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Also known as: Source Onboarding, Data Ingestion for Sharing, Producer Integration
Onboard and ingest source systems, establishing connectivity, contracts, and pipelines to provide master and reference data for enterprise sharing.
DESCRIPTION
Acquiring sources involves connectivity setup, profiling, mapping, and contract negotiation. Teams choose batch or streaming ingestion, apply CDC for timely updates, and implement quality checks. Security and privacy are validated before production onboarding. Documentation and catalogs register ownership and semantics. Repeatable onboarding templates accelerate new integrations and reduce risk. Effective acquisition underpins reliable data sharing services.
ALTERNATIVE DEFINITIONS
Wikipedia
Extract, transform, load (ETL) is the general procedure of copying data from one or more sources into a destination system that represents the data differently from the source.
Emphasizes transformation-oriented batch acquisition into new representations.
Confluent
Kafka Connect provides a framework and ready-to-use connectors to reliably move large volumes of data between Apache Kafka and other systems.
Targets streaming acquisition with connector ecosystem.
Google Cloud
Data ingestion is the process of transporting data from various sources into a storage system for processing and analysis, at batch or streaming rates.
Distinguishes batch and streaming ingestion modes.
RELATED TERMS
Data Ingestion Extract-Transform-Load ELT Source System Data Mapping Change Data Capture Schema Registry Data Contracts Data Quality Data Onboarding
COMPETING TERMS
Batch Ingestion Streaming Ingestion
RELATIONS
- Broader: Data Ingestion Data Onboarding
KNOWLEDGE AREAS
Data Engineering Data Integration & Interoperability Reference & Master Data
TARGET AUDIENCE
Data Engineer Data Steward Data Architect Analytics Engineer
EXTERNAL SOURCES
- Wikipedia - Extract, transform, load
- Google Cloud - Ingestion patterns and best practices
- Confluent - Streaming ingestion connectors
- Microsoft Learn - Batch file ingestion guidance
TOP QUESTIONS
What steps are involved in Acquire Data Sources for Sharing?
Connect to sources, profile data, map fields, define contracts, choose ingestion mode, implement quality checks, and register assets in catalogs.
Category: Requirements
How do Batch Ingestion and Streaming Ingestion compare in Acquire Data Sources for Sharing?
Batch suits periodic large loads; streaming delivers continuous updates with lower latency and more complex operations.
Category: Comparison
Who owns producer-side responsibilities in Acquire Data Sources for Sharing?
Source owners provide interfaces and metadata; stewards validate semantics; engineers implement connectors and CDC.
Category: Management
What are common pitfalls in Acquire Data Sources for Sharing?
Weak contracts, undocumented fields, ignoring PII, and underestimating schema drift lead to brittle pipelines.
Category: Mistakes
How to handle schema changes in Acquire Data Sources for Sharing?
Use registries, backward-compatible changes, versioning, and alerting to prevent consumer breakage.
Category: Operations
Why is profiling crucial in Acquire Data Sources for Sharing?
It reveals anomalies, distributions, and semantics necessary for accurate mapping and cleansing.
Category: Relevancy
How do contracts help in Acquire Data Sources for Sharing?
Contracts formalize schema, meaning, SLAs, and change policies, aligning producer and consumer expectations.
Category: Best practices
Which tools assist Acquire Data Sources for Sharing?
Dataflow/ADF for batch, Kafka Connect for streaming, schema registries, and lineage tools for traceability.
Category: Tooling
How does privacy factor into Acquire Data Sources for Sharing?
Identify personal data, enforce minimization, use masking/tokenization, and validate lawful bases before ingestion.
Category: Risk