ETL Engine

The ETL (Extract, Transform, and Load) engine is one of the key components used to perform the data transformation in the ETL process. It is responsible for processing data extracted from different data sources, performing operations such as data cleaning, integration, calculation, and formatting according to predefined transformation rules, and finally loading the processed data into the target system (such as the database, and the data warehouse). The ETL engine is the core technical component of the ETL process. Through its powerful data processing capabilities and functional characteristics, the ETL engine can realize the full-process data management and processing from multi-source data extraction and transformation to loading data into the target system.

ETL Basic Concepts - Process

ETL processing is divided into five modules: data extraction, data cleaning, in-database transformation, rule checking, and data loading Each module can be flexibly combined to form an ETL processing flow.

Data Extraction

• Determine the data source, and determine the source systems used to extract data.

• Define the data interface, and explain each source file and each field of the system in detail.

• Determine the data extraction method: Is it extracted actively or pushed by the source system? Is it incremental extraction or full extraction? Is it extracted daily or monthly? 

Data Cleaning and Transformation

• Data cleaning: mainly includes incomplete data processing, erroneous data processing, and duplicate data prosessing

• Data transformation: null value processing, data standards, data splitting, data verification, data replacement, and data association

Data Loading

Load the data in the data buffer directly into the corresponding tables of the database. Use the Load method for full loading. Merge the data into the database according to business rules if incremental loading is used.

Data Cleaning and Validation

Clean the data extracted from the source system to ensure the accuracy and consistency of the data. This may include removing duplicate data, handling missing values, and unifying data formats.

Data Transformation

Transform and calculate the data according to predefined business logic and transformation rules. For example, data formatting, calculation of derived fields, and mathematical or logical operations can be performed.

Data Mapping and Reconstruction

Mapping data from different data sources to the data model of the target system. This may involve restructuring the data structure to adapt to the data model of the target system.

Performance Optimization

When processing large amounts of data, the ETL engine needs effective performance and processing capabilities to ensure the speed and efficiency of data transformation and loading.

Task Scheduling and Monitoring

Manage and monitor the execution of ETL tasks, including scheduling the execution time of tasks, error handling when tasks fail, and real-time monitoring of task execution status.

Fault Tolerance and Recovery Capability

During the processing, the ETL engine needs a fault tolerance mechanism to deal with emergencies or exceptions and ensure the stability and integrity of data processing.

Scalability and Flexibility

The ETL engine supports multiple data sources and target systems, as well as flexible configurations and customized requirements to meet the data processing requirements in different business scenarios.

The ELT process is different:

• Extraction: Collect raw data from different sources (same as that of ETL).

• Load: Load the raw data directly into the target database without preprocessing.

• Transform: Transform and process the data after the data has been loaded into the target database.

The main difference between ETL and ELT is whether the transformation step is performed before or after the data is loaded. ETL is suitable for situations where the data volume is small and the transformation logic is complex, while ELT is suitable for situations where the data volume is large and the powerful processing capabilities of the database are needed to process the data. With the development of big data and cloud computing technology, ELT has become increasingly popular because it can process large-scale data more efficiently.

When choosing between ETL and ELT, you need to consider the size, diversity, real-time requirements of the data, and the processing capabilities of the target database. For example, for traditional data warehouses that require a lot of data transformation and cleaning, ETL may be a better choice. For big data scenarios that require fast data loading and subsequent processing using the powerful processing capabilities of the database, ELT may be more appropriate.