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Also known as: Upstream System, Upstream Data Store
The originating system or storage from which data is read during ingestion or integration.
DESCRIPTION
A source data store is the upstream system where data originates for pipelines and integrations. It may be a relational database, SaaS application, event stream, file system, or API. Defining sources includes specifying connection details, schemas, and read semantics. Robust ingestion requires managing credentials, latency, throughput, and schema evolution. Provenance and contracts help coordinate changes between producers and downstream consumers. Change data capture and incremental fetch patterns reduce load and latency.
ALTERNATIVE DEFINITIONS
Microsoft Learn
In Copy activity, the source defines the data to be read from a source data store.
Places the term in Azure Data Factory’s source/sink pattern.
Apache Kafka
Source connectors ingest data from external systems into Kafka and make it available for consumption.
Clarifies the role of a source in connector-based ingestion.
RELATED TERMS
Source System Change Data Capture API Operational Database Extract-Transform-Load Middleware Streaming Source SaaS Application Online Transaction Processing
COMPETING TERMS
KNOWLEDGE AREAS
Data Integration & Interoperability Data Engineering Data Storage & Operations
TARGET AUDIENCE
Data Engineer Analytics Engineer Data Architect MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Data source
- Microsoft - ADF source and sink concepts
- AWS - AWS DMS source endpoint guidance
TOP QUESTIONS
What are examples of a Source Data Store?
Operational databases, SaaS CRMs, event streams, file shares, and REST APIs commonly serve as sources for ingestion pipelines.
Category: Example
How do schema changes in a Source Data Store impact pipelines?
They can break ingestion and downstream models. Use schema evolution policies, contracts, and monitoring to mitigate disruption.
Category: Risks
How to reduce load on a Source Data Store?
Leverage CDC, incremental queries, read replicas, off-peak scheduling, and proper indexing.
Category: Best Practices
How is security handled for Source Data Stores?
Use least-privilege credentials, secret rotation, network controls, and audit logs for access.
Category: Requirements
What’s a common mistake with Source Data Stores?
Pulling full extracts when change-based or incremental loads would suffice, causing unnecessary cost and contention.
Category: Mistakes
What contract patterns help with Source Data Stores?
Data contracts define schemas, SLAs, and breaking-change processes between producers and consumers.
Category: Management
How do you handle late or out-of-order data from sources?
Buffer windows, watermarking, and idempotent upserts help ensure correctness in downstream systems.
Category: Best Practices
What tooling supports Source Data Store connectors?
Platforms like Airbyte, Fivetran, Kafka Connect, and cloud-native services provide ready-made connectors.
Category: Tools
How do you validate ingested data from a source?
Employ checksum comparisons, record counts, schema validation, and sampling against source-of-truth queries.
Category: Validation