登录查看更多内容

What are the most common ETL data governance frameworks and how can you use them effectively?

由人工智能和领英社区提供技术支持

ETL, or extract, transform, and load, is a process of moving data from various sources to a target destination, such as a data warehouse or a data lake. ETL data governance is a set of policies, standards, and best practices that ensure the quality, security, and usability of the data throughout the ETL lifecycle. In this article, you will learn about the most common ETL data governance frameworks and how you can use them effectively to design and implement your ETL projects.

此文章中的业界达人

由社区从 2 条内容中精选。了解更多

Samrat Mitra

Lead MLOps & Data Engineer @Vivasoft | GCP, Airflow, Databricks | Ex BI Data Engineer @uPay(UCB) | Ex Data Scientist…

1 The DAMA-DMBOK framework

The DAMA-DMBOK, or the Data Management Body of Knowledge, is a comprehensive guide for data management professionals, developed by the Data Management Association International (DAMA). The DAMA-DMBOK framework covers 11 knowledge areas, such as data quality, data security, data integration, and data architecture. Each knowledge area defines the goals, objectives, principles, roles, and activities for managing data. You can use the DAMA-DMBOK framework as a reference to align your ETL data governance strategy with the industry standards and best practices.

添加您的观点

Fredricka David

Data Management | AWS Certified Cloud Practitioner
举报内容
The DAMA-DMBOK framework is like a helpful guide for managing data in organizations, including the steps involved in ETL (Extract, Transform, Load) processes. It focuses on the importance of strong data governance, which means setting up rules and standards to manage data in an organized way. In ETL, this means making sure data is handled the same way and follows the rules from start to finish.

已翻译

赞

2 The CMMI-DCMM framework

The CMMI-DCMM, or the Capability Maturity Model Integration for Data Management Maturity, is a framework for assessing and improving the maturity of data management capabilities, developed by the CMMI Institute. The CMMI-DCMM framework consists of six capability areas, such as data governance, data quality, data operations, and data architecture. Each capability area has five maturity levels, from initial to optimized. You can use the CMMI-DCMM framework as a tool to measure and benchmark your ETL data governance performance and identify the gaps and opportunities for improvement.

添加您的观点

3 The DGI-DCAM framework

The DGI-DCAM, or the Data Governance Institute Data Capability Assessment Model, is a framework for evaluating and enhancing the data capabilities of an organization, developed by the Data Governance Institute (DGI). The DGI-DCAM framework comprises eight data capabilities, such as data strategy, data quality, data integration, and data security. Each data capability has four dimensions, such as people, process, technology, and structure. You can use the DGI-DCAM framework as a method to assess and improve your ETL data governance capabilities and align them with your business goals and requirements.

添加您的观点

4 The TDWI-ETL framework

The TDWI-ETL, or the Data Warehousing Institute Extract, Transform, and Load Framework, is a framework for designing and implementing ETL processes, developed by the Data Warehousing Institute (TDWI). The TDWI-ETL framework consists of four phases, such as analysis and design, extraction and transportation, transformation and cleansing, and loading and distribution. Each phase has several tasks, such as data profiling, data mapping, data validation, and data auditing. You can use the TDWI-ETL framework as a guide to plan and execute your ETL data governance activities and ensure the quality and consistency of the data.

添加您的观点

5 The ETL best practices framework

The ETL best practices framework is a framework for applying the best practices for ETL data governance, based on the experience and expertise of the ETL practitioners and experts. It includes principles such as defining and documenting roles and responsibilities, establishing and enforcing policies and standards, implementing and monitoring processes and workflows, using and maintaining tools and technologies, as well as reviewing and improving practices and outcomes. This framework can be used as a checklist to ensure that you follow the best practices for ETL data governance and achieve the desired results.

添加您的观点

Samrat Mitra

Lead MLOps & Data Engineer @Vivasoft | GCP, Airflow, Databricks | Ex BI Data Engineer @uPay(UCB) | Ex Data Scientist @TallyKhata,@SureCash | Big Data Enthusiast | Experienced in designing Data warehouse infrastructure
举报内容
I know a little bit about Apache Atlas. Apache Atlas is a metadata management and governance tool; it is not an ETL tool in and of itself. Better data governance is made possible by its integration with a range of ETL and data processing tools, which create a consolidated metadata repository.

已翻译

赞

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Database Development

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What are the most common ETL data governance frameworks and how can you use them effectively?

1

2

3

4

5

6

1 The DAMA-DMBOK framework

2 The CMMI-DCMM framework

3 The DGI-DCAM framework

4 The TDWI-ETL framework

5 The ETL best practices framework

6 Here’s what else to consider

Database Development

给文章评分

感谢您的反馈

更多Database Development相关文章

更多相关阅读内容

What are the most common ETL data governance frameworks and how can you use them effectively?

1

2

3

4

5

6

1 The DAMA-DMBOK framework

2 The CMMI-DCMM framework

3 The DGI-DCAM framework

4 The TDWI-ETL framework

5 The ETL best practices framework

6 Here’s what else to consider

Database Development

给文章评分

感谢您的反馈

查看其他技能