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Data ingestion is "the overall process of collecting, transferring, and loading data from one or multiple sources so that it may be analyzed immediately or stored in a database for later use" (Import.io). Data may be entered "into a database, data warehouse, data repository or application" (Keren Blau).
"Data can be streamed in real time or ingested in batches. When data is ingested in real time, each data item is imported as it is emitted by the source. When data is ingested in batches, data items are imported in discrete chunks at periodic intervals of time.
"[I]t can be challenging for businesses to ingest data at a reasonable speed. ... When data ingestion is automated, the software used to carry out the process may also include data preparation features to structure and organize data so it can be analyzed on the fly or at a later time by business intelligence (BI) and business analytics (BA) programs" (TechTarget).
"Data ingestion tools provide a framework that allows companies to collect, import, load, transfer, integrate, and process data from a wide range of data sources" (PAT Research).
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- Business intelligence (BI)
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- Data analytics
- Data architecture
- Data engineering
- Data enrichment
- Data exploration
- Data ingestion
- Data integration
- Data lake
- Data migration
- Data mining
- Data modeling
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- Data science
- Data visualization
- Data warehouse
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- Deduplication
- ELT
- ESB - What is an Enterprise Service Bus?
- ETL
- ETL pipeline
- Information lifecycle management (ILM)
- Machine learning
- Master data management (MDM)
- SaaS
- Service-oriented architecture (SOA)
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