Data Science Architect

Key Components of a Data Science Architecture

• Data Models:

  These are the structural representations of data, defining how data elements are organized and their relationships within databases.

• Data Integration:

  This component focuses on ensuring smooth data flow and consistency between different systems using processes like ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) pipelines.

• Data Governance:

  This involves defining policies and standards for data quality, security, privacy, and compliance, ensuring that data is managed responsibly and ethically.

• Data Storage:

  Architectures include solutions for storing data, such as traditional databases, data warehouses, and data lakes, which differ in how they handle structured and unstructured data.

• Data Processing & Transformation:

  This involves the tools and methods for cleaning, transforming, and preparing data for analysis and modeling.

• Data Distribution & Consumption:

  This refers to the processes and systems for delivering data to users, applications, and dashboards for consumption and decision-making.