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What are the most common data integration standards and protocols and how do you apply them?

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Data integration is the process of combining data from different sources into a unified and consistent view. It is essential for data architecture, as it enables data analysis, reporting, and decision making across various domains and systems. However, data integration also poses many challenges, such as data quality, security, compatibility, and scalability. To overcome these challenges, data architects need to follow some methods and standards that facilitate data integration and ensure its reliability and efficiency. In this article, we will explore some of the most common data integration standards and protocols and how you can apply them in your data architecture projects.

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Ken Kondo CSM, CSPO

AI, Software and Data Leader and Innovator

1 Data integration methods

Data integration methods vary depending on the nature and frequency of data exchange, the level of data transformation, and the type of data integration architecture. Extract, transform, and load (ETL) is a popular method for batch processing of large volumes of structured or semi-structured data. Extract, load, and transform (ELT) is similar to ETL, but the transformation is done after loading into the target destination. Change data capture (CDC) captures and transfers only changes in source data and is useful for real-time or near-real-time data integration. Data virtualization creates a virtual layer that integrates data from various sources without physically moving or storing it. This method provides a unified view of the data and is suitable for complex and heterogeneous data environments.

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Chris Finegan

Principal Architect-Data
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ALL methods, seek “unified view” of data, ETL & ELT is done once & only once “prior to query”, and data virtualization is done on each & every query. ALL three methods must have “versioning” capable of mapping source across changing layouts/sources.

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Ken Kondo CSM, CSPO

AI, Software and Data Leader and Innovator
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In my experience as a VP of Software Development, middle Tier interfaces using API and Middleware would be additional methods of Data Integration. You can provide, acquire and govern data through these methods.

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2 Data integration standards

Data integration standards are sets of rules and guidelines that define how data should be represented, exchanged, and processed among different systems and applications. The purpose of these standards is to ensure data interoperability, compatibility, and quality across various data sources and destinations. Data model standards, such as entity-relationship model (ERM), unified modeling language (UML), and dimensional modeling, define the logical and physical structure and design of the data, such as its entities, attributes, relationships, and constraints. Data format standards like comma-separated values (CSV), JavaScript object notation (JSON), and extensible markup language (XML) define the syntax and semantics of the data, such as its encoding, notation, and validation. Lastly, data exchange standards like hypertext transfer protocol (HTTP), simple object access protocol (SOAP), and representational state transfer (REST) define the protocols and mechanisms for data transmission and communication among different systems and applications. All these standards help to ensure data security, integrity, and reliability across different networks and channels.

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Ken Kondo CSM, CSPO

AI, Software and Data Leader and Innovator
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In my experience as a VP of Software Development, data governance, a well-defined data dictionary and maybe most importantly the business logic calculations are necessary data integration standards that must be met. Without these, the data can be messy and misleading.

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3 Data integration protocols

Data integration protocols are specific implementations of data exchange standards that enable data to be exchanged between different systems and applications. These protocols define how data should be requested, delivered, and processed among different endpoints. Common data integration protocols include web services, which use HTTP as the underlying protocol and SOAP or REST as the data exchange standard, application programming interfaces (APIs) that use various data exchange standards and protocols to expose and consume data from different sources and destinations, and message brokers that facilitate data exchange among different applications using a publish-subscribe model. Data integration protocols are used for integrating data across various web-based applications, software systems, modules, and distributed asynchronous systems.

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4 How to apply data integration standards and protocols

To apply data integration standards and protocols in your data architecture projects, you need to assess your requirements and objectives, choose the appropriate method and architecture, adopt relevant standards and protocols, and implement and monitor your solution. Understanding the scope, purpose, and goals of your project is essential for selecting the data integration method and architecture that best suit your needs. You'll also need to consider the trade-offs of each option before adopting the data integration standards and protocols that are compatible with the data sources and destinations you need to integrate. Once you have implemented your solution, it's important to monitor it by using metrics, indicators, and feedback mechanisms that measure performance, quality, and outcomes.

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Ken Kondo CSM, CSPO

AI, Software and Data Leader and Innovator
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As a Data Steward, it's important to lead with the Business and come up with well-defined measures and KPIs. Data Integration is only as good as it's governance and provided value. If you go through the process of getting and shaping your data and only end up with comments like "That's neat!", you're missing the point.

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