1. Introduction to ETL
The ETL process is the core part of data warehouse construction. It involves extracting data from various data sources, cleaning, converting and integrating it, and finally loading it into the data warehouse for analysis and decision-making. In the context of data warehouse localization, the ETL process plays an important role. Today we will talk about the concept and design method of the ETL process.
1. Data extraction (Extract )
Data extraction is the first step in the ETL process, which involves extracting data from various data sources and extracting the data from the source system to prepare for subsequent processing. Data sources can be of various types, which are divided into structured data, semi-structured data and unstructured data, including relational databases, files (such as CSV, Excel, JSON, etc.), APIs, log files, etc. Data extraction can be divided into the following methods under different data source structures:
- Structured data: Extract data records from structured data sources such as relational databases, tables, and CSV files using SQL queries or API calls; use incremental extraction or CDC technology to extract only changed or new data. to improve efficiency and real-time performance.
(2 ) Unstructured or semi-structured data: Extract valuable information from unstructured data sources such as text files, logs, images, audio, and videos with appropriate parsing technology; use text mining, image processing, Technologies such as speech recognition convert unstructured data into structured or semi-structured form.
In terms of data extraction methods, the following methods can generally be used:
(1 ) Full Extraction : Extract all the data in the source system at one time. It is suitable for situations where the amount of data is small and the changes are small, such as initial data loading.
(2 ) Incremental Extraction : Only the data that has changed in the source system is extracted. Timestamps or incremental tags are usually used to identify new or modified data, and are generally used for data updates.
(3 ) Incremental extraction + log tracking (Change Data Capture , CDC ): Use log tracking technology in the database to monitor changes in the database in real time and extract the changed data to ensure the real-time nature of the data.
2. Data conversion (Transform )
Data conversion is the core part of the ETL process, which involves cleaning, integrating and converting the extracted data to adapt it to the needs of target storage and analysis. The data conversion methods of different structures are also different:
(1 ) Structured data: The conversion method mainly involves data cleaning, removing duplicate values, processing missing data, ensuring data consistency and accuracy, and performing operations such as connection, merging, and filtering of relational data to integrate data from different sources. data, etc.;
(2 ) Unstructured data: The conversion method mainly involves natural language processing of text data, such as word segmentation, entity recognition, sentiment analysis, etc., to extract key information of text content and convert unstructured data into suitable Structured formats for storage and analysis, such as converting text into tabular form, etc.
Data conversion includes the following main steps:
(1 ) Data cleaning: Data cleaning is to deal with anomalies, missing or errors in the data and ensure the accuracy and consistency of the data. This may involve removing duplicate values, filling in missing values, correcting formatting issues, etc.
(2 ) Data integration: If data comes from multiple source systems, data integration may be needed to merge data from different sources and eliminate duplicates to obtain a more comprehensive view.
(3 ) Data conversion and calculation: In this step, the data can be subjected to mathematical calculations, logical operations, date processing and other operations to generate new derived data or indicators. For example, calculate sales, calculate growth rate, etc.
(4 ) Data formatting: Converting data into the target storage format may involve reorganizing the data structure, adjusting data types, etc.
(5 ) Data standardization: Unify the representation of data values to ensure data consistency and comparability. For example, convert region names to standard region codes.
3. Data loading (Load )
Data loading is the final step in the ETL process, which loads the extracted and transformed data into a target storage, usually a data warehouse or data lake. Data loading can be divided into the following methods:
(1 ) Full Load : Load all processed data into the target storage at one time, suitable for initial loading or when the amount of data is small.
(2 ) Incremental Load : Only load data that has changed after extraction and conversion to ensure the real-time nature and efficiency of the data.
(3 ) Transactional loading: Use the transaction mechanism of the database to ensure the integrity of data loading, that is, either all loading is successful, or it is rolled back to the state before loading.
(4 ) Batch loading and streaming loading: Batch loading is suitable for large-scale data processing, while streaming loading is suitable for scenarios that require real-time data analysis.
Whether processing structured or unstructured data, the core goal of the ETL process is to transform raw data into valuable data that can be used for analysis, reporting, and decision-making. Different data types require different extraction, transformation, and loading operations based on their characteristics to ensure data quality and availability.
