Data Engineering
Kafka, Flink, Spark. Real-time pipelines, CDC ingestion, feature stores, and production data infrastructure that feeds AI, analytics, and operational systems.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Real-Time Data Infrastructure
We build the data backbone that feeds AI systems, analytics, and operational products: CDC ingestion, streaming pipelines, feature stores, schema governance, and recovery paths.
Typical engagement starts when
- downstream AI, analytics, or operational systems are consuming data that is late, inconsistent, or hard to trust
- event volume, replay requirements, or schema change risk have pushed the team past what scheduled jobs can safely handle
- leadership wants the data layer treated as infrastructure with ownership, governance, and recovery paths instead of ad hoc glue
- a product launch, migration, or AI initiative is exposing missing streaming, CDC, or feature-serving capabilities
What We Build
| Capability | What We Deliver |
|---|---|
| Streaming pipelines | Apache Kafka with Kafka Streams and Kafka Connect for real-time event processing |
| Batch + streaming hybrid | Apache Flink and Spark for unified batch and streaming architectures |
| Data transformation | dbt models with testing, documentation, and lineage tracking |
| Feature stores | Redis and Feast-based feature serving for ML model inference |
Engineering Standards
| Standard | What It Protects |
|---|---|
| Delivery semantics matched to the workload | Prevents over-promising where source, sink, connector, or retry behavior changes delivery behavior |
| Schema evolution with Avro or Protobuf registries | Keeps producers and consumers from drifting silently |
| Automated data quality checks | Catches pipeline issues before they reach AI, analytics, or product layers |
| Infrastructure-as-code with Terraform | Makes the data platform repeatable and reviewable |
The important signal here is not just throughput. It is whether the pipeline can keep data trustworthy when schemas change, backfills happen, and downstream systems depend on the same event stream.
Common failure patterns we fix
- Kafka or streaming infrastructure introduced before the operating model, schema discipline, or ownership model was ready
- CDC and event pipelines that work in steady state but fail during backfills, replays, or schema evolution
- batch and streaming paths diverging into conflicting versions of the same business truth
- downstream AI and ML systems depending on freshness behavior the platform cannot actually support
- no observability around consumer lag, delivery behavior, or data quality until incidents reach the product layer
What you leave with
- a data architecture aligned to actual latency, replay, and reliability requirements instead of tool fashion
- ingestion, transformation, and serving paths with explicit ownership and production guardrails
- delivery semantics, schema governance, and recovery procedures documented well enough for the internal team to operate confidently
- a platform that can support AI, analytics, and operational workloads without fragile one-off pipelines
Best Fit
- Team already has multiple data sources, event streams, or operational systems that need one reliable backbone
- Product depends on low-latency events, CDC, feature freshness, or streaming analytics
- Organization needs schema governance, replayability, and production-grade ingestion discipline
- Engineering leadership wants the data layer treated as infrastructure, not as ad hoc glue code
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Low-latency event processing, high throughput, and strong delivery semantics are needed | Apache Kafka + Kafka Streams |
| Complex event processing, windowed aggregations, stateful joins | Apache Flink on Kafka |
| Large batch jobs, ML feature engineering, data lake processing | Apache Spark / PySpark + Delta Lake |
| CDC from legacy databases, ETL from SaaS APIs | Kafka Connect + dbt transformations |
| Real-time dashboards and low-latency OLAP on event streams | Apache Druid on Kafka |
| Data integration across heterogeneous sources, flow-based routing | Apache NiFi for ingestion layer |
Specialist Capabilities
| Capability | Focus |
|---|---|
| Apache Kafka Engineering | Real-time streaming, event-driven microservices, Schema Registry governance |
| Apache Flink Engineering | Stateful stream processing, CEP, exactly-once at scale |
| Apache Spark Engineering | Large-scale batch/streaming, PySpark, Delta Lake, Databricks |
| Apache NiFi Engineering | Data integration, flow-based programming, enterprise data routing |
| Apache Druid Engineering | Real-time OLAP, low-latency analytics, high-concurrency dashboards |
Deployments in this area
Real-time anomaly detection processing 2.4M events/day with 70% fewer false positives
How we built a real-time anomaly detection pipeline processing 2.4M events/day using Kafka, Isolation Forest, and foundation models. False positive rate reduced from 68% to under 20%.
Real-Time IoT Analytics Platform for Smart Agriculture
We built a real-time streaming analytics platform for an AgriTech startup, processing live GPS data from farming equipment to track field coverage, calculate equipment utilization, and deliver dynamic ETAs to mobile devices.
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