What is Data Ingestion
Receiving data from sources and transferring it to a warehouse, pipeline, or processing system.
Definition
Data Ingestion is the act of taking data from sources and transferring it to a warehouse, pipeline, or processing system. Simply put, this concept helps build reliable services around models: data, compute, access, deployment and monitoring. In practice, it helps to understand what capabilities the tool actually has, what data it will need, and what limitations are worth checking before implementation.
Example
The service uploads new user events from the application to storage for analytics and models every hour.
Why it matters
Stable data loading is important for fresh forecasts and automation of AI processes. This helps you choose AI tools not by big promises, but by how they work in a real problem.
How it works
Typically, the process starts with data sources and the environment, then sets up calculations, access, automation, monitoring, and security rules. In the case of the term “Data Loading”, it is important to look separately at the data, quality criteria and application conditions.
Where it is used
- It is found in projects where data storage, computing, integration, deployment, security and stable operation of AI services are important.
Limitations
Limitations are related to computational cost, security, data quality, latency, service availability, and maintenance complexity.