DataTerminology.comNEW
Also known as: Data Import
The process of collecting and importing data from varied sources into storage or processing systems for downstream use.
DESCRIPTION
Data ingestion brings data into platforms via batch loads, streaming pipelines, APIs, and file transfers. It supports operational and analytical workloads, feeding warehouses, lakes, and lakehouses. Key considerations include scalability, latency, ordering, deduplication, schema validation, and security. Modern ingestion uses managed services, connectors, and CDC to reduce friction. Observability and backpressure handling maintain reliability. Proper governance ensures compliant handling of personal and sensitive data.
ALTERNATIVE DEFINITIONS
AWS
Data ingestion is the process of taking data from one or more sources and moving it to a destination for storage or processing.
Highlights multi-source movement into processing destinations.
Google Cloud
Data ingestion is the process of importing, transferring, loading, and processing data for immediate use or storage in a database.
Emphasizes architecture patterns and GCP services.
Confluent
Data ingestion is the process of moving data from various sources to a destination where it can be stored and analyzed.
Focuses on streaming platforms and analytics use.
RELATED TERMS
Data Integration Extract-Transform-Load ELT Streaming Ingestion Batch Ingestion Change Data Capture Queue API Pipelines Data Lake
COMPETING TERMS
Streaming Data Ingestion Batch Data Ingestion
RELATIONS
KNOWLEDGE AREAS
Data Engineering Real-time & Streaming Data Data Integration & Interoperability Data Services Cloud
TARGET AUDIENCE
Data Engineer Data Architect MLOps Engineer AI Engineer Software Developer
EXTERNAL SOURCES
- Wikipedia - Data pipeline
- Google Cloud - Reference architectures for ingestion
- AWS - Kinesis streaming ingestion docs
- Apache Kafka - Kafka ingestion, streaming platform
- Microsoft Learn - Azure ingestion patterns
TOP QUESTIONS
What is the difference between Streaming and Batch Data Ingestion?
Streaming ingests continuous events for low latency; batch imports data in scheduled chunks for throughput and simplicity.
Category: Misunderstanding
What are common Data Ingestion sources?
Databases via CDC, APIs, files, logs, queues, devices, and SaaS platforms.
Category: Example
What are typical risks in Data Ingestion?
Schema drift, duplicate events, ordering issues, backpressure, and security exposures can compromise reliability and compliance.
Category: Risk
How to ensure quality in Data Ingestion?
Add schema validation, idempotency, deduplication, monitoring, and alerting; enforce access controls and encryption.
Category: Best practices
What is a hot topic around Data Ingestion?
Managed CDC connectors and declarative ingestion pipelines are rapidly evolving.
Category: Topic
How is Data Ingestion managed at scale?
Use partitioning, buffering, autoscaling, and backpressure handling with observability and SLAs.
Category: Management
Where should Data Ingestion land data?
Stage raw data in lakes or staging schemas, then curate into warehouses or lakehouses via downstream transformations.
Category: Opinion
How does Data Ingestion relate to Data Governance?
Governance ensures lawful collection, consent, security, and retention; it must be embedded in ingestion workflows.
Category: Requirements
Where to start learning Data Ingestion?
Implement a simple pipeline using managed services like Kafka or Kinesis, add schema validation, and monitor throughput.
Category: Requirements