Why SingleStore Is a High-Performance Analytics Database for Financial Services

When a delay derails the meeting

The first time I saw it clearly was in what should have been a routine portfolio review.

We were walking through a dashboard. The numbers looked fine. Then someone asked what sounded like an easy follow-up: Can we slice that by region and overlay the last two volatility spikes?

The analyst added a couple of filters and hit apply. The chart disappeared, replaced by a spinner. A few seconds went by. Nobody said anything, but the mood in the room started to shift. Instead of focusing on follow-up questions, new questions emerged: Is this data current? How close to real time is this? Are we looking at, and about to act on the wrong picture?

That is the real cost of slow analytics in financial services. More than just a performance metric, it's the point where people lose confidence in the insights and begin to hedge their decisions.

This is why I wince a little when I hear “our warehouse is fast.” It might be fast in a single-user scenario, but it doesn’t mean it will feel fast when an entire desk, product line, or executive team is poking at dashboards at the same time.

In practice, modernization projects start because the business needs answers quickly, under real-world load, and the current platform cannot keep up.

And the pressure to operate this way is only going up. Financial institutions are under increasing pressure to move from retrospective reporting to real-time decision-making. As highlighted in Deloitte’s analysis of finance leadership trends, finance teams are playing a growing role in shaping strategy and leveraging technologies like cloud, AI, and advanced analytics to drive business outcomes. For banks and capital markets firms, this shift raises the bar for data infrastructure: they must be able to query large volumes of live data, support high concurrency, and deliver insights instantly as events unfold.

Analytics is now a live system

If you rewind ten or fifteen years, a lot of analytics in banks and capital markets ran on rails. Data landed overnight, ETL pipelines did their work, and the business consumed finance reports and financial statements the next morning. The questions that arose were generally suggestions to address the topic further in a subsequent meeting.

Whilst this still happens, expectations have changed dramatically.

Advisory teams, risk managers, product leads, and executives now demand the ability to explore data live. They need to be able to click into a dashboard during a client call, pivot a view mid-meeting, and drill into a sudden spike instantly, rather than waiting for a follow-up data pack.

Modern Business Intelligence (BI) tools increasingly favor patterns like direct querying to eliminate scattered extracts and duplicated datasets. In this model, every interaction with a dashboard triggers queries against the underlying data source often powering real-time financial analytics and interactive financial reporting experiences. Microsoft’s own guidance for Power BI DirectQuery highlights that each visual or interaction can generate queries sent directly to the database, meaning overall user experience depends heavily on the performance and scalability of the underlying system. Under light usage this works well, but as the number of users, visuals, and queries grows, the load placed on the database increases rapidly, reinforcing the need for a high-performance analytics platform.

For most financial institutions, the same analytics surface ends up serving very different audiences at once:

• Central data teams maintain shared models and enterprise reports.

• Risk and portfolio analysts run heavier, exploratory queries.

• Product and app teams embed charts and KPIs into client-facing experiences.

• Operations teams watch live signals and alerts.

When the dashboard slows down, most people complain about the dashboard itself. The real problem is usually below the waterline. The platform was never designed for requirements that combine high concurrency and data freshness.

Event-driven data changed expectations

At the same time, the way data flows through financial organisations has changed.

Many teams are now using event-driven designs because they are a practical way to keep systems loosely coupled and responsive. Apache Kafka is often used here, carrying everything from payments, orders, and trades through to customer interactions and platform telemetry.

Change data capture (CDC) approaches the same concept from a different perspective. Instead of waiting for an overnight batch, CDC streams changes from operational databases into downstream systems as they happen. Cloud providers increasingly recommend this as a foundation for real-time analytics and financial analytics, because it reduces the “it happened” versus “we can see it” gap.

Once these patterns were implemented, something else happened: the business learned to expect the rest of the stack to behave in a more real‑time way as well.

New excuses or explanations start to appear. It is accurate as of the last refresh. It is near real time depending on the pipeline. It is real time, but only after it lands in the serving layer. These statements do not instill confidence when you are about to adjust limits, rebalance exposure, or show a client their current position.

This is usually the point where leaders stop asking: How do we make the warehouse a bit faster? Instead, the question turns into: Have we actually built for fresh, interactive analytics at scale?

Why existing stacks struggle under financial-scale loads

To be fair, the issue isn’t really that a specific technology is bad. A lot of the platforms in use today are very good for what they were built to do. And this is the real challenge, they were not built to sustain high concurrency analytics on fresh data.

The pain manifests itself as a result of usual patterns:

• Batch-first designs with unavoidable latency, even when you shrink the window as much as you can.

• Strict separation of OLTP and OLAP that forces data through several hops before it is ready for analytics.

• A focus on raw throughput more than on predictable latency when hundreds or thousands of users are active.

Financial workloads never behave in neat, even lines; they arrive in waves—at market open, during a volatility spike, at month-end, following a regulatory announcement, or when a steering committee opens the same set of dashboards five minutes before a meeting starts.

When the platform cannot cope with this wave-like demand, people compensate by introducing pre-aggregations and summary tables specifically for certain dashboards. They add caches and special pipelines for anything that needs to look real-time, sometimes routing entire use cases to separate systems.

