Pipelines that fail loudly,
not silently.
Ingestion, transformation, warehousing and the observability around them, built so a broken load wakes someone up instead of quietly serving stale numbers to a dashboard.
Orchestration is the difference.
Most data platforms do not fail at the transform step. They fail because nothing knew the transform depended on a load that never finished, and the dashboard kept rendering yesterday’s number.
// Freshness and volume monitoring, so quality problems surface before a dashboard lies to an executive.
Orchestrating Glue ETL with Apache Airflow.
ETL jobs were running without dependency-aware scheduling. Failures surfaced late, backfills were manual, and there was no reliable way to answer whether a given table was current.
We orchestrated the AWS Glue jobs through Apache Airflow with real dependencies, retries and visibility, so a failed upstream stops the downstream instead of feeding it stale input.
The payoff is not speed, it is trust. A pipeline people trust gets used for decisions. One they do not gets quietly worked around with a spreadsheet.
From source system to decision.
Cloud-native data platforms on AWS, with schema evolution and recovery designed in rather than bolted on after the first outage.
Pipelines & ETL
AWS Glue, Lambda and Step Functions orchestration with automated schema evolution and self-healing recovery.
Warehousing
Amazon Redshift modelling, performance tuning and cost control that survives growth rather than degrading with it.
Integration
Airbyte and API ingestion across external sources, landed reliably and idempotently so a replay is safe.
Orchestration
Airflow and AWS MWAA with dependency-aware scheduling and backfill that actually works.
Data observability
Freshness, volume and schema monitoring, so quality problems surface before someone makes a decision on them.
Analytics infrastructure
Amazon QuickSight and Power BI on a modelled warehouse rather than direct queries against production.
What we build on.
Airbyte, AWS DMS, Kinesis, API integration, change data capture.
AWS Glue, Lambda, Step Functions, dbt, Apache Airflow, AWS MWAA.
Amazon Redshift, RDS, S3, QuickSight, Power BI.
What we will not do
We will not build a warehouse before we understand what decision it serves. Plenty of data platforms are technically excellent and answer nothing anyone asked. If the honest answer is that a report would do, that is the answer you will get.
Bring us the pipeline nobody trusts.
Stale dashboards, silent failures, or a warehouse bill nobody can attribute to a team.