Data Ingestion moves data from various sources to a storage destination, preparing it for analytics through real-time or batch streaming and data cleaning.
Copies and synchronizes data between systems, ensuring backup and consistency, occurring in bulk, scheduled batches, or real-time across data centers or the cloud.
Speeds up analytics-ready data availability by automating the entire data warehouse lifecycle, including modeling, ingestion, data marts, and governance.
Ensures comprehensive business views for analytics tools through intelligent pipelines, prioritizing scalability, performance, and real-time data quality.
Data integration (DI) consolidates data from multiple sources into a central repository, primarily to bolster BI and analytics capabilities. While contemporary DI tools and methods can manage real-time operational data, historically, the focus was on transferring static, relational data among data warehouses.
Application integration (API) synchronizes data between separate applications to meet operational needs like aligning HR and finance systems, ensuring consistency across datasets. SaaS application automation tools facilitate efficient creation and maintenance of native API integrations.
Discover the benefits of modernizing your data and analytics environment with scalable, efficient, and real-time data replication, without disrupting production systems.