Building An Enterprise Data Fabric At CluedIn
Data Engineering Podcast
How to Manage Data Lineage and Data Provenance
The data model that we use is kind of like a Git object, right? So I would refer to it as like a versioned object graph. And the interesting thing behind the clue model is that, hey, just because you have the data doesn't mean it's true, right? We need to be statistically confident and making it statistic and confident is something you get by throwing clues through our pipeline. The other way that we expose our data is still through GraphQL, but instead of this classic paging of data, it will stream the data out of us.
Summary
Data integration is one of the most challenging aspects of any data platform, especially as the variety of data sources and formats grow. Enterprise organizations feel this acutely due to the silos that occur naturally across business units. The CluedIn team experienced this issue first-hand in their previous roles, leading them to build a business aimed at building a managed data fabric for the enterprise. In this episode Tim Ward, CEO of CluedIn, joins me to explain how their platform is architected, how they manage the task of integrating with third-party platforms, automating entity extraction and master data management, and the work of providing multiple views of the same data for different use cases. I highly recommend listening closely to his explanation of how they manage consistency of the data that they process across different storage backends.
Announcements
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- Your host is Tobias Macey and today I’m interviewing Tim Ward about CluedIn, an integration platform for implementing your companies data fabric
Interview
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Introduction
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How did you get involved in the area of data management?
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Before we get started, can you share your definition of what a data fabric is?
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Can you explain what CluedIn is and share the story of how it started?
- Can you describe your ideal customer?
- What are some of the primary ways that organizations are using CluedIn?
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Can you give an overview of the system architecture that you have built and how it has evolved since you first began building it?
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For a new customer of CluedIn, what is involved in the onboarding process?
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What are some of the most challenging aspects of data integration?
- What is your approach to managing the process of cleaning the data that you are ingesting?
- How much domain knowledge from a business or industry perspective do you incorporate during onboarding and ongoing execution?
- How do you preserve and expose data lineage/provenance to your customers?
- What is your approach to managing the process of cleaning the data that you are ingesting?
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How do you manage changes or breakage in the interfaces that you use for source or destination systems?
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What are some of the signals that you monitor to ensure the continued healthy operation of your platform?
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What are some of the most notable customer success stories that you have experienced?
- Are there any notable failures that you have experienced, and if so, what were the lessons learned?
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What are some cases where CluedIn is not the right choice?
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What do you have planned for the future of CluedIn?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- CluedIn
- Copenhagen, Denmark
- A/B Testing
- Data Fabric
- Dataiku
- RapidMiner
- Azure Machine Learning Studio
- CRM (Customer Relationship Management)
- Graph Database
- Data Lake
- GraphQL
- DGraph
- RabbitMQ
- GDPR (General Data Protection Regulation)
- Master Data Management
- OAuth
- Docker
- Kubernetes
- Helm
- DevOps
- DataOps
- DevOps vs DataOps Podcast Interview
- Kafka
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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