Data Drift
Data drift refers to the phenomenon where the statistical properties of a dataset used for machine learning or analysis change over time. This alteration can be due to various factors, such as shifts in data collection processes, changes in the underlying distribution of the data, or modifications in the environment from which the data originates. Detecting and addressing data drift is crucial to maintaining the performance and reliability of machine learning models and analytical systems.
DBT
dbt is an open-source command-line tool that enables data transformation and modeling in a structured and efficient manner. It allows data engineers and analysts to define and manage the data transformation pipeline using SQL queries. With dbt, you can write modular and reusable SQL code, called "models," which define the transformations required to convert raw data into structured and analysis-ready data. These models can be organized, tested, and documented within the dbt framework. dbt leverages the power of SQL and provides a layer of abstraction on top of the data warehouse, making it easier to develop, test, and maintain complex data transformations. It promotes best practices such as version control, testing, and documentation, enabling collaborative and maintainable data modeling workflows. dbt integrates with various data warehouses and can be used in conjunction with other data tools and orchestration platforms to create a robust and reliable data pipeline.