Real-time data analytics: act on events while they still matter

Some decisions cannot wait for last night's load: a stock-out, a failed payment run, a machine running hot, a sudden drop in orders. We build streaming pipelines that capture changes as they happen in your databases, applications and devices, and turn them into live dashboards, alerts and automated actions.

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What we cover

When it is worth itNot every report needs to be live
Change data captureCapture changes without slowing the source
StreamingPipelines that process events as they arrive
From insight to actionDashboards, alerts and automated responses
ReliabilityBuilt to keep running when something breaks

When it is worth it

Not every report needs to be live

Real-time analytics processes data as soon as it arrives rather than in a nightly batch, so people and systems can respond in seconds or minutes. It costs more to build and run than a scheduled load, so we start by sorting your use cases: which ones lose value by the hour, and which ones are fine with yesterday's numbers. Monitoring operations, catching errors and fraud, tracking stock and orders, and watching equipment usually earn a live feed. Monthly finance reporting does not.

What we do

Use cases sorted by how fast they need data
A latency target agreed for each one
Batch wherever batch is enough
A cost estimate before the build

Change data capture

Capture changes without slowing the source

The fastest way to get data out of a business database is to read its changes, not to query it again and again. Change data capture reads each insert, update and delete from the database log and passes it on in order, with little extra load on the system people are working in. We use Oracle GoldenGate, AWS Database Migration Service and similar tools for this: the same replication technology our database team uses to keep two databases in sync during a zero-downtime migration.

What we do

Change data capture from Oracle, SQL Server, PostgreSQL and MySQL
Oracle GoldenGate and AWS Database Migration Service
Events from applications and APIs
Device and sensor readings

Streaming

Pipelines that process events as they arrive

Captured events flow into a streaming platform, such as Apache Kafka or the streaming services in Azure, AWS and Microsoft Fabric, where they are buffered, ordered and kept for replay. Stream processing then filters, joins and aggregates them: a running total of orders by region, the average temperature of each machine over the last few minutes, a payment that does not match its invoice. The results land in a live dashboard, in a database that applications read, or in the warehouse, where they join the historical data.

What we do

Apache Kafka and cloud streaming services
Filtering, joining and windowed aggregation
Replay when a downstream system was down
Results written to dashboards, databases and the warehouse

From insight to action

Dashboards, alerts and automated responses

A live number on a screen helps only if someone is looking. So beside the dashboards in Power BI, we set thresholds that alert the person who owns the problem, in Teams or by email, and, where the response is routine, automate it with Power Automate or a cloud function: hold an order, open a ticket, notify a supplier. A person stays in the loop for the decisions that need judgment.

What we do

Live Power BI dashboards
Alerts to a named owner in Teams or by email
Automated responses with Power Automate and Azure Functions
A log of every alert and action

Reliability

Built to keep running when something breaks

A streaming pipeline runs all the time, so its failures have to be handled by design. We make each step safe to retry, keep events until every consumer has processed them, and monitor the lag, so a slow consumer is noticed before the dashboard falls behind. A schema change in a source system is detected rather than silently breaking the feed, and data that arrives late or out of order is handled instead of counted twice.

What we do

Monitoring of lag and throughput
Retries without duplicates
Detection of schema changes
Runbooks for the team that supports it

Case studies

Real-time analytics in practice

Client names are withheld. We are happy to share details on a call.

All case studies

Delivered
30 TB, zero downtime
Financial services
Data Analytics
Zero-downtime move of a 30 TB Oracle database for a global retail bank
The bank's transaction database served thousands of users around the clock. We built the new Oracle 12c RAC platform on IBM AIX alongside the old Solaris system, kept the two in sync with Oracle GoldenGate, tested everything and switched over with no downtime and no data loss.
- 30 TB moved with zero downtime
- Zero data loss at cut-over
Read the case study →

Questions

Common questions

What is real-time data analytics?

It is the analysis of data as it is created, rather than after a scheduled load. Changes in databases, application events and device readings are captured continuously, processed in a stream, and shown in dashboards or used to trigger alerts and actions within seconds or minutes.

Will capturing changes slow down our production database?

Change data capture reads the database's own log of changes instead of running queries against its tables, so the extra load is small. We measure it on your system during the pilot, before anything goes live.

Can real-time data go into our existing data warehouse?

Yes. Streamed data can be written to the warehouse continuously or in small batches, so the live dashboards and the historical reports use the same data and the same definitions.

Do we need Apache Kafka?

Not always. Kafka suits high volumes and many consumers. For a few feeds, the managed streaming and integration services in Azure, AWS or Microsoft Fabric are often simpler to run. We recommend the smallest setup that meets your latency and volume needs.

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