Business analytics solutions built to run in real-time

We architect and deploy data & analytics solutions for real-time environments, applying streaming where it creates a genuine advantage, so decisions can be made while opportunities still exist.

Real-time data challenges we help you solve

1 Knowing when real-time actually matters

The right architecture for the right decision

Some decisions genuinely need real-time data, while others don't. We help identify where streaming earns its complexity and where it doesn't.

2 Catching business performance issues early

Analytics that surface problems as they emerge

Event data gets analyzed as it arrives, giving operating teams a chance to respond before a small issue becomes a larger one.

3 Extending existing analytics into a streaming environment

Real-time capability added to what already works

Existing data & analytics infrastructure can be extended to handle streaming use cases, often without a full rebuild of what's already in place.

4 Scaling analytics as event volume grows

Architecture that holds up as activity increases

Streaming architecture gets designed to handle rising transaction and event volume, so performance holds steady as data requirements grow.

How we approach real-time analytics and streaming

Streaming where it’s warranted, not by default Streaming where it's warranted, not by default

Every engagement starts by evaluating whether a use case genuinely benefits from real-time processing before recommending the added complexity it requires.

Deep experience across the data stack Deep experience across the data stack

Real-time capability is layered onto the same data engineering and analytics expertise we apply to every project, not treated as a separate discipline.

Reliable under real operating conditions Reliable under real operating conditions

Streaming pipelines are built to handle out-of-order events and temporary outages, so real-time doesn't come at the cost of accuracy.

Built to extend, not just launch Built to extend, not just launch

Analytics architectures are designed with future event volume and new use cases in mind, avoiding unnecessary rebuilds as needs evolve.

The practices behind the outcomes

Real-Time Architecture Assessment

Existing systems and use cases get evaluated to determine where streaming adds genuine value.

Streaming Data Pipeline Design

Data pipelines are architected to process and move data continuously as it's generated.

Event-Driven Analytics

Business events are analyzed as they occur, connecting activity to immediate, actionable insight.

Anomaly & Fraud Detection

Unusual patterns are flagged in real time, catching issues before they escalate.

Operational & Monitoring Analytics

Live system and performance data are surfaced directly to the teams responsible for it.

Hybrid Batch & Streaming Integration

Real-time streams and historical batch data get combined into a single, coherent analytics layer.

Related Services

Explore these additional services from our Data & Analytics capability pillar.

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We implement governance frameworks that ensure compliance, quality, and trust.

arrow-forward Explore Data Governance

Built to pivot without losing velocity

We understand that in a high-growth business, pivots are inevitable. We’re built to absorb the impact and keep you moving forward.
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Business analytics consultants who have been there, done that

No engagement is one-size-fits-all. Our data experts adapt to your stage, your stack, and your team.

Case Studies

1. Fleet Battery Intelligence
Manufacturing & Industrial Services

Fleet battery intelligence customer portal and installation app

Our product teams turn field activity, asset data, and fleet metrics into customer-facing insight and installation control.
20
Multi-disciplinary delivery engineers
30k+
Fleet of trucks and batteries
2
Connected products for web & mobile

Ready to explore real-time analytics and streaming for your environment?

Connect with our global team of data & analytics experts to get started.

Relevant
Questions

The deciding factor is whether a delayed decision creates real cost or risk, like fraud detection or equipment failure. If a daily or hourly refresh wouldn't change the outcome, batch processing is usually simpler and cheaper to maintain.

Streaming systems require continuous compute resources and more complex infrastructure to manage state and fault tolerance, which raises both build and operating costs. Batch processing runs on a schedule and can scale compute up only when needed, making it considerably less expensive for use cases that don't require immediacy.

 

Redundancy at the message broker level, combined with dead-letter queues for failed events and clear fallback behavior, prevents a pipeline outage from silently dropping data. Testing failure scenarios before launch matters more here than in batch systems, since there's no next scheduled run to catch up.

Change data capture tools can feed warehouse tables incrementally as source data changes, avoiding the need to re-architect the entire warehouse. This approach works well when the goal is fresher data rather than sub-second processing.

Bottlenecks usually stem from under-partitioned topics, processing logic that can't scale horizontally, or downstream systems that can't absorb the write throughput. Diagnosing which stage is the actual constraint matters more than simply adding more compute across the board.

Consumer lag, end-to-end latency, and error rates matter more than uptime alone, since a pipeline can be technically running while falling further behind in real time. Alerting on lag trends catches degradation before it becomes a visible outage.