XTX's 500PB Open Source Lessons, Enterprise RAG Reality, Cachey, and the Art of Performance Engineering
When algorithmic trading meets open source, RAG systems hit enterprise reality, and custom optimization beats general-purpose excellence.
🏗️ XTX Markets Just Open-Sourced Their Exabyte-Scale Filesystem (And It's Actually Interesting)
When an algorithmic trading firm tells you they've been running 500PB across 40,000 drives "without losing a single byte" - well, that gets your attention. XTX Markets just open-sourced TernFS, their distributed filesystem, and reading through their technical deep-dive is like getting a masterclass in what large-scale storage actually looks like in practice.
The metadata sharding approach is clever: 256 logical shards from day one means no rebalancing nightmares when you scale (which is exactly the kind of forward-thinking that separates real engineering from "we'll figure it out later")
Immutable files with snapshot protection: Once written, files can't be changed, but you get automatic protection against the dreaded rm -rf moments (truth!)
Purpose-built for substantial files: Median size of 2MB means this isn't trying to be everything to everyone - it's optimized for the data engineering reality where you're actually processing meaningful datasets
A good way to think about this is that TernFS acknowledges the brutal economics of large-scale data storage and builds solutions around those constraints rather than pretending they don't exist. The firm started with "a couple of desktops and an NFS server" and ended up needing to store hundreds of petabytes - which is probably a more relatable scaling journey than most of us would like to admit.
📊 Databricks Shares Their Database Reliability Playbook (The Honest Version)
Database reliability conversations usually involve a lot of uptime percentages and theoretical guarantees, but this Databricks piece cuts through that to talk about what actually works when your database is the nervous system of someone's business operations (which is terrifying when you think about it).
Monitoring that actually helps: They focus on capturing the full context of failures, not just error counts - because knowing something broke is utterly useless without understanding why it broke
Automated recovery with human judgment: Smart enough to handle the obvious cases automatically, but wise enough to escalate the weird edge cases to humans who can actually think through novel problems
Making reliability a cultural priority: Treating reliability as a first-class engineering concern rather than something you retrofit later (which should be obvious but apparently isn't)
What's interesting here is the implicit acknowledgment that reliability engineering isn't fundamentally a technical problem - it's an organizational one where technology is just the implementation mechanism. You can have perfect monitoring and automated failover, but if your team culture doesn't prioritize reliability, you'll still find creative ways to break things.
⚡ Cachey: A Read-Through Cache That Actually Understands Object Storage
Object storage is fantastic until you need to read the same blob repeatedly, at which point you remember why caching was invented in the first place. Cachey is a new open-source read-through cache designed specifically for S3-compatible storage, and it has that refreshing quality of solving exactly one problem really well.
Hybrid memory-disk caching using the Foyer library: Intelligent about what goes where based on access patterns, so your frequently accessed data stays fast without your cache becoming a memory hog
S3-compatible everything: Works with any S3-like storage and includes a /fetch API for pre-signed URLs (which is exactly how you'd want to integrate this into existing workflows)
Built for immutable blobs: Acknowledges that most object storage use cases involve data that doesn't change, so the cache can be much more aggressive about retention
This falls into that category of tools that make you think "why didn't this exist already?" The answer, of course, is that building good caching is harder than it looks, but when someone gets it right, it feels obvious in retrospect.
🤖 Building RAG Systems at Enterprise Scale: The Brutal Realities Nobody Discusses
This Reddit thread is one of those refreshingly honest discussions that cuts through the RAG evangelism to talk about what actually happens when you try to implement RAG in banking, pharma, and legal environments. Spoiler: it's considerably messier than the conference talks suggest.
OCR noise is the silent productivity killer: Real enterprise documents aren't clean markdown files - they're scanned PDFs with inconsistent formatting that systematically destroys your chunking strategies
Metadata becomes mission-critical: Enterprise documents have complex relationships and hierarchical structures that simple vector similarity completely misses (which explains why your retrieval quality is mysteriously terrible)
Domain-specific chunking strategies are non-negotiable: Generic text splitting fails spectacularly when dealing with legal contracts or financial reports that have meaningful structural boundaries
The honest truth about RAG in production environments is that roughly 80% of your engineering effort goes into data quality and preprocessing work, not the sophisticated ML components that get all the conference attention. It's unglamorous work, but it's the difference between a demo and a system that actually works.
🔧 Open-Source Ingestion Tools in 2025: What's Actually Working According to Practitioners
This community discussion reveals fascinating patterns about which open-source ingestion tools are winning in practice. The responses tell a story about how the data tooling landscape has matured (and where it definitely hasn't).
dlthub and DLT are gaining serious momentum: Simple, well-documented tools are consistently beating feature-heavy platforms because most teams just want reliable data movement without vendor complications
DuckDB keeps appearing in unexpected contexts: Its combination of performance and operational simplicity makes it a compelling choice for transform-heavy ingestion workflows
Direct database integration remains surprisingly popular: Many teams are bypassing specialized ingestion tools entirely and going straight to PostgreSQL (which says something interesting about complexity creep in data tooling)
The pattern emerging here is that teams are gravitating toward boring, reliable solutions over exciting, complex ones - which is probably a healthy sign that the industry is maturing beyond the "let's rebuild everything" phase.
💡 e6data AI Analyst Early Access
We're launching early access to e6data AI Analyst, and honestly, it's about time someone built a data querying interface that actually understands how humans think about data. Ask questions exactly as they occur to you, get contextual multi-turn conversations, and for once, actually enjoy the follow-up process.
95%+ accuracy on enterprise workloads: Because "pretty good" genuinely isn't good enough when you're dealing with 1000+ tables and the kind of complex relationships that make SQL joins look like abstract art
Multi-turn conversations that make sense: Your data can finally talk back in a way that doesn't require translating human curiosity into rigid query syntax
Zero migration headaches: Works with your existing data platform because we know exactly how much "fun" those migration projects actually are
→ Get early access here
Community & Events:
The team published a new technical read on German Strings: The 16-Byte Secret to Faster Analytics!
We are hosting the next Lakehouse Days with Bengaluru Streams on data streaming, lakehouse architecture, and the future of real-time analytics this 27th September.
You'll also find us at: Big Data London (24-25 Sep, 2025)
AND we're hunting for data engineers who get excited about AI and aren't afraid to build the future.