Data Observability for Real-Time Data Processing
We design, build, and operate production-grade data observability for streaming pipelines, covering freshness, volume, schema, lineage, and latency SLOs across Kafka, Flink, Spark Structured Streaming, and cloud-native services. Your teams get real-time visibility into data health, so analytics, ML, and customer-facing systems stop breaking silently.
Production-ready streaming pipelines with built-in monitoring, lineage, and SLO-based alerting from day one.
- Event-driven ingestion on AWS, GCP, or Azure with Kafka, Kinesis, or Pub/Sub
- Stream processing with Flink, Spark Structured Streaming, and Beam
- Freshness, volume, schema-drift, and distribution checks across every stream
- End-to-end lineage and SLO-based alerting for real time data monitoring
- 6-8 weeks from source access to an observable streaming data pipeline in production
Why Do Real-Time Data Pipelines Fail Silently in Production?
Most organisations already run streaming workloads, yet learn about broken pipelines from angry business users, not from monitoring. The cause is rarely the engine. It is the missing observability standards, lineage, schema contracts, and SLOs across the real-time data processing stack. When a topic stalls or a schema shifts, downstream dashboards, ML models, and customer journeys degrade without warning.
Architecture & Technical Building Blocks
Event-driven pipelines on Kafka, Kinesis, or Pub/Sub with stream processors like Flink and Spark Structured Streaming. Each pipeline ships with schema contracts, dead-letter queues, exactly-once semantics where required, and defined latency and freshness SLOs.
Freshness, volume, schema, distribution, and lineage checks across every stream and sink. Observability agents instrument topics, processors, and tables so real time data monitoring is continuous, with alerting tied to business SLOs instead of infrastructure noise.
The streaming backbone behind a real-time customer data platform: event collection, identity resolution, feature computation, and activation into marketing, product, and support tools. This enables real-time customer journey orchestration with sub-second decisioning.
Streaming data integrated with warehouses and lakehouses (Snowflake, BigQuery, Databricks) for real time big data analytics, combining hot paths for dashboards with cold paths for historical models. Integration is governed via contracts, CDC patterns, and validated connectors.
SLIs/SLOs for latency, freshness, and completeness per pipeline, wired into dashboards with alerting connected to on-call rotations. Teams get a clear contract with business stakeholders on what "real time" actually means for each use case.
How It Works
We map source systems, event volumes, latency targets, compliance constraints, and consumer use cases. Output: target architecture, SLIs/SLOs per stream, and observability blueprint. (1-2 weeks)
We provision brokers, stream processors, schema registry, and the observability layer on your cloud. Output: running platform with base monitoring, lineage, and CI/CD for streaming jobs. (2-3 weeks)
We implement the first production pipelines with schema contracts, transformations, DLQs, and freshness checks. Output: governed streaming data flowing to warehouse, CDP, or analytics tools. (2-3 weeks)
We cut over the first business use case, whether real-time dashboards, CDP activation, or ML features, under defined SLOs. Output: live pipeline with observability, alerting, and runbooks. (6-8 weeks total)
We provide SLA-based support, onboard new streams, tune performance, and transfer ownership. Output: a platform with documented standards and enabled internal teams.
Business Impact
70-90% faster incident detection as freshness and schema checks catch issues before business users do.
50-80% reduction in data downtime across streaming pipelines feeding analytics and ML.
30-50% lower integration cost via standardised real time data integration tools and contracts.
6-8 weeks from engagement to the first observable streaming pipeline in production.
Who This Service Is For
Streaming Observability & Real-Time Data Engineering
We cover the full streaming stack: pipeline engineering, observability, customer data platform enablement, big data analytics integration, and platform reliability with measurable SLOs.
Frequently Asked Questions
Data observability is the continuous measurement of data health, covering freshness, volume, schema, distribution, and lineage across streaming pipelines. In real-time data processing it detects stalled topics, schema drift, or missing events within seconds, before they corrupt downstream analytics, ML models, or customer-facing experiences.
Real time data monitoring focuses on the data itself, not the servers. Traditional monitoring tells you a broker is up; data observability tells you that events arrive on time, with the expected schema, volume, and distribution. You need both, but only data observability catches silent data quality failures.
It depends on workload and cloud. For most clients we deploy Kafka or Kinesis as the event broker, Flink or Spark Structured Streaming for stateful processing, a schema registry for contracts, and a dedicated observability layer. Sinks usually include a warehouse (Snowflake, BigQuery, Databricks) and operational stores like a CDP or feature store.
Yes. We build real-time customer data platform capabilities on top of your existing cloud and event infrastructure rather than forcing a vendor swap. We add identity resolution, event unification, segment computation, and activation connectors, with observability and lineage across the full flow.
Typically 6-8 weeks from source access to the first production streaming pipeline with SLOs, observability, and at least one activated use case. Complex enterprise integrations or strict compliance environments can extend this to 10-12 weeks.
We work with Kafka Connect, Debezium, Fivetran HVR, Confluent, Estuary, Striim, and native cloud services (Kinesis, Pub/Sub, Event Hubs). Tool choice follows the architecture, not the other way around. We select integration tools that fit your latency, compliance, and operating model.
Yes. We implement dual-path architectures where the hot path powers sub-second dashboards, alerting, and activation, while the cold path lands governed data in the warehouse or lakehouse for historical analytics and ML training, with consistent schemas across both.
Ready to Make Your Streaming Data Observable?
Book a 30-minute, no-obligation technical session. We'll review your current streaming data pipeline architecture, find the top observability and reliability gaps, and outline a 6-8 week path to a production-grade, observable real-time data processing platform.
Discovery call
A 30-minute technical session to review your current streaming architecture and reliability gaps.
Observability review
We map your top freshness, schema, and latency gaps against business SLOs.
Roadmap
You get a 6-8 week path to a production-grade, observable real-time data processing platform.