# Techtide Alibaba Maxcompute Dataworks Analyst

> 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.

- Skill: `techtideohio/techtide-alibaba-maxcompute-dataworks-analyst` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add techtideohio/techtide-alibaba-maxcompute-dataworks-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techtideohio/techtide-alibaba-maxcompute-dataworks-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: TechTideOhio (https://skillmd.com/u/techtideohio)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/techtideohio/techtide-alibaba-maxcompute-dataworks-analyst

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# 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.