Distributed Data Platform

A distributed processing platform that treats data quality, observability, and deployment discipline as first-class platform concerns.

Open repository

Project claim

5 TB+/day ETL pipeline built for scale and operational quality.

Role

Platform builder

Focus signals

5TB+ daily99.9% job successplatform observability

Proof signal 1

5TB+ processed daily

Proof signal 2

100+ concurrent jobs

Proof signal 3

40% infrastructure cost reduction

Challenge

Large-volume jobs fail not just from code issues but from orchestration, resource pressure, weak observability, and brittle release processes.

Solution

Built a distributed platform around Airflow, Spark on Kubernetes, FastAPI, S3, Prometheus, and Grafana with quality rules, metrics, runbooks, and CI/CD.

Build notes

Treated orchestration, quality, monitoring, and deployment as one platform problem instead of disconnected tool decisions.

Prioritized operational recovery and cost-awareness alongside throughput.

Made observability a product feature for the engineers running the system.

Tools + stack

Airflow, Spark, Kubernetes, Prometheus, and Grafana.

PythonAirflowSparkFastAPIDockerKubernetesAWS EKSPrometheusGrafana

Daily scale

5 TB+

Job health

99.9% success

Ops mindset

Observable by design

Architecture flow

Step 1

ingest

Step 2

dag orchestration

Step 3

spark jobs

Step 4

metrics

Step 5

serving api

Operational value

The point was not only to move data quickly, but to make the whole pipeline observable, recoverable, and economical to operate.

What stands out

Airflow orchestration, Spark on Kubernetes, data quality enforcement, and platform monitoring all work together rather than living as disconnected tools.

Decision signals

Design for operators, not only builders

Quality rules belong inside the pipeline

Scale without observability is unfinished work

Outcomes

Turned ETL into a platform concern with clearer operational ownership.

Integrated quality rules and SLO thinking into the data layer instead of treating them as afterthoughts.

Improved trust by making health, metrics, and deployability part of the product.