ETL stands for Extract, Transform & Load and it is a data integration process that combines data from multiple data sources into a single, consistent data store. In addition, the primary purpose of this process is to move and transform data from many different sources and load it into various targets.
Workings Of ETL
ETL consists of three steps that are as follows.
- Extract- Data extraction is the process of copying or exporting data from source locations to a staging area. This source location includes SQL or NoSQL servers, CRM and ERP systems, Flat files, Email, and Web pages. Given below are some of the common data extraction methods.
- Partial extraction with an update notification
- Partial extraction without update notification
- Full extraction
- Note on extractions
- Transform- After extracting the raw data, it goes through data processing. This process includes transforming and consolidating data for its intended analytical use case. In addition, the transform process consists of several tasks like cleaning and standardization, verification and validation, filtering, sorting, and deduplication. Apart from it, given below are some of its advanced functionalities.
- Data audits to ensure data quality and compliance.
- It facilitates Data encryption and protection.
- Useful in performing calculations, and translations on the raw data.
- Facilitates formatting, joining, or splitting data.
- Load- It is the final and last step of the ETL process and it includes loading the transformed data to its final destination. In addition, the destination can be anything from a simple database to a massive data warehouse depending on the size and complexity of the underlying data and the overall organizational requirement. To further know about these processes, one can visit ETL Testing Training in Noida. The load consists of two types of processes that are as follows.
- The full loading processes
- The incremental loading
Benefits Of Using ETL
ETL is highly beneficial for businesses as it provides a standardized process for aggregating and transforming row data. It also helps in storing the transformed data for further analytics. In addition, it helps in data analytics as it introduces an automated data processing pipeline for gathering and formatting data. It is capable of handling big datasets and enables advanced data profiling and cleaning. Moreover, it helps in easily obtaining deep historical context for the organization and facilitates impact analysis. ETL is capable of easily leveraging AI and ML tools and results in increasing the accuracy and effectiveness of the analytical process. Many institutions provide ETL Testing Online Training and one can enroll in them to learn it and start a career in it. Above all, it quickly adapts to changing technological and integration needs.
- It results in increasing Memory efficiency.
- Facilitates real-time query processing.
- Useful in mapping data historical, current, and predictive data.
- Provides smart data storage and retrieval features.
Best ETL Testing Tools in 2022
Given below is a list of the best ETL testing tools in 2022.
- RightData– It is a self-service ETL/Data Integrations testing tool that is useful for business and technology teams in the automation of data quality assurance. In addition, it allows users to perform queries on any data source, explore metadata, analyze data, and discover data.
- iCEDQ- It is a DataOps platform for Data Testing and Monitoring. In addition, it helps in creating rules to test transformations, duplicates, schema, and Type II Dimension. Moreover, it facilitates integration with different enterprise products such as Slack, Jira, ServiceNow, Alation, and Manta.
- Informatica Data Validation- This testing tool is capable of integrating with the PowerCenter Repository and Integration Services. in addition, it allows developers and business analysts to create rules to test the mapped data. Moreover, it comes with an intuitive user interface and built-in operators that are useful in reducing programming efforts.
- QuerySurge- This ETL testing training is developed by RTTS and it helps in automating the testing of Data Warehouses & Big Data. In addition, it helps in improving data quality & data governance along with accelerating your data delivery cycles. Moreover, it facilitates testing across various platforms such as Oracle, Teradata, IBM, Amazon, Cloudera, etc.
- QualiDI- This ETL testing tool helps in achieving higher ROIs and accelerating time to market. In addition, it is capable of managing test cycles through dashboards and reports and facilitates meaningful auto-test data generation using constraints and referential integrity.
Conclusion
ETL is a data integration process that combines data from multiple data sources into a single, consistent data store. It consists of three processes that are Extract, Transforms, and Load. RightData, iCEDQ, QuerySurge, and QualiDI are some of the common ETL tools in the market.
