Data is collected from various sources in the pipeline, including relational databases and SaaS applications. In most cases, data is ingested into the pipeline via a push mechanism, an API call, or a webhook. The data may be synchronized in real-time or at predefined intervals. After ingesting the raw data, pipelines perform transformation operations, such as deduplication and standardization, and then load the results into a data warehouse or BI application.
For example, data that is stored in a pipeline’s PODS database is organized by pipe segment. Every record in the database is associated with a pipeline segment. A data pipeline can track the history of important data records, such as engineering analyses or regulatory reports. Using this system, an organization can integrate and analyze the information in a single, easy-to-use interface. Then, it can access all of the necessary information about its assets in a single place.
Once the data is extracted from the pipeline, it needs to be transformed and cleansed before it is ready to be analyzed. Data quality includes conformance to master data and validation of data types. It can also be enriched with reference data and additional fields. After obtaining the pipeline data, the information is stored in various points in the pipeline. The records are stored in the landing zone and in a structured store, such as a data warehouse.