Data ingestion is the process of collecting and importing raw data from various sources (Database, API, Data Streaming services) into a system for processing and analysis, and can be performed in batch and realtime ingestion.
Used for building Data Pipeline
Challenges
- Data Quality: Ensuring that the ingested data is accurate, complete, and consistent.
- Scalability: Handling large volumes of data efficiently as the data sources grow.
- Latency: Minimizing the delay between data generation and processing, especially in real-time scenarios.
Use Cases:
- Data ingestion is used in various applications, including: business intelligence, Machine Learning
Tools and Technologies:
- Apache Kafka
- AWS Kinesis: A cloud service for real-time data processing, enabling the collection and analysis of streaming data.
- Google Pub/Sub: A messaging service that allows for asynchronous communication between applications, supporting real-time data ingestion.