Eventual Connectivity

In the ever-evolving landscape of data management, traditional methods often fall short, paving the way for innovative approaches to take center stage. One such paradigm shift is the move from the well-established ETL (Extract, Transform, Load) model to the intriguing concept of "Eventual Connectivity."


The ETL Conundrum

For decades, ETL has been the stalwart for importing data, ensuring its cleanliness, and planning meticulous architectures for system connections. However, as data ecosystems burgeon and diversify, ETL hits scalability roadblocks. Its assumptions about pristine data, simple system connections, and straightforward identification of joins become untenable, especially when dealing with a multitude of sources.


Enter Eventual Connectivity

The Eventual Connectivity pattern challenges the status quo, offering a dynamic and scalable alternative. Unlike ETL, it doesn't demand upfront planning of system connections or intricate modeling. Instead, it adopts a "load first, connect later" philosophy. This approach involves flagging records with metadata and relationships as data is loaded, allowing relationships to emerge organically.


Why Eventual Connectivity Shines

  1. Scalability Unleashed: With ETL, connecting 500 systems prompts concern about months of upfront modeling. Eventual Connectivity, however, allows a systematic, one-system-at-a-time approach, easing the integration process.

  2. Denormalized Discoveries: The pattern aids in discovering denormalized references and relationships between data, removing the need to revisit the drawing board when new systems are introduced.

  3. Graph Databases: Leveraging graph databases, Eventual Connectivity ensures a more flexible and less modeling-dependent approach, catering to the needs of modern data consumers, such as Data Warehouses and Machine Learning applications.

  4. Transparency in Non-Blending: Identifying data that doesn't blend well becomes straightforward, thanks to floating "edges," enabling a clearer understanding of potential issues.


Acknowledging the Challenges

While Eventual Connectivity introduces a breath of fresh air, acknowledging its challenges is crucial for a balanced perspective:

  1. Post-Ingestion Duplicates: The process might lead to the appearance of duplicates during data ingestion, posing a challenge that needs to be addressed after the fact.

  2. Perceived Lack of Control: Some may feel a diminished sense of control over data connections. However, this is often a result of oversight in architecture planning sessions.


A Reading Analogy

To understand the distinction, consider reading a book.
ETL would ask you to explain character relationships on page 14, akin to predicting the data connections upfront. In contrast, Eventual Connectivity suggests finishing the book, marking relationships along the way, and creating a comprehensive map-a more intuitive and flexible approach.


Embracing the Data Revolution

As businesses grapple with data from myriad sources, embracing the data revolution becomes paramount. Eventual Connectivity emerges not as a silver bullet but as a potent tool for dynamic, scalable, and less modeling-restrictive data integration. It's time to reevaluate our strategies, ushering in a new era of connectivity that aligns with the evolving demands of the digital landscape.

Read more:
CluedIn Eventual Connectivity Whitepaper



Tags iconMaster Data Management, CluedIn