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03 / REAL-TIME-ANALYTICS

Big Data

Real-time AnalyticsBig Data — Decide while events happen, not the day after.

Decide while events happen, not the day after.

Real-time analytics systems for operational use cases: fraud detection, dynamic pricing, industrial monitoring, personalisation. End-to-end streaming with sub-second latency.

<100ms

Typical p99 latency

1M+

Events per second

exactly-once

Processing guarantees

§ A

Overview

Batch analytics answers 'what happened'. Real-time analytics answers 'what is happening' and enables immediate reactions. We build event-driven pipelines that process millions of events per second.

We combine stream processing engines (Flink, Spark Streaming, ksqlDB), real-time OLAP databases (ClickHouse, Druid, Pinot) and a serving layer for live dashboards and operational APIs. Kappa or Lambda architecture depending on requirements.

§ B

What's included

  • Event model design and schema contracts (Avro, Protobuf)
  • Message broker setup (Kafka, Pulsar, Redpanda)
  • Stream processing with stateful aggregations and windowing
  • Real-time OLAP for analytical queries on fresh data
  • Live dashboards (Grafana, Superset, custom)
  • Alerting and automatic actions
  • Replay and backfill from storage

§ C

Deliverables

What you get at the end — or along the way — of an engagement on Real-time Analytics.

  1. D/01Documented streaming topology
  2. D/02Schema registry and versioned contracts
  3. D/03Tested and idempotent processing jobs
  4. D/04Real-time dashboard
  5. D/05SLAs and operational alerting

§ D

Use cases

Fraud detection

Transaction scoring in <100ms with real-time feature engineering.

Dynamic pricing

Price and availability updates based on demand, stock, competition.

Manufacturing & IoT

Sensor monitoring, OEE, predictive maintenance.

Personalisation

Recommendations and content adapted to the user's current behaviour.

§ E

Our process

01

Use case design

Latency SLA, throughput, exactness (exactly-once?) definition.
02

Event modeling

Schemas, keys, partitioning strategy.
03

Build

Pipeline implementation with tests on historical data.
04

Hardening

Resilience, backfill, late-event handling.
05

Run

Operation with lag, throughput and error monitoring.

§ F

Technologies

Apache Kafka · RedpandaApache Flink · Spark StreamingksqlDB · MaterializeClickHouse · Apache Druid · PinotSchema RegistryGrafana · Superset

Indicative stack. We adapt choices to your context, internal skills and existing constraints.

§ G

Frequently asked questions

Q/01Do I really need real-time?+

Often not. We assess whether near-real-time (1–5 min) is enough: it costs a fraction and covers many use cases.

Q/02Kafka or alternatives?+

Kafka remains the standard. Redpanda works well for smaller clusters; managed cloud (Confluent, MSK) for those who don't want to run it.

Q/03How much does it cost?+

PoC €30–50k. High-throughput production systems €100–300k + cloud run.

Other capabilities — Big Data

Data Engineering

Pipelines that handle petabytes and teams that keep them running.

Data Governance

Know what you have, where it lives, who uses it, and why.

BI Dashboards

Clear insights, not slides packed with charts.

Next step

Let's talk about real-time analytics.

A 30-minute call to understand your context and whether we can really help. No commitment.

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