Welcome to the tag category page for Data modeling!
Databricks is an enterprise software company that combines data warehouses and data lakes into a lakehouse architecture. It was founded by the creators of Apache Spark and provides a web-based platform for working with Spark, offering automated cluster management and IPython-style notebooks. Databricks is used for processing, storing, cleaning, sharing, analyzing, modeling, and monetizing datasets, with solutions ranging from business intelligence to machine learning. It is available on two cloud platforms, Azure and AWS, and is infinitely scalable and cost-effective. The Databricks platform can handle all types of data and everything from AI to BI, making it popular among data scientists and data engineers.
Pydantic is a Python library used for data validation and settings management via Python type annotations. It enforces type hints at runtime, making it user-friendly by providing helpful error messages when data is incorrect. It is widely used and downloaded millions of times a day by thousands of developers worldwide. Pydantic allows custom data types to be defined, and it has a custom validation mechanism, making it an easy-to-use and efficient tool for parsing requests and responses. In addition, Pydantic is often used in the FastAPI framework as it has built-in support for JSON encoding and decoding. Compared to other data validation libraries, such as marshmallow, Pydantic returns Python objects directly, making it a powerful tool in data modeling and parsing. It provides the dataclass decorator which creates dataclasses with input data parsing and validation. Pydantic also has a built-in support for JSON encoding and decoding, which makes parsing JSON data effortless.
