every intersection. It's not a solution; it's an admission of failure.
This is classic. It's the same playbook:
Anyway, this has been a wonderful trip down a very bitter memory lane. You've perfectly illustrated not just a performance comparison, but the entire engineering culture that leads to these kinds of "solutions."
Thanks for the write-up. I will now cheerfully promise to never read this blog again.
Ah, another visionary blog post. It's always a treat to see the future of data architecture laid out so... cleanly. I especially appreciate the diagram with all the neat little arrows. They make the whole process of gluing together seven different managed services look like a simple plug-and-play activity. My PTSD from the Great Sharded-Postgres-to-Dynamo-That-Actually-Became-Cassandra Migration of 2022 is already starting to feel like a distant, amusing memory.
I must commend the author’s faith in a “scalable, flexible, and secure data infrastructure.” We've certainly never heard those adjectives strung together before. It’s comforting to know that this time, with MongoDB Atlas and a constellation of AWS services, it’s finally true. My on-call phone just buzzed with what I'm sure is a notification of pure, unadulterated joy.
My favorite part is the casual mention of how MongoDB’s document model handles evolving data structures.
Whether a car has two doors or four, a combustion or an electric drive, MongoDB can seamlessly adapt to its VSS-defined structure without structural rework, saving time and money for the OEMs.
My eye started twitching at “seamlessly adapt... without structural rework.” I remember hearing that right before spending a weekend writing a script to manually backfill a “flexible” field for two million records because one downstream service was, in fact, expecting the old, rigid schema. But I’m sure that was a one-off. This VSS standard sounds very robust. It has a hierarchical tree, which has historically never led to nightmarish recursive queries or documents that exceed the maximum size limit.
And the move from raw data to insight is just... breathtaking in its simplicity.
It’s just so elegant. You barely notice the five different potential points of failure, each with its own billing model and configuration syntax.
I’m also genuinely moved by the vision of “empowering technicians with AI and vector search.” A technician asking, “What is the root cause of the service engine light?” and getting a helpful, context-aware answer from an LLM. This is a far better future than the one I live in, where the AI would confidently state, “Based on a 2019 forum post, the most common cause is a loose gas cap, but it could also be a malfunctioning temporal flux sensor. Have you tried turning the vehicle off and on again?” The seamless integration of vector search with metadata filters is a particularly nice touch. I’m sure there will be zero performance trade-offs or bizarre edge cases when a query combines a fuzzy semantic search with a precise geographic bounding box. Absolutely none.
The promise to “scale to millions of connected vehicles with confidence” is the real chef’s kiss. It fills me with the kind of confidence I usually reserve for a DROP TABLE command in the production database after being awake for 36 hours. The confidence that something is definitely about to happen.
This architecture doesn’t eliminate problems; it just offers an exciting, venture-backed way to have new ones. And I, for one, can't wait to be paged for them.
Well, well, well. Look what we have here. Another "strategic partnership" press release disguised as a technical blog. I remember my days in the roadmap meetings where we'd staple two different products together with marketing copy and call it "synergy." It's good to see some things never change. Let's peel back the layers on this masterpiece of corporate collaboration, shall we?
It’s always a good sign when your big solution to "cost implications" is an "Agentic RAG" workflow that, by your own admission, can take 30-40 seconds to answer a single question. They call this a "workflow"; I call it making a half-dozen separate, slow API calls and hoping the final result makes sense. The "fix" for this glacial performance? A complex, multi-step fine-tuning process that you, the customer, get to implement. They sell you the problem and then a different, more complicated solution. Brilliant.
I had to laugh at the description of FireAttention. They proudly announce it "rewrites key GPU kernels from scratch" for speed, but then casually mention it comes "potentially at the cost of initial accuracy." Ah, there it is. The classic engineering shortcut. "We made it faster by making it do the math wrong, but don't worry, we have a whole other process called 'Quantization-Aware Training' to try and fix the mess we made." It’s like breaking someone’s leg and then bragging about how good you are at setting bones.
The section on fine-tuning an SLM is presented as a "hassle-free" path to efficiency. Let's review this "hassle-free" journey: install a proprietary CLI, write a custom Python script to wrangle your data out of their database into the one true JSONL format, upload it, run a job, monitor it, deploy the base model, and then, in a separate step, deploy your adapter on top of it. It’s so simple! Why didn't anyone think of this before? It’s almost like the 'seamless integration' is just a series of command-line arguments.
And MongoDB's "unique value" here is... being a database. Storing JSON. Caching responses. Groundbreaking stuff. The claim that it’s "integral" for fine-tuning because it can store the trace data is a masterclass in marketing spin. You know what else can store JSON for a script to read? A file. Or any other database on the planet. Presenting a basic function as a cornerstone of a complex AI workflow is a bold choice.
"Organizations adopting this strategy can achieve accelerated AI performance, resource savings, and future-proof solutions—driving innovation and competitive advantage..."
Of course they can. Just follow the 17-step "simple" guide. It's heartening to see the teams are still so ambitious, promising a future-proof Formula 1 car built from the parts of a lawnmower and a speedboat.
It’s a bold strategy. Let’s see how it plays out for them.
Alright, settle down, kids. Let me put down my coffee—the kind that's brewed strong enough to dissolve a floppy disk—and read this... this press release.
