Hi @ManojReddy , Certainly! Let’s break it down in a way that’s easy to understand:
Materialized Views:
- Imagine materialized views as precomputed results. They store data that you can query efficiently.
- When you refresh a materialized view, it recalculates the results based on changes in the original data.
- Think of it like a snapshot that updates itself whenever the underlying data changes.
DLT Pipelines:
- DLT pipelines are like powerful data transformers. They process data, apply logic, and create the desired output.
- When you trigger a DLT pipeline, it processes data through query rules and transformations.
- For materialized views, the pipeline ensures that the results are always accurate and up-to-date.
Choosing the Right Dataset Type:
- If you have complex queries, use views. They don’t store results but help break down big queries.
- For shared data across multiple queries, go for materialized views. They recompute results every time you query them.
- If you’re dealing with constantly growing data, consider streaming tables. They compute results incrementally.
In a nutshell, materialized views keep things accurate, DLT pipelines do the heavy lifting, and choosing the right dataset type ensures efficiency. 🌟
