ETL (extract-transform-load) describes the process of extracting, transforming, and loading data from the ** end to the target side. ETL is more commonly used in data warehouses, but its target is not limited to data warehouses. It can automate the data processing process, reduce manual operations and errors, and improve the reliability and efficiency of data analysis.
Enterprise data sources vary widely in type, format, size, and reliability, so data needs to be processed before it can be used by organizations and users. As a result, ETL data processing is indispensable in businesses. The ETL process can be simply divided into extract, transform, and load, which we will introduce next.
Data extract:Extract data from disparate data sources, including relational databases, unstructured data, log data, and more. This link mainly uses extraction tools such as sqoop, flume, kafka, as well as kettle, datax, maxwell, etc. When extracting data, full synchronization or incremental synchronization is generally used.
Data Transform:Extracted data is cleaned, transformed, and merged to make it suitable for storage in a data warehouse or data lake. Data conversion can also include operations such as data deduplication, format conversion, and data merging to ensure data consistency and accuracy.
Load:Once the extraction transformation is complete, the data is loaded into a data warehouse or data lake for use in business analysis and reporting. There are also two ways to load data: full load and incremental load. In this step, tools such as HBase and HDFS are used.
There are also many ETL tools, ETL-like data integration synchronization tools, or languages. The mainstream ETL tools include sqoop, datax, canal, flume, logstash, kettle, datastage, informatica, talend, etc., and the languages include powerful sql, shell, python, j**a, scala, etc.
RepresentativeetlTools include: sqoop, datax, kettle, canal, informatica, datastage, etc.
sqoop，sql-to-hadoopi.e. "SQL to Hadoop and Hadoop to SQL". It is an apache open source tool for transferring data between Hadoop and relational database servers, and is a common tool in the field of big data.
DataX is a widely used offline data synchronization tool platform within Alibaba Group, which implements efficient data synchronization between various heterogeneous data sources including MySQL, Oracle, SQLSer, Postgre, HDFS, Hive, ADS, HBase, Tablestore (OTS), MaxCompute (ODPS), DRDS, etc. Kettle is a foreign free and open source, visual, powerful ETL tool, written in pure J**A, can run on Windows, Linux, Unix, efficient and stable data extraction. The disadvantage is that it is subject to the use of components in the face of particularly complex business logic.
Canal is an open source project owned by Alibaba, developed purely by J**A. Based on the incremental log parsing of the database, it provides real-time subscription and consumption of incremental data, which mainly supports MySQL and MariaDB. informaticaAn ETL tool that is easy to configure and manage, and enables rapid implementation of ETL tasks. The disadvantages are the same as flume, ** high and takes up a lot of space.
DataStage, an ETL tool developed by IBM, has good cross-platform and data integration capabilities, and provides a visual ETL operation interface. The disadvantage is that it is much higher than other ETL tools, and it requires high system resources and hard disk space.
Nowadays, more and more enterprises have begun to get involved in and attach importance to big data, and major banks, finance, telecommunications, electric power, hospitals, universities and large-scale manufacturing industries are in urgent need of big data talents. ETL development is in the middle and early stage of the whole project process of big data, which is a foundation, and if ETL technology work is done well, it will have twice the result with half the effort.
There will be a large talent gap for ETL development engineers, and ETL engineers will have better career development prospects and huge development space. For example, the IT industry has the highest salary package in the industry, and the starting salary of an ETL big data engineer is much higher than that of other industries.
ETL engineers generally involve the following work contents, and students in related majors need to learn more and learn more in order to be competent in ETL work.
ETL development of massive data and extraction into various data requirements;
Participate in the design and development of data warehouse architecture;
Participate in the optimization of ETL process in data warehouse and solve technical problems related to ETL;
Research and follow-up database development technology, provide data and report support for various business systems, etc.