Batch Processing
C3 AI provides you with options to handle large and complex data efficiently. Use data integration options provided by the C3 Agentic AI Platform to support data aggregation from multiple disparate sources.
Batch processing
Batch processing involves collecting and processing large volumes of data at scheduled intervals. This approach is ideal for scenarios where immediate data availability is not critical, as it introduces some latency between data ingestion and availability. Common tasks include data aggregation, transformations, and loading data into databases for analysis.
Type system abstractions
C3 AI utilizes type system abstractions over various data stores, allowing seamless integration with the platform. These abstractions enable users to interact with different data sources consistently, enhancing flexibility and usability across the system.
Platform-supplied data stores
C3 AI supports several platform-supplied data stores, including:
PostgreSQL: A robust relational database that offers strong consistency and complex querying capabilities, making it suitable for structured data.
Cassandra: A distributed NoSQL database designed for high availability and scalability, ideal for managing large volumes of unstructured data.
In-Memory Stores: These provide rapid data access for real-time analytics and processing, significantly reducing latency.
pgvector: An extension for PostgreSQL that enables efficient storage and querying of vector data, particularly useful for machine learning applications.
Examples in C3 AI
Loading Data: Bulk loading data from remote file systems like S3 or Azure Blob storage.
Scheduled Processing: Processing data from CRM systems like Salesforce or ERP systems like SAP at defined intervals.
By leveraging these type system abstractions and platform-supplied data stores, C3 AI enhances its batch processing capabilities, ensuring efficient data management and analysis while accommodating diverse data needs.