Real-Time Data Streaming
StepTo builds real-time data streaming solutions with Apache Kafka and Flink, high-throughput, event-driven pipelines for analytics and operations.
Reviewed by Igor Gazivoda, Co-founder & CEO of StepTo · Updated
StepTo builds real-time data streaming systems that move events through your infrastructure in seconds instead of overnight batches, powering fraud detection, live inventory, operational dashboards, and event-driven microservices. We design and operate streaming platforms built on Apache Kafka, Flink, and cloud-native services as part of our custom software development offering. Our Serbian engineering team, drawn from a market of 60,000+ IT professionals, delivers this specialised work at $35–75/hr for most delivery work, 40–60% below Western European or US data engineering rates. See our fintech payments case study for a real example of Apache Kafka in production, an event-sourced ledger that cut reconciliation time from 3 days to under 4 hours with zero missed or duplicate transactions.
What Real-Time Streaming Services Does StepTo Deliver?
- Event backbone design & build: Kafka (self-managed, MSK, Confluent Cloud, Redpanda), topic and partition strategy, schema registry, and multi-environment topology.
- Stream processing: Apache Flink and Kafka Streams jobs for enrichment, aggregation, windowing, and joins, fraud scoring, sessionisation, real-time KPIs.
- Change data capture (CDC): Debezium-based pipelines that stream database changes into analytics stores, caches, and search indexes without touching application code.
- Event-driven microservices: Decoupling monoliths with publish/subscribe patterns, transactional outbox, and saga-based workflows.
- Real-time analytics serving: Landing streams into ClickHouse, Apache Pinot, or Materialize to power sub-second dashboards and in-product analytics.
- IoT & telemetry ingestion: MQTT-to-Kafka bridges, device fleets, and backpressure-safe ingestion for sensor data at scale.
- Streaming platform audits: Review of existing Kafka estates, topic design, delivery semantics, consumer lag, and disaster-recovery posture, with a prioritised remediation plan.
Which Streaming Technologies Do We Work With?
- Brokers & logs: Apache Kafka, Redpanda, AWS Kinesis, Google Pub/Sub, Azure Event Hubs, NATS
- Processing: Apache Flink, Kafka Streams, Spark Structured Streaming
- CDC & connectors: Debezium, Kafka Connect, custom source/sink connectors
- Serving layers: ClickHouse, Apache Pinot, Materialize, Redis, Elasticsearch
- Governance: Confluent Schema Registry, Avro/Protobuf contracts, data lineage tooling
- Operations: Kubernetes, Terraform, Prometheus/Grafana lag and throughput monitoring
How Much Does a Real-Time Streaming Project Cost?
- First-pipeline project: One high-value flow end-to-end (source, processing, serving, monitoring), typically $30,000–80,000. Rates $35–75/hr for most delivery work; see pricing.
- Platform build: A governed event backbone with schema management and self-service onboarding for internal teams.
- Dedicated streaming team: From $13,500/month to build and operate event infrastructure as a product for your organisation.
- Staff augmentation: Kafka/Flink engineers embedded with your data team from $4,500/month per engineer.
Why Choose StepTo for Real-Time Streaming Systems?
- Semantics-first design: We decide ordering, delivery guarantees, and replay strategy up front, the choices that are expensive to retrofit later.
- Operations included: Every pipeline ships with lag dashboards, alerting thresholds, and runbooks; streaming systems fail loudly at 3 a.m. or not at all.
- Pragmatism about batch: We will tell you when a nightly job is the better answer, streaming should be earned by the use case.
- CET timezone and cost: Real-time overlap with EU teams, 3-4 hours with US East Coast, at 40–60% below Western rates.
How Does StepTo Deliver a Streaming Project?
- Use-case and volume analysis (1–2 weeks): Event sources, throughput and latency targets, consumers, and delivery-semantics requirements.
- Architecture: Broker choice (managed vs self-hosted), topic design, schema contracts, and failure-mode planning.
- First flow in production: One end-to-end pipeline with monitoring, delivered in 6–10 weeks.
- Platform hardening: Security (mTLS, ACLs), quotas, disaster recovery, and multi-team governance.
- Scale-out: Additional producers and consumers onboarded against documented standards.
What Do Data Engineers Ask About Streaming Platforms?
- How much does a real-time streaming project cost?
- A first streaming pipeline, for example, change-data-capture from an operational database into a real-time dashboard, typically costs $30,000–80,000 over 2–4 months. A production event backbone with multiple producers and consumers, stream processing, schema governance, and monitoring usually runs $80,000–250,000 over 4–9 months. StepTo engineers bill $35–75/hr for most delivery work, roughly 40–60% below Western European or US data consultancies.
- Should we run Kafka ourselves or use a managed service?
- For most teams a managed service, Confluent Cloud, AWS MSK, Redpanda Cloud, or cloud-native options like Kinesis and Pub/Sub, is the right call: you trade a licensing premium for not operating brokers, upgrades, and rebalancing yourself. Self-managed Kafka makes sense at sustained high volumes where managed pricing dominates, or under strict data-residency rules. We help you model the cost crossover for your actual throughput before choosing, and we build so the application layer is portable either way.
- When is streaming worth it versus batch processing?
- Streaming earns its complexity when the value of data decays in minutes: fraud checks, inventory and pricing sync, operational alerting, live personalisation, logistics tracking. If your consumers act on data hourly or daily, well-built batch ETL is cheaper to run and debug. Many of our engagements end up hybrid, a streaming backbone for the few genuinely time-critical flows, batch for everything else.
- How do you guarantee data is not lost or processed twice?
- Delivery semantics are an architecture decision, not a default. We use Kafka transactions and idempotent producers for effectively-once processing where it matters, consumer-side idempotency keys and dead-letter queues elsewhere, and schema registries (Avro/Protobuf) so producers cannot silently break consumers. Every pipeline ships with lag monitoring, alerting, and replay procedures documented in runbooks.
- Can you take over or stabilise an existing Kafka setup?
- Yes. A common engagement is a streaming-platform audit: we review topic design, partitioning, consumer-group health, schema management, and failure handling, then fix the highest-risk issues. Ongoing operation is available via staff augmentation from $4,500/month per engineer, or a dedicated platform team from $13,500/month for larger event-driven estates.