技能档案

Data Lake Architecture

Medallion architecture, data zones, partitioning, compaction, storage organization

Data Engineering Data Lakehouse

角色数

2

包含此技能的角色

级别数

5

结构化成长路径

必要要求

10

其余 0 个可选

领域

Data Engineering

skills.group

Data Lakehouse

最后更新

2026/3/17

如何使用

选择当前级别并对比期望。下方卡片显示晋升所需掌握的内容。

各级别期望

表格展示从初级到首席的技能深度变化。点击行查看详情。

角色 必要性 描述
Analytics Engineer 必要 Understands data lake zone architecture (raw, curated, consumption). Queries data from curated layers using SQL and dbt models. Follows team conventions for partitioning, file formats, and naming standards.
Data Engineer 必要 Understands medallion architecture principles (bronze/silver/gold). Ingests data into raw landing zones using batch loaders and schema registries. Follows established patterns for Parquet/ORC file layout and catalog metadata.
角色 必要性 描述
Analytics Engineer 必要 Independently builds analytics data products on top of data lake layers using dbt and Spark SQL. Optimizes query performance through intelligent partitioning and Z-ordering. Ensures data quality with Great Expectations checks at zone boundaries.
Data Engineer 必要 Independently designs ETL pipelines across data lake zones with schema evolution support. Optimizes storage costs using lifecycle policies, compaction, and tiered storage. Implements data quality gates between medallion layers with automated validation.
角色 必要性 描述
Analytics Engineer 必要 Architects data systems with Data Lake Architecture. Optimizes for big data. Implements data governance and quality frameworks.
Data Engineer 必要 Designs data architecture with Data Lake Architecture. Optimizes for big data. Implements data governance and quality frameworks.
角色 必要性 描述
Analytics Engineer 必要 Defines the data lake architecture for the analytics platform: medallion approach (bronze/silver/gold), storage format selection (Parquet, Delta, Iceberg). Implements partitioning and retention standards for cost optimization.
Data Engineer 必要 Defines data lake standards: zone architecture (bronze/silver/gold), file formats, partition strategies. Implements access control and data classification. Coordinates between data producers and consumers.
角色 必要性 描述
Analytics Engineer 必要 Architects the enterprise lakehouse: Delta Lake/Iceberg as open table format, integration with dbt for transformations, unified governance. Defines the strategy for combining data lake and warehouse for different analytical workloads.
Data Engineer 必要 Designs data lakehouse architecture: unified storage layer, query engine federation (Trino/Spark), governance framework. Defines when lakehouse vs traditional DWH vs data mesh.

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📋 提案
暂无提案 Data Lake Architecture
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