2. Recommendation of free ETL tools
According to different data sources, data warehouse ETL tools can be divided into structured data ETL tools and unstructured/semi-structured data ETL tools. The following are several free ETL tools that are worth recommending after trial.
1. Kettle
Kettle is a free foreign open source ETL tool that is widely used. It is currently the most powerful open source ETL tool on the market. Kettle can be used for data extraction, conversion and loading to achieve rapid data warehousing and analysis. Let’s briefly talk about the advantages and disadvantages of Kettle:
advantage:
( 1 ) Provides an intuitive graphical user interface. Users can build a data integration process by dragging, dropping and connecting conversion steps. This visual development method makes it easy for non-technical personnel to get started and speeds up development efficiency.
( 2 ) Kettle provides a wealth of conversion steps and functions, allowing users to clean, filter, convert and merge data. It supports various data processing technologies, including string operations, date processing, aggregation calculations, and conditional judgments. etc. to meet complex data conversion needs.
shortcoming:
- It is difficult to learn and get started. For novices, Kettle may take some time to understand its concepts and operation methods. Especially when dealing with complex data conversion logic, certain knowledge of data processing and programming is required.
- Documentation support is limited. Compared with some other domestic ETL tools, Kettle has a large number of domestic users, but its Chinese documentation and technical support are relatively limited. This can lead to more self-study and research when encountering a problem.
(3) It does not support the CDC real-time data collection function and can only rely on accelerating the scheduling frequency of tasks such as 1 minute to achieve real-time data transmission. If the amount of data is relatively large, it will cause great pressure on the production system.
Use the interface diagram:
(As open source software, Kettle can be downloaded directly from the official website)
2. AirByte ：
airbyte is the latest open source data integration software that synchronizes data from applications, APIs and databases to data warehouses, data lakes and other destinations. It supports 200 Source type connectors and 100 Destination type connectors.
(AirByte ’s linker interface )
( Data synchronization monitoring interface )
- ETLCloud
It is a domestic data integration platform that can realize real-time data synchronization, offline data processing, and comprehensive process monitoring. Compared with other foreign ETL tools , it is easier to use. ETLCloud is divided into community version and commercial paid version. The community version is free to use. . Let’s briefly talk about its advantages and disadvantages:
Advantages:
( 1 ) Powerful data support function: It can connect to databases, upper-layer general protocols, message queues, files, platform systems, applications and other types of data sources to provide enterprises with a complete set of data integration and analysis solutions.
( 2 ) Supports CDC real-time data collection capabilities, high synchronization efficiency, and detailed monitoring reports during the data synchronization process.
( 3 ) It provides an intuitive WEB visual configuration interface and a unified operation and maintenance platform. It is a localized self-developed data integration product.
( 4 ) The community free version has a large user group, comprehensive technical documentation, and a rich component market to quickly connect with SASS applications.
shortcoming:
- The community free version does not support some functions and requires the enterprise version to use it.
Use the interface diagram:
Process Design:
(Process design interface)
( Task monitoring running interface)
4.DataX
DataX is an offline synchronization tool for heterogeneous data sources open sourced by Alibaba. As an ETL tool that serves big data (actually it can be regarded as an ELT tool), in addition to providing data snapshot relocation functions, it also provides rich data conversion functions and can provide stable and efficient data synchronization functions. Let’s briefly summarize Talk about its advantages and disadvantages.
advantage:
( 1 ) Supports multiple data sources and data targets, and is easy to access.
( 2 ) Supports high-speed data transmission and is suitable for large-scale data processing scenarios.
( 3 ) High degree of customization, supporting user-defined development.
shortcoming:
- DataX But DataX performs tasks in the form of a script. It requires a complete understanding of the source code before it can be called, and the learning cost is high.
- It lacks a user-friendly interface, requires writing scripts for configuration, and has insufficient visual monitoring and data tracking capabilities. Operation and maintenance costs are relatively high.
Use the interface diagram:
3. Summary
This article introduces what ETL is , analyzes the role and importance of ETL in big data processing, and shares the application scenarios and applicability of ETL . It should be noted that the advantages and disadvantages of the above ETL tools are for reference only, and specific evaluations need to be comprehensively considered based on actual needs and usage. It is recommended that when selecting ETL tools, you should conduct a comprehensive evaluation and comparison based on your own business needs to choose the most suitable tool.