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Dive into our library of real-time data integration insights and tutorials. Whether you're starting out or scaling up, Estuary empowers your data-driven success.
What are Schema Inference, Write and Read Schemas?
Lesser known facts about schemas: What is schema inference? What are write and read schemas? When to use both? Try Estuary for free: https://www.estuary.dev/ Join our Slack channel with a community of developers: https://estuary-dev.slack.com/ Flow documents and collections always have an associated schema that defines the structure, representation, and constraints of your documents. Collections must have one schema, but may have two distinct schemas: one for when documents are added to the collection, and one for when documents are read from that collection. Schemas are a powerful tool for data quality. Flow verifies every document against its schema whenever it's read or written, which provides a strong guarantee that your collections hold only "clean" data, and that bugs and invalid documents are caught before they can impact downstream data products. In most cases, Flow generates a functioning schema on your behalf during the discovery phase of capture. In advanced use cases, however, customizing your schema becomes more important. Flow performs static inference of the collection schema to verify the existence and types of all keyed document locations, and will report an error if the location could not exist, or could exist with the wrong type. #schema #estuaryflow #data #dataops #dataengineering #datapipeline
Streaming vs Batch Processing in 5 minutes
This video explains: - What is streaming / stream processing? - What is batch processing? - Why and when to use streaming over batch processing? - A real-world example - 6 real-world use cases Estuary Flow is in public beta now! Get free access to experiment with data streaming in real time: https://www.estuary.dev/ Join our Slack channel with a community of developers: https://estuary-dev.slack.com/ssb/redirect Check out our blog on this topic: https://www.estuary.dev/real-time-and-batch-data-processing-an-introduction/ We're collecting product feedback and are actively developing connectors that our users request. So come try us out! If you need a connector we don't have, reach out to us. We would love to collaborate with you and support your data projects as we continue to refine our product. #data #dataops #dataengineering #estuaryflow
What are Schema Inference, Write and Read Schemas?
Lesser known facts about schemas: What is schema inference? What are write and read schemas? When to use both? Try Estuary for free: https://www.estuary.dev/ Join our Slack channel with a community of developers: https://estuary-dev.slack.com/ Flow documents and collections always have an associated schema that defines the structure, representation, and constraints of your documents. Collections must have one schema, but may have two distinct schemas: one for when documents are added to the collection, and one for when documents are read from that collection. Schemas are a powerful tool for data quality. Flow verifies every document against its schema whenever it's read or written, which provides a strong guarantee that your collections hold only "clean" data, and that bugs and invalid documents are caught before they can impact downstream data products. In most cases, Flow generates a functioning schema on your behalf during the discovery phase of capture. In advanced use cases, however, customizing your schema becomes more important. Flow performs static inference of the collection schema to verify the existence and types of all keyed document locations, and will report an error if the location could not exist, or could exist with the wrong type. #schema #estuaryflow #data #dataops #dataengineering #datapipeline
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