3 Data Ingestion Best Practices: The Trends to Drive Success

Data ingestion, the first stage of a data pipeline, plays a crucial role in establishing the flow of data from source systems to target systems, such as data lakes or data lakehouses, and can be executed through batch processing, streaming, or change data capture methods. Batch ingestion collects and transfers data at scheduled intervals, making it suitable for scenarios where real-time processing isn't needed, while streaming ingestion captures and transfers data continuously for real-time applications. Change data capture tracks dataset changes and updates the analytic system when a threshold is reached. Best practices for optimizing data ingestion include using Apache Iceberg for its cost-effective cloud storage and enhanced metadata capabilities, employing versatile workload configurations to accommodate various data velocities, and performing data quality checks to ensure accuracy and reliability. Starburst Icehouse architecture supports these practices by leveraging Apache Iceberg, data streaming, and quality checks, fostering an open data architecture that democratizes data access and prevents vendor lock-in.