Oh, wonderful. "Neki." Sounds like something my granddaughter names her virtual pets. So, you've taken the shiniest new database, Postgres, and you're going to teach it the one trick that every database has had to learn since the dawn of time: how to split a file in two. Groundbreaking. Truly, my heart flutters with the thrill of innovation. You've made "explicit sharding accessible." You know what we called "explicit sharding" back in my day? We called it DATABASE_A and DATABASE_B, and we used a COBOL program with a simple IF-THEN-ELSE statement to decide where the data went. The whole thing ran in a CICS region and was managed with a three-inch binder full of printed-out JCL. Accessible.
They say it's not a fork of Vitess, their other miracle cure for MySQL. No, this time they're architecting from first principles.
To achieve Vitess’ power for Postgres we are architecting from first principles...
First principles? You mean like, Edgar F. Codd's relational model from 1970? Or are you going even further back? Are you rediscovering how to magnetize rust on a plastic tape? Because we solved this problem on System/370 mainframes before most of your developers were even a twinkle in the milkman's eye. We called it data partitioning. We had partitioned table spaces in DB2 back in the mid-80s. You'd define your key ranges on the CREATE TABLESPACE statement, submit the batch job, and go home. The next morning, it was done. No "design partners," no waitlist, no slick website with a one-word name ending in .dev.
And the hubris... "running at extreme scale." Let me tell you about extreme scale, sonny. Extreme scale is watching the tape library robot, a machine the size of a small car, frantically swapping cartridges for a 28-hour end-of-year batch reconciliation. It's realizing the backup job from Friday night failed but you only find out Monday morning when someone tries to run a report and the whole system grinds to a halt. It's physically carrying a box of punch cards up three flights of stairs because the elevator is out, and praying you don't trip. That's extreme. Your "extreme scale" is just a bigger number in a billing dashboard from a cloud provider that's just renting you time on... you guessed it... someone else's mainframe.
They're "building alongside design partners at scale." I love that. We had a term for that, too: "unpaid beta testers." We'd give a new version of the payroll system to the accounting department and let them find all the bugs. The only difference is they didn't get a featured blog post out of it; they got a memo and a stern look from their department head.
So let me predict the future for young "Neki":
And in five years, when this whole sharded mess becomes an unmanageable nightmare of distributed state and cross-shard join-latency, PlanetScale will announce its next revolutionary product: a tool that seamlessly "un-shards" your data back into a single, robust Postgres instance. They’ll call it "cohesion" or "unity" or some other nonsense, and a whole new generation of developers will call it revolutionary.
Now if you'll excuse me, I've got a cryptic error code from an IMS database to look up on a microfiche. Some of us still have real work to do.
Ah, yes. I’ve just had the… pleasure… of perusing this article on the "rise of intelligent banking." One must applaud the sheer, unadulterated ambition of it all. It’s a truly charming piece of prose, demonstrating a grasp of marketing buzzwords that is, frankly, breathtaking. A triumph of enthusiasm over, well, computer science.
The central thesis, this grand "Unification" of fraud, security, and compliance, is a particularly bold stroke. It’s a bit like deciding to build a Formula 1 car, a freight train, and a submarine using the exact same blueprint and materials for the sake of "synergy." What could possibly go wrong? Most of us in the field would consider these systems to have fundamentally different requirements for latency, consistency, and data retention. But why let decades of established systems architecture get in the way of a good PowerPoint slide?
They speak of a single, glorious "Unified Data Platform." One can only imagine the glorious, non-atomic, denormalized splendor! It’s a bold rejection of first principles. Edgar Codd must be spinning in his grave like a failed transaction rollback. Why bother with his quaint twelve rules when you can simply pour every scrap of data—from real-time payment authorizations to decade-old regulatory filings—into one magnificent digital heap? It's so much more agile that way.
The authors’ treatment of the fundamental trade-offs in distributed systems is especially innovative. Most of us treat Brewer's CAP theorem as a fundamental constraint, a sort of conservation of data integrity. These innovators, however, seem to view it as more of a… à la carte menu.
“We’ll take a large helping of Availability, please. And a side of Partition Tolerance. Consistency? Oh, just a sliver. No, you know what, leave it off the plate entirely. The AI will fix it in post-production.”
It’s a daring strategy, particularly for banking. Who needs ACID properties, after all?
One gets the distinct impression that the authors believe AI is not a tool, but a magical panacea capable of transmuting a fundamentally unsound data architecture into pure, unadulterated insight. It’s a delightful fantasy. They will layer sophisticated machine learning models atop a swamp of eventually-consistent data and expect to find truth. It reminds one of hiring a world-renowned linguist to interpret the grunts of a baboon. The analysis may be brilliant, but the source material is, and remains, gibberish.
Clearly they've never read Stonebraker's seminal work on the fallacy of "one size fits all" databases. But why would they? Reading peer-reviewed papers is so… 20th century. It's far more efficient to simply reinvent the flat file, call it a "Data Lakehouse," and declare victory.
In the end, one must admire the audacity. This isn’t a blueprint for the future of banking. It’s a well-written apology for giving up.
It's not an "intelligent bank"; it's a very, very fast abacus that occasionally loses its beads. And they've mistaken the rattling sound for progress.