As more organizations are modernizing and adopting cloud data warehouses, a new range of data ingestion and integration use cases emerge. On-premises data warehouses are typically populated in a traditional extract, transform, and load (ETL) process, using batch extracts followed in lockstep by batch loads. However, cloud data warehouses are much more flexible and dynamic, with data consumers expecting to integrate a wide variety of data sources and data types, including streaming, “semi-batch,” and batch data—as well as structured, unstructured, and semistructured data.
In this webinar, we will explore differences between on-premises and cloud platforms for data warehousing that introduce best practices for data ingestion and integration for cloud data warehouses.
Attendees will learn about:
- Handling ingestion for different types of data (e.g., structured versus unstructured data)
- The challenges of ingesting and absorbing data streams
- Efficient ingestion patterns for batch and streaming data
- Integrating across different data sources, types, and ingestion patterns