Security Data Lakes: Rise and (Possible) Fall?
Why schema‑on‑read looked like magic, what governance headaches followed, and how pipeline‑fed lakes might evolve by 2030
An Overnight Sensation. Fifteen Years in the Making.
Every security team eventually hits the same wall: the SIEM bill grows faster than the budget, yet retention windows shrink. Schema‑on‑read data lakes promised a loophole, dump everything first, decide the schema later. When Netflix rolled out its “SOCless Detection” model in 2018, the idea felt punk‑rock: ditch the SIEM console, land raw logs in a lake, and wire micro‑services around it.
Since then, interest has spiked. Analyst estimates put current enterprise adoption around 5 percent, projected to double by 2028. That growth is powered by two forces:
Cloud economics: object storage is cheap and (mostly) limitless.
Big‑picture analytics: threat hunting, attack reconstruction, AI, ML feature stores, tasks a SIEM was never built for.
Why Schema‑on‑Read Won the First Round
Any data, any format: No need to pre-normalize 500+ log types before ingest, which makes life easier for fast-moving DevOps teams.
Long-term retention: Lets you replay months or even years of telemetry to hunt down “slow-burn” breaches.
Data-science ready: You can feed notebooks, ML pipelines, or GenAI copilots directly, without first exporting data from your SIEM.
Vendor-agnostic: CrowdStrike, Palo Alto, AWS, and home-grown logs can all live side-by-side in one place.
In short, the lake flipped the SIEM model from schema‑on‑write (parse first, pay forever) to schema‑on‑read (store cheap, pay later). Security engineers loved the flexibility; finance teams loved the initial cost curve.
Governance: The Hangover After the Hype
But once the honeymoon ended, three governance gremlins appeared:
Data Swamp Risk: Without strict lifecycle policies, lakes balloon into petabyte‑scale junk collections. Duplicate records and stale backups inflate cloud bills and slow queries.
Access & Residency: Sensitive logs often cross regions or land in multi‑tenant buckets, colliding with GDPR and industry regs.
Talent Gap: Running Spark jobs at 2 a.m. isn’t a traditional SOC skill. Teams end up hiring data engineers on top of analysts, erasing the original cost savings.
Add classic data‑quality issues, missing timestamps, inconsistent hostnames, and your “single source of truth” starts to resemble a single point of confusion.
Enter the Telemetry Pipeline
Security telemetry pipelines are emerging as a middle layer that curates data before it touches the lake: deduplication, enrichment, severity tagging, routing. Think of pipelines as an industrial refinery feeding a reservoir. Benefits include:
Cost governance: Strip junk events upstream; pay the lake to store only signals.
Quality‑in, quality‑out: Normalized logs mean fewer ad‑hoc parse functions in every notebook.
Selective forwarding: Send high‑value data to SIEM or XDR in real time, archive the rest.
SIEM vendors have noticed, baking pipeline features into their own stacks (Splunk, Exabeam, etc.).
Looking to 2030
1️⃣ Lake‑House Convergence: Delta/Iceberg‑style formats add ACID & fine‑grained governance.
Win: data engineers. Risk: license sprawl.
Lake-House Convergence represents the most technically ambitious path, where data lakes gain the reliability and governance features traditionally associated with data warehouses. The integration of ACID transactions through formats like Delta and Apache Iceberg could finally deliver on the promise of having both the flexibility of a lake and the reliability of a warehouse. Data engineers would benefit from reduced complexity in managing multiple systems, while ML teams would gain more reliable data pipelines. However, the warning about tool proliferation and license sprawl highlights a key risk - organizations might end up with an even more complex ecosystem as they try to integrate these capabilities.
2️⃣ Pipeline‑First SecOps: Curated streams power real‑time detection while the raw lake becomes cold archive.
Win: cost‑sensitive shops. Risk: false sense you can ditch the SIEM.
Pipeline-First SecOps takes a more pragmatic approach, focusing on cost optimization by treating raw data lakes as cold storage while processing happens in lightweight, purpose-built query layers. This scenario particularly appeals to cost-conscious organizations dealing with ever-growing data volumes. The curated streams approach could significantly reduce infrastructure costs and improve query performance. The cautionary note about organizations thinking they can eliminate their SIEM systems is particularly insightful - it suggests that while this approach may reduce costs, it could create security blind spots if not implemented thoughtfully.
3️⃣ Federated Mesh: Query engines span multiple regional lakes, keeping data local but logically unified.
Win: global, regulated orgs. Risk: complex policy management.
Federated Mesh addresses the increasingly important challenge of data residency and regulatory compliance in a globalized world. By keeping data geographically distributed but logically unified, this approach could enable multinational organizations to comply with various data sovereignty requirements while maintaining analytical capabilities. The complexity of policy management across multiple jurisdictions and systems would be the primary challenge here.
PREDICTION: By 2030 most SOCs will treat the raw lake as tape‑backup‑with‑search, while real‑time detection runs on trimmed, well‑labeled streams.
Questions to Ask Before You Dive Into Your Lake
Retention vs. Recall: How often do you actually need two‑year‑old DNS logs?
Data Lineage: Can you prove which parser touched a record before it landed in a dashboard?
Exit Strategy: If the lake vendor raises prices, can you migrate without weeks of re‑hydration?
Human Capital: Do you have (or can you hire) cloud data engineers on a 24/7 rota?
Final Words
Security data lakes solved one set of pains, exploding SIEM costs and short retention spans, only to introduce new ones around governance, talent, and complexity. Pipelines, lake‑houses, and mesh architectures are already nibbling at the edges. Treat the lake as a capability, not a destination: pair it with disciplined curation, clear ownership, and realistic ROI metrics. Otherwise, the future fall may come faster than the rise.
The Cyber Futurists is a boutique Cybersecurity Advisory firm led by former Gartner Research Director Oliver Rochford.