KafkaFlinkSparkdbt

Data Engineering

Kafka, Flink, Spark. Real-time pipelines processing millions of events per day with exactly-once semantics. We build the data backbone that feeds your AI systems — from CDC ingestion to feature stores.

[ SUBMIT SPECS ] [ SEE OUR WORK ]

What happens after you submit specs

1. Context

We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.

2. Recommendation

You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.

3. Next Step

If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.

// Streaming pipeline health check

$ kafka-check --cluster prod --topics 48

✓ Consumer lag: 0 · Throughput: 2.4M events/day

✓ CDC ingestion: 12 sources active

✓ Schema registry: 340 schemas

Real-Time Data Infrastructure

We build the data backbone that feeds your AI systems — from CDC ingestion to feature stores, with exactly-once semantics and sub-second latency.

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

Exactly-once delivery semantics
Schema evolution with Avro/Protobuf registries
Automated data quality checks at every pipeline stage
Infrastructure-as-code with Terraform

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 feature freshness the platform cannot actually guarantee
no observability around consumer lag, delivery guarantees, 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
Sub-second event processing, high throughput, exactly-once 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, sub-second 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, sub-second analytics, high-concurrency dashboards

Evidence

Deployments in this area

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Kafka Isolation Forest

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%.

events_day: 2.4M

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Apache Kafka Apache Spark Streaming

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.

data_processing: Real-Time

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Engineering Intelligence

MLOps

Discuss your Data Engineering path

Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.

1. Context

We review the system, constraints, and where risk is most likely to surface.

2. Recommendation

You get a direct recommendation: audit, advisory, sprint, or pause.

3. Next Step

If there is a fit, we define the shortest useful engagement.