Over time, these workarounds become the de facto architecture.

This leads to complexity. It also shapes behaviour. Teams prepare screenshots instead of live demos because they do not trust the dashboard to load in time. Risk managers run key queries in off‑hours, then paste numbers into slides. Product teams hold back on more ambitious analytics experiences because they are not confident the back end will hold up.

A more useful definition of high-performance for finance

This is why I tend to unpack the word “fast” whenever it comes up.

If a platform has an impressive average query time for one or two test users, that is interesting but not decisive. For financial services, a more relevant definition of high performance looks something like this:

• Latency is low for the actual queries people run, not only for demos.

• Concurrency is handled gracefully, including peak bursts.

• Performance is predictable. The occasional ten-second response times, which could interrupt an important call, have been eliminated.

• Freshness is built in. Access to the most recent data should be seamless and not dependent on complex, custom-built data pathways.

This definition lines up with how a lot of financial institutions are rethinking analytics and data platforms more broadly. Real-time and operational analytics are moving from “innovation projects” into the centre of day‑to‑day decision-making.

It also gives different stakeholders a common framework. Product owners can map those properties to user experience. Risk leaders can map them to control and timeliness. Data leaders can map them to architecture and operations.

Where SingleStore fits in that picture

Against that backdrop, it is easier to explain where SingleStore sits.

SingleStore is a distributed SQL database that combines high‑throughput ingestion with low‑latency analytics at scale. It is designed to handle streaming and batch data, support both transactional and analytical workloads, and stay responsive when many users are active.

From an architectural point of view, the aim is simple: make it possible to query fresh data without pushing it through several layers of transformation and movement first, enabling faster financial analytics and more responsive financial reporting. That is why SingleStore focuses on ingesting streams, handling fast writes, and serving analytical queries from the same engine.

If you want a technical deep dive on real-time analytics, the product documentation is the best place to start. 

If you prefer to think in terms of outcomes, I wrote an article not too long ago, laying out the design principles to build a real-time data warehouse. This guidance aligns closely with what many financial services teams are trying to do: shorten batch windows, reduce the number of copies, and keep dashboards interactive even when usage spikes.

When I engage with customers, we typically start with high-value use cases such as real-time portfolio and risk dashboards, fraud and anomaly detection, and embedded analytics in digital banking and wealth experiences. These all have something in common: data changes quickly, users interact live, and the platform has to handle that combination cleanly.

Since SingleStore was built with performance under concurrency on fresh data as a first principle, make sure to test us alongside the other vendors you’re considering.

What actual deployments tell you that diagrams cannot

Architecture diagrams are useful. They get you excited and thinking, but they do not paint a whole story.

What tends to change minds are real deployments. SingleStore has published, among others, a case study with an America‑based Tier 1 bank that needed to modernise away from mainframe‑centric patterns and improve responsiveness for large, critical workloads. They used SingleStore as part of a broader effort to move key analytics onto a more flexible, real-time-friendly foundation.

Another example is a Fortune 25 financial services firm that uses SingleStore to power high‑concurrency, low‑latency experiences for wealth and investment customers and advisors. The emphasis there is not only on how fast individual queries run, but on how consistently the platform behaves when lots of people are exploring portfolios at once.

These stories resonate because they mirror the situations many teams find themselves in. The tools work fine in quiet periods and struggle when the business actually needs them most. That’s when you really find out whether your high‑performance database is built for the job.

How to test platforms without turning it into a saga

There’s no need for a big‑bang initiative to figure out what works. In fact, the best evaluations are usually tightly scoped.

Pick one workload where responsiveness obviously changes behaviour. A few examples:

• A portfolio dashboard that advisors use live with clients.

• An exposure or liquidity view that risk teams rely on during volatile markets.

• A client-facing analytics feature where lag is directly visible to customers.

Then design the evaluation so it looks and feels like production:

• Simulate the kind of concurrency you actually expect, including spikes.

• Feed data in the way you intend to run long term, whether that means streams, CDC, micro‑batches, or a mix.

• Use real dashboards, financial reports, and complex queries, not just synthetic tests.

• Measure behaviour over a period that includes busy times.

When evaluating your options, the focus should be on understanding how each platform performs under your specific circumstances.

Consider including SingleStore in your evaluation alongside your current systems and any other platforms you are considering. A positive result provides a concrete justification for incorporating it into your future plans. Conversely, if another platform is a better fit, you still gain a clearer understanding of the performance criteria you need.

Bringing it back to the decision

Financial services analytics is moving towards shorter decision loops, more interactive exploration, and more always‑on data experiences. 

That direction seems stable, regardless of whether it’s labelled as real‑time analytics, operational analytics, HTAP, or something else.

There is no shortage of strong technology in the market. The high-performance differentiator, in my view, is how well a platform handles fresh data under real concurrency, without needing to add extra caches, extracts, and special cases.

If you are in the middle of an analytics or data platform rethink, I would encourage you to evaluate SingleStore alongside your other traditional contenders. 

Review the materials shared earlier in the post first, then run one focused test that reflects your real concurrency and freshness requirements. 

I’m confident the results will speak for themselves!