Data to AI
This section is a perspective on data modeling for journeying from data to AI through several stages, including ingestion to storage, integration, transformation, and archival considerations:
- Data ingestion: Data ingestion is the process of acquiring and importing data from various sources into an analytics database or data warehouse. When designing the data model for ingestion, consider the frequency and volume of data updates, data formats, and data integration requirements. Choose appropriate ingestion mechanisms such as batch processing, real-time streaming, or event-based ingestion based on the timeliness and velocity of your data. Ensure data validation and cleansing mechanisms are in place to maintain data quality during ingestion.
- Storage: Choosing the right storage infrastructure is crucial for efficiently managing and accessing large volumes of data in AI workflows. Cloud object storage and database services such as Google Cloud Storage and BigQuery...