Datamerge

Datamerge is the process of combining data from two or more sources into a single dataset. It is a fundamental operation in data integration, analytics, and reporting, enabling a unified view of information across systems.

Merging can be horizontal (combining columns based on a key) or vertical (appending rows when schemas align). Key-based merges use identifiers to align records across sources. When keys do not perfectly match, techniques such as fuzzy matching or probabilistic record linkage may be used. Schema reconciliation resolves differences in column names, data types, and units.

Common methods include SQL joins (inner, left, right, full outer), as well as programmatic merges in data processing frameworks such as pandas, Spark, or data integration tools. Datasets may be merged in ETL or ELT pipelines, and may also be joined with time-based as-of merges for time series.

Key considerations include data quality, deduplication, handling conflicts (which source wins), missing values, data provenance, and governance. Performance and scalability matter for large datasets, as do privacy and compliance when merging sensitive data.

Applications include consolidating customer data from multiple systems, combining experimental results with metadata in research, or integrating sensor streams in IoT environments. Datamerges are often iterative, supporting data cleansing and enrichment as new sources or revisions become available.