Overview
Pipelines are written one at a time by different people, and the platform ends up as a set of jobs with no single description. AceMQ inventories the estate, finds where failures come from, and designs the target architecture with the team that will run it.
Challenge
Failures are fixed by restarting the job. Two pipelines compute the same figure from the same source, in different ways, and reports disagree. Retries duplicate data because tasks are not idempotent. Events from operational systems reach the analytics side in a batch that is hours behind. The engineers who built the first pipelines have moved on, and the platform has no owner at night.
Environment
Apache Airflow orchestrating Spark jobs and warehouse loads, a Kafka cluster carrying some events, object storage and a cloud warehouse, on one cloud provider.
Approach
AceMQ works remotely, guided by the team's engineers, who provide exports and read-only views. It builds an inventory of sources, pipelines, schedules, storage layers and consumers, reads the last several months of failures, and groups them by cause. Specialist partner engineers join where the estate calls for Spark or warehouse depth. The target design is staged so old and new paths can run side by side before cutover.
Solution
- Inventory and dependency map of sources, pipelines, schedules, storage layers and consumers
- Failure analysis grouped by cause: non-idempotent tasks, overlapping schedules, upstream late data and resource limits
- Target architecture covering ingestion, storage layers, orchestration and quality checks at stage boundaries
- Recommendation on where change data capture or a broker replaces batch extracts
- Idempotency, backfill and alerting standards for new and existing pipelines
- A staged plan with exit criteria for each stage and a stated owner for each pipeline
- Runbooks and the support tier to put around the platform after delivery
Outcome
The engagement delivers a written inventory, a target architecture with the reasons for each choice, ranked findings, standards for idempotency, backfills and alerts, and a staged plan the team can run or hand to AceMQ. The risk removed is an estate that depends on one person's memory and cannot be changed safely.
Technologies
Apache Airflow
Apache Spark
Kafka
Snowflake
Kubernetes
PostgreSQL
Debezium
