Data Coupling
Data Coupling

Data Coupling

Coupling is a parameter dependency approach used cross software or cross systems.

Let’s decode it…..

This approach is used when there are more than one software or system is in picture to deliver the result.

Data Coupling is about when data elements are used, to kick-off next step in a different system. For example, in data pipeline, we are pulling data from a hospital management system e.g., when a baby is born let’s have a data dependency like, the moment the baby is registered in system, pull its data. So, it’s a cross system data dependency or we called is Data Coupling.

Image: https://www.chegg.com/learn/computer-science/computer-software/data-coupling

Disadvantage of Data Coupling approach is, if something goes wrong in first system, dependent systems cannot move.

Let me share another very old example. Old data pipeline folks will remember this one i.e., when we used to load files from another system, we had to wait till the time whole file movement is completed. The challenge was, how to make sure when to start pulling like only once file is completely copied.

The workarounds for above challenge were.

·????????Copy file in a different name. Once file is completely copied, another command can rename it and pulling job can pull the required file.

·????????Create a temp file after data file is completely copied so when pulling job can see temp file, it should start pulling data files.

·????????Now a day, export or copy or move commands are smart. When files are in progress, there is tmp add in the naming conversion of the file. Once files are copy, the same query removes the tmp word and pulling job can recognize the data file name to pull it.

Type of Coupling

·????????No Direct Coupling

·????????Data Coupling

·????????Stamp Coupling

·????????Control Coupling

·????????External Coupling

·????????Comment Coupling

·????????Content Coupling

Cheers.

要查看或添加评论,请登录

Mustafa Qizilbash的更多文章

  • Data Products with Challenges

    Data Products with Challenges

    In today’s data-driven landscape, organizations heavily rely on data products to drive insights, improve efficiency…

    6 条评论
  • Common Pitfalls when evaluating and decommissioning data products & How to Avoid

    Common Pitfalls when evaluating and decommissioning data products & How to Avoid

    Even with a structured approach, organizations often encounter challenges when evaluating and decommissioning data…

    2 条评论
  • A Lifecycle Framework for Evaluating and Decommissioning Data?Products

    A Lifecycle Framework for Evaluating and Decommissioning Data?Products

    A structured lifecycle approach ensures efficiency, accountability, and minimal disruption when evaluating and retiring…

    2 条评论
  • Types of Data Products to Decommission

    Types of Data Products to Decommission

    Not all data products remain valuable indefinitely. As businesses evolve, certain data assets become obsolete…

  • The Need for Evaluating and Decommissioning Data Products

    The Need for Evaluating and Decommissioning Data Products

    1. The Challenge of Data Product Sprawl Organizations tend to accumulate numerous data products over time for several…

    4 条评论
  • Impact & Governance

    Impact & Governance

    As organizations strive to become data-driven, the ability to measure, govern, and optimize data initiatives is…

    2 条评论
  • Decision-Making Context or Data?Story

    Decision-Making Context or Data?Story

    Data, in its raw form, is just a collection of facts. It’s the story we weave around that data that transforms it into…

    1 条评论
  • Data Product Lifecycle & Problem-Solving Focus

    Data Product Lifecycle & Problem-Solving Focus

    In today’s data-driven landscape, the role of a Chief Data Officer (CDO) extends beyond governance and compliance. A…

    3 条评论
  • The Evolving Landscape of Data Practice [a landscape CDO Owns]

    The Evolving Landscape of Data Practice [a landscape CDO Owns]

    In today’s rapidly evolving digital landscape, organizations face increasing challenges in building scalable…

    2 条评论
  • Foundation of Data Practice (Supporting the Four 4s Framework)

    Foundation of Data Practice (Supporting the Four 4s Framework)

    The Four 4s framework provides a structured approach for data teams to define strategy, execute initiatives, and drive…

    6 条评论

社区洞察

其他会员也浏览了