The ETL pattern that has become commonplace for integrating data from multiple sources has proven useful, but complex to maintain. For a small number of sources it is a tractable problem, but as the overall complexity of the data ecosystem continues to expand it may be time to identify new ways to tame the deluge of information. In this episode Tim Ward, CEO of CluedIn, explains the idea of eventual connectivity as a new paradigm for data integration. Rather than manually defining all of the mappings ahead of time, we can rely on the power of graph databases and some strategic metadata to allow connections to occur as the data becomes available. If you are struggling to maintain a tangle of data pipelines then you might find some new ideas for reducing your workload.
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- Your host is Tobias Macey and today I’m interviewing Tim Ward about his thoughts on eventual connectivity as a new pattern to replace traditional ETL
- How did you get involved in the area of data management?
- Can you start by discussing the challenges and shortcomings that you perceive in the existing practices of ETL?
- What is eventual connectivity and how does it address the problems with ETL in the current data landscape?
- In your white paper you mention the benefits of graph technology and how it solves the problem of data integration. Can you talk through an example use case?
- How do different implementations of graph databases impact their viability for this use case?
- Can you talk through the overall system architecture and data flow for an example implementation of eventual connectivity?
- How much up-front modeling is necessary to make this a viable approach to data integration?
- How do the volume and format of the source data impact the technology and architecture decisions that you would make?
- What are the limitations or edge cases that you have found when using this pattern?
- In modern ETL architectures there has been a lot of time and work put into workflow management systems for orchestrating data flows. Is there still a place for those tools when using the eventual connectivity pattern?
- What resources do you recommend for someone who wants to learn more about this approach and start using it in their organization?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Eventual Connectivity White Paper
- Multivariate Testing
- Graph Database
- Apache NiFi
- Apache Airflow
- SAP HANA
- IOT == Internet of Things
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