For decades now the database world has been oriented towards the schema-on-write approach. First you define your schema, then you write your data, then you read your data and it comes back in the schema you defined up-front. This approach is so deeply ingrained in our thinking that many people would ask, “how else would you do it?” The answer is schema-on-read.
Schema on read follows a different sequence – just load the data as-is and apply your own lens to the data when you read it back out. You might say, “OK, fine. But why would you want to do that?” There are several really compelling reasons. I’ll cover the main ones here.
At this point people often say, “Well sure, but you need a predefined schema or it will be slow.” That’s absolutely true for traditional technologies, but not for an Enterprise NoSQL database like MarkLogic. We are built from the ground up to excel at this approach. [Ed. There’s not enough room to go into how we accomplish that here, but if you’re curious, we’ve got a great paper you can read on the topic.]
The other important thing to keep in mind is that just because we don’t force you to do an extensive data-modeling task up front, doesn’t mean that you can’t learn from your data over time. Get your data loaded, start using it, get value from it. Over time you may well find that you want to normalize certain aspects of your data or otherwise optimize your representation. With MarkLogic, that evolution can happen over time as you gain real-world experience with your use cases and datasets. Imposing too much structure too soon and trying to optimize before you really understand the bottlenecks is a common trap. Schema-on-read can help you avoid it.
Schema-on-read is just one of the ways that MarkLogic can help you solve problems that are a major challenge with traditional technologies.
Like what you just read, here are a few more articles for you to check out or you can visit our blog overview page to see more.
A data platform lets you collect, process, analyze, and share data across systems of record, systems of engagement, and systems of insight.
We’re all drowning in data. Keeping up with our data – and our understanding of it – requires using tools in new ways to unify data, metadata, and meaning.
A knowledge graph – a metadata structure sitting on a machine somewhere – has very interesting potential, but can’t do very much by itself. How do we put it to work?
Don’t waste time stitching together components. MarkLogic combines the power of a multi-model database, search, and semantic AI technology in a single platform with mastering, metadata management, government-grade security and more.
Request a Demo