Extract, Transform, Load (ETL) Task
An Extract, Transform, Load (ETL) Task is a data processing task that involves extracting data from source systems, transforming it to fit operational needs, and loading it into a target database.
- AKA: ETL Process, Data Integration Task, Data Pipeline Task.
- Context:
- Task Input: Source Data, Transformation Rules
- Task Output: Loaded Data, ETL Logs
- Task Performance Measure: ETL Performance Metrics such as data throughput, transformation latency, and load time
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- It can typically be solved by an ETL System (that possibly is based on an ETL platform).
- It can typically extract Source Data from source systems through extraction methods.
- It can typically transform Source Data into Target Data Structure through transformation operations.
- It can typically load Transformed Data into target databases through loading mechanisms.
- It can typically validate Extracted Data using data quality checks.
- It can typically cleanse Source Data by removing data inconsistency and data redundancy.
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- It can often handle Batch Processing for large volume data transfers.
- It can often maintain Data Lineage for audit purposes.
- It can often implement Error Handling for failed transformations.
- It can often provide Job Scheduling for automated executions.
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- It can range from being a Simple ETL Task to being a Complex ETL Task, depending on its data complexity.
- It can range from being a Batch ETL Task to being a Real-Time ETL Task, depending on its processing frequency.
- It can range from being a Homogeneous ETL Task to being a Heterogeneous ETL Task, depending on its source system diversity.
- It can range from being a Manual ETL Task to being an Automated ETL Task, depending on its execution method.
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- It can have ETL Subtasks for modular processing.
- It can provide Transformation Logs for data lineage tracking.
- It can perform Data Profiling for source data understanding.
- It can support Incremental Processing for efficiency optimization.
- It can implement Data Partitioning for workload distribution.
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- Examples:
- ETL Task Types, such as:
- Operational ETL Tasks, such as:
- Analytical ETL Tasks, such as:
- ETL Task Complexitys, such as:
- ETL Task Frequencys, such as:
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- ETL Task Types, such as:
- Counter-Examples:
- Extract, Load, Transform (ELT) Task, which performs transformation after data loading rather than before it.
- Data Streaming Task, which processes data in real-time continuous flows rather than in scheduled batches.
- Data Migration Task, which focuses on one-time movement of data rather than recurring data processing.
- Data Replication Task, which creates exact copies of data without transformation logic.
- Data Synchronization Task, which ensures data consistency across systems without necessarily performing complex transformations.
- See: Data Warehouse, Data Mart, Operational Data Store, ETL System, ETL Platform, Data Pipeline.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Extract,_transform,_load Retrieved:2017-11-8.
- In computing, extract, transform, load (ETL) refers to a process in database usage and especially in data warehousing. The ETL process became a popular concept in the 1970s. [1] Data extraction is where data is extracted from homogeneous or heterogeneous data sources; data transformation where the data is transformed for storing in the proper format or structure for the purposes of querying and analysis; data loading where the data is loaded into the final target database, more specifically, an operational data store, data mart, or data warehouse.
Since the data extraction takes time, it is common to execute the three phases in parallel. While the data is being extracted, another transformation process executes while processing the data already received and prepares it for loading while the data loading begins without waiting for the completion of the previous phases.
ETL systems commonly integrate data from multiple applications (systems), typically developed and supported by different vendors or hosted on separate computer hardware. The separate systems containing the original data are frequently managed and operated by different employees. For example, a cost accounting system may combine data from payroll, sales, and purchasing.
- In computing, extract, transform, load (ETL) refers to a process in database usage and especially in data warehousing. The ETL process became a popular concept in the 1970s. [1] Data extraction is where data is extracted from homogeneous or heterogeneous data sources; data transformation where the data is transformed for storing in the proper format or structure for the purposes of querying and analysis; data loading where the data is loaded into the final target database, more specifically, an operational data store, data mart, or data warehouse.