🔁Incremental Refresh

NEW

Also known as: Incremental Load, Delta Refresh

Updating datasets or tables by processing only newly added or changed records, often via partitions or watermarks.

DESCRIPTION

Incremental refresh avoids full reloads by operating on recent partitions or changes detected via CDC, timestamps, or watermarks. It reduces compute, cost, and latency, enabling near-real-time analytics. BI tools and warehouses implement policies to refresh only active windows while retaining historical data. Careful key selection, idempotency, and late-arriving data handling are vital. Monitoring ensures correctness and identifies drift or gaps.

ALTERNATIVE DEFINITIONS

Microsoft Power BI

Incremental refresh enables large datasets to be refreshed efficiently by refreshing only data that has changed.

Highlights efficiency gains for large BI datasets.

Source

dbt Labs

An incremental model is a table that updates only rows that are new or changed, rather than rebuilding the entire table.

Defines ELT materialization behavior in analytics engineering.

Source

Google Cloud

Partitioned tables organize data by time or other fields, enabling efficient queries and incremental loading of new data.

Connects incremental operations to partitioned storage.

Source

RELATED TERMS

Materialized View Partitioning Change Data Capture Streaming Ingestion Extract-Transform-Load ELT Watermarking Windowing Delta Loads Data Incrementality

COMPETING TERMS

Full Refresh Table Rebuild

KNOWLEDGE AREAS

Data Warehousing & BI Data Engineering Data Integration & Interoperability Observability & Telemetry

TARGET AUDIENCE

BI Specialist Analytics Engineer Data Engineer Data Architect Application Administrator

EXTERNAL SOURCES

• Wikipedia - Materialized view
• Microsoft Power BI - Power BI incremental refresh guidance
• dbt Labs - dbt incremental models documentation
• Google Cloud - BigQuery partitioned tables for incremental loads

TOP QUESTIONS

Why use Incremental Refresh in analytics?

It lowers costs and accelerates updates by processing only recent changes.

Category: Relevancy

How is Incremental Refresh implemented?

Use partitions, timestamps, watermarks, or CDC to identify new or changed records.

Category: Requirements

What mistakes occur with Incremental Refresh?

Incorrect keys or watermark logic cause missed or duplicated rows and data drift.

Category: Mistakes

What risks are associated with Incremental Refresh?

Late-arriving data and schema changes can break assumptions, leading to partial updates.

Category: Risks

How to monitor Incremental Refresh?

Track partition counts, row deltas, error logs, and freshness SLIs/SLOs.

Category: Management

What are alternatives to Incremental Refresh?

Full refresh or periodic rebuild trades efficiency for simplicity and determinism.

Category: Alternatives

How does dbt support Incremental Refresh?

dbt incremental models update new or changed rows using user-defined merge strategies.

Category: Example

How to handle deletions in Incremental Refresh?

Use soft-delete flags or change tables and merge logic to reconcile deletions.

Category: Requirements

What is a debated topic about Incremental Refresh?

Choosing batch windows vs. streaming micro-batches for freshness and cost is debated.

Category: Opinions