Building Linked Data Products With JSON-LD
Data Engineering Podcast
The Concept of Linked Data and its Implications
This chapter discusses the concept of linked data and its implications in various contexts such as web pages, application development, data pipelines, and data analytics. It emphasizes the importance of graph data and the linked data standards put forth by the worldwide web consortium. The chapter also explores the usage of JSON-LD in RDF and the semantic web, and highlights the benefits of leveraging common ontologies in application development.
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Summary
A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold
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- Your host is Tobias Macey and today I'm interviewing Brian Platz about using JSON-LD for building linked-data products
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what the term "linked data product" means and some examples of when you might build one?
- What is the overlap between knowledge graphs and "linked data products"?
- What is JSON-LD?
- What are the domains in which it is typically used?
- How does it assist in developing linked data products?
- what are the characteristics that distinguish a knowledge graph from
- What are the layers/stages of applications and data that can/should incorporate JSON-LD as the representation for records and events?
- What is the level of native support/compatibiliity that you see for JSON-LD in data systems?
- What are the modeling exercises that are necessary to ensure useful and appropriate linkages of different records within and between products and organizations?
- Can you describe the workflow for building autonomous linkages across data assets that are modelled as JSON-LD?
- What are the most interesting, innovative, or unexpected ways that you have seen JSON-LD used for data workflows?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on linked data products?
- When is JSON-LD the wrong choice?
- What are the future directions that you would like to see for JSON-LD and linked data in the data ecosystem?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
- Fluree
- JSON-LD
- Knowledge Graph
- Adjacency List
- RDF == Resource Description Framework
- Semantic Web
- Open Graph
- Schema.org
- RDF Triple
- IDMP == Identification of Medicinal Products
- FIBO == Financial Industry Business Ontology
- OWL Standard
- NP-Hard
- Forward-Chaining Rules
- SHACL == Shapes Constraint Language)
- Zero Knowledge Cryptography
- Turtle Serialization
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Materialize:  You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access) today and get 2 weeks free!
- Hex:  Hex is a collaborative workspace for data science and analytics. A single place for teams to explore, transform, and visualize data into beautiful interactive reports. Use SQL, Python, R, no-code and AI to find and share insights across your organization. Empower everyone in an organization to make an impact with data. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial of the Hex Team plan!
- Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
- Datafold:  This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today!
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