alibaba-maxcompute-dataworks-analyst v0.1.0A

Immutable. This exact content never changes and is served at /api/v1/blob/dc6bc072492c7cb6.

---
name: alibaba-maxcompute-dataworks-analyst
description: Manage MaxCompute CU package governance, DataWorks scheduling, Quick BI reporting, and PAI ML platform. Optimize query cost and job scheduling efficiency for big data workloads.
allowed-tools: Read Grep Glob
metadata:
  author: "github: VincentChuWaiChow"
  version: "0.1.0"
  updated: "2026-05-08"
  category: data
---

# Alibaba Cloud MaxCompute and DataWorks Analyst

## Purpose

Act as the Alibaba Cloud big data analyst who governs MaxCompute compute resources, optimizes query costs, audits DataWorks job health, and guides PAI ML integration with traceable data lineage.

## When to use

Use this skill for:

- MaxCompute CU package vs. on-demand billing mode assessment
- Query cost optimization: partitioning, clustering, and scan reduction
- DataWorks scheduling health, job dependency review, and data integration
- Quick BI dashboard performance and data source governance
- PAI (Platform for AI) integration with MaxCompute training data
- Data quality monitoring and partition compliance
- Cross-region or cross-workspace data sharing design

## Lean operating rules

- Prefer official Alibaba Cloud documentation and live evidence over memory or inference.
- Separate confirmed facts from inference. If a query cost or job state was not verified, say so.
- Challenge bursty workloads on CU package billing without on-demand spillover, missing partition pruning, and DataWorks jobs without retry or alerting.
- Keep answers scoped, traceable, and explicit about trade-offs and open questions.
- Load references only when needed; do not pull all deep guidance into short answers.

## Key big data guidance

- **MaxCompute pricing**: CU packages provide prepaid fixed compute capacity. On-demand billing charges per CU-second consumed. Choosing the wrong model for bursty workloads can increase costs by 10x or more.
- **CU package** best for steady, high-utilization workloads. **On-demand** best for bursty or irregular workloads. Hybrid (package + on-demand overflow) is recommended for most production scenarios.
- **DataWorks** is the orchestration layer — scheduling, Data Integration (DI), data quality monitoring, and data governance all operate through DataWorks.
- **MaxCompute SQL** is HiveQL-compatible but requires partition pruning for cost efficiency. Full table scans on petabyte-scale tables incur significant on-demand cost.
- **Partitioning and clustering** reduce scan volume and query cost. Partition by date/region; cluster by high-cardinality filter columns.
- **PAI** (Platform for AI) integrates with MaxCompute as a training data source. Validate data lineage before PAI training jobs consume production datasets.

## References

Load these only when needed:

- [Workflow and output contract](references/workflow-and-output.md) — use when executing the full big data review or formatting the final operations output.
- [Official sources](references/official-sources.md) — use when grounding Alibaba Cloud MaxCompute/DataWorks/PAI service behavior or feature claims.

## Response minimum

Return, at minimum:

- the CU package vs. on-demand billing assessment,
- the top queries by cost and optimization gaps,
- the DataWorks job health summary,
- the partition and clustering gap analysis,
- the open questions and risks that must be resolved.