Data integration means combining two or more pieces of information. Today’s data engineers create and manage data pipelines that connect data from sources—such as files or databases—to targets such as cloud data platforms. They send data through those pipelines to feed operations and analytics. Finance, sales, and marketing applications all consume data, as do analytics projects that range from business intelligence (BI) to machine learning (ML) and other types of advanced analytics. Data integration has four components. First is data ingestion, which extracts batches or increments of data (as well as their schema and metadata) from a source, then loads data to the target. Second, transformation combines, formats, structures, and cleanses data. It might also import or create data models. Third, data engineers manage their environments by designing, developing, testing, and deploying the data pipelines that ingest and transform data. They also monitor, tune, and reconfigure those pipelines. The final component, control, refers to tasks such as provisioning, version control, workflow orchestration, lineage, and documentation.