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Extract, transform, load (ETL) design and development is the design of some of the heavy procedures in the data warehouse and business intelligence system. Kimball et al. suggests four parts to this process, which are further divided into 34 subsystems [3]: Extracting data; Cleaning and conforming data; Delivering data for presentation
Data warehousing procedures usually subdivide a big ETL process into smaller pieces running sequentially or in parallel. To keep track of data flows, it makes sense to tag each data row with "row_id", and tag each piece of the process with "run_id". In case of a failure, having these IDs help to roll back and rerun the failed piece.
Operational systems maintain a snapshot of the business, while warehouses maintain historic data through ETL processes that periodically migrate data from the operational systems to the warehouse. Online analytical processing (OLAP) is characterized by a low rate of transactions and complex queries that involve aggregations. Response time is an ...
In the data warehouse practice of extract, transform, load (ETL), an early fact or early-arriving fact, [1] also known as late-arriving dimension or late-arriving data, [2] denotes the detection of a dimensional natural key during fact table source loading, prior to the assignment of a corresponding primary key or surrogate key in the dimension table.
Master data management; Reference data; Data integration and inter-operability Data movement (ETL, ELT) Data interoperability; Documents and content Document management system; Content management; Records management; Data warehousing and business intelligence and analytics Business intelligence; Data analysis and data mining; Data warehouse and ...
According to AHIMA's Data Quality Management Model, there are four key processes for data: Application: the purpose for which the data are collected. Collection: the processes by which data elements are accumulated. Warehousing: the processes and systems used to store and maintain data and data journals.
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed.
The data warehouse approach is less feasible for data sets that are frequently updated, requiring the extract, transform, load (ETL) process to be continuously re-executed for synchronization. Difficulties also arise in constructing data warehouses when one has only a query interface to summary data sources and no access to the full data.