Yesterday’s architectures can’t solve today’s real-time demands
Enterprises have never had more data, tools or architectural options. Yet for many, dashboards still lag, AI assistants work with outdated context and customer experiences stall.
The reason is simple: most modern data architectures were built for batch analytics — not for real-time experiences.
We started with data warehouses, structured and governed for BI and reporting. Then came data lakes, offering low-cost storage and flexibility for machine learning and semi-structured data. Now, lakehouses are emerging as the latest evolution — bringing together the best of both worlds.
According to Gartner’s recent report, “Top Practices for Using Data Warehouses, Lakes, Lakehouses and Hubs”, organizations are increasingly combining multiple architectural patterns to meet complex analytics needs.
That’s a smart foundation — but here’s what’s missing:
The report barely touches on real-time performance.
There’s little discussion of streaming ingestion, low-latency querying, high concurrency or AI application serving — all critical for today’s digital experiences and intelligent systems.
Lakehouses are a powerful consolidation pattern, but they were not built for real time.
The evolution of data architecture
Architecture | What it solves | What it misses |
Data warehouse | Structured analytics and BI | Real-time ingestion, semi-structured data, high concurrency, scalability |
Data lake | Flexible storage for varied data types, scalability | Governance, speed, query performance, useability (poor SQL capabilities) |
Lakehouse | Unifies warehouse and lake capabilities | Real-time responsiveness, AI/ML inference, application integration |
Each of these has value — but none is built to deliver real-time data experiences at the level users and applications increasingly demand.
It’s also worth noting data hubs, which Gartner highlights as a way to centralize sharing and orchestration across systems. Hubs should not be seen as the next step in the architectural evolution, but rather as an adjacent pattern designed to improve connectivity and governance. While they can simplify data movement, they still don’t resolve the performance gap or enable true real-time experiences.
Gartner’s take on combining architectures
In their report, Gartner advises combining warehouses, lakes, lakehouses and hubs based on use cases:
Warehouses for governed BI
Lakes for flexible storage and ML
Lakehouses for unified analytics
Hubs for orchestration and governance
This is sensible advice for building an analytics foundation.
But there’s a missing layer in this blueprint: real-time enablement.
The ability to ingest streaming data, query it instantly and serve AI-powered applications with fresh, low-latency results is absent.
And that’s not just a gap — it’s a strategic blind spot. Worse, when data needs to move between these systems, performance degrades and complexity increases — often undermining the very agility these architectures were meant to enable.
Where prevailing architectures fall short
Even with lakehouses or unified data platforms, most architectures lack the ability to support:
Gap | Why it matters | What you experience | Business impact |
Real-time ingestion | Required for streaming and event-driven data | Laggy insights, outdated context, delayed customer experiences | Poor decisions, missed opportunities, slower time-to-market |
Low-latency queries | Needed for operational apps and AI responsiveness | Sluggish dashboards, poor UX, slow decision-making | Frustrated customers, lower productivity, competitive disadvantage |
High concurrency | Supports large-scale usage across teams and apps | Query delays, throttling, missed SLAs | Inability to scale apps, revenue loss during peak demand |
AI application serving | AI systems require fast inference and retrieval | LLMs return stale answers, poor recommendations in apps | Reduced trust in AI, poor adoption, lost customer confidence |
Multimodal data access | Real-world apps span structured, time series, vector, and semi-structured data | Complex stacks, brittle pipelines, higher operational costs | Rising infrastructure costs, slower innovation, fragile systems |
Most lakehouse implementations — including those from Databricks and Snowflake — are designed for batch analytics, not for speed at scale.
How SingleStore closes the real-time gap
SingleStore isn’t here to replace your warehouse or lakehouse. It’s designed to complete your architecture by delivering the missing performance layer — without compromising openness or scalability.
With Snowflake
Keep Snowflake as your system of record
Use SingleStore to serve real-time queries, dashboards and AI lookups
→ Business outcome: leaders make decisions with the freshest insights, not yesterday’s reports
With Databricks
Use Databricks for data science and model development
Serve inference workloads and AI outputs in real time with SingleStore
→ Business outcome: AI-driven experiences respond instantly, keeping customers engaged
With Apache Iceberg or Delta Lake
Store data in open formats
Query and operationalize it at speed using SingleStore’s high-performance engine
- Business outcome: gain the openness of Iceberg with the responsiveness of a low-latency engine
The result: A seamless bridge between data at rest and real-time intelligence — powering AI, applications and decisions with up-to-the-moment data.
Final thought: Lakehouses are not the final stop
Gartner’s guidance provides a strong foundation for modern data strategy. But it largely reflects a world optimized for batch processing, offline analytics and centralized reporting.
That’s no longer enough for today’s real-time, AI-driven landscape.
SingleStore fills the architectural blind spot — delivering real-time ingestion, millisecond query speeds and high-concurrency access on top of what you already have.
It’s not about replacing what you’ve built.It’s about delivering the real-time performance today’s applications demand.
