The ETL automation tool designed for hybrid IT

Extract, transform, load (ETL)is a part of the data testing process and is used to move and transform data between different data stores, such as databases, data warehouses and data lakes.

In an ETL process, data is first extracted from various sources, such as operational databases, flat files or APIs.

The extracted data is then transformed into a format that is suitable for the target data store, such as by filtering, aggregating or joining the data.

Finally, the transformed data is loaded into the target data store, such as a data warehouse or data lake, where it can be used for analytical or reporting purposes.

Data validation is an important step within the transform phase of ETL, where the data is checked to ensure that it conforms to specific rules or the quality of the transformed data.

ETL is heavily dependent on the quality and integrity of the source data and requires careful handling and preparation to ensure the success of the transformation and loading processes. Learn more about Redwood’s data automation software and testing solution.

ETL automation refers to the methods and tools used to extract, transform and load data without human intervention. ETL automation has grown in importance as data processes have become more complex and businesses are more dependent on disparate data sets.

Cloud-native workload automation solutions such as RunMyJobs by Redwood offer low-code ETL automation and data orchestration. Automate your entire data pipeline with Redwood.

ETL tools are specialized software solutions designed to handle the complex data processing needs of large organizations. These tools extract data from various sources, transform it to meet specific business or technical requirements and load it into a target system. ETL tools are essential for data integration because they ensure data from disparate systems is clean, consistent and ready for data analytics.

In large enterprises, ETL use cases include handling high volumes of data from multiple, diverse sources, including relational databases, CRMs, ERPs, financial applications and more. Enterprise ETL tools typically offer features like automation, error handling and data validation. They also handle metadata management, which is critical for tracking information about data, such as its source, transformations and loading history.

These solutions often come with pre-built connectors for popular enterprise applications (like Salesforce, SAP or Oracle) and can manage complex data workflows. Some advanced ETL tools also support real-time data processing and can be part of larger data pipeline automation strategies, helping businesses drive actionable insights faster. Well-known ETL tools include Informatica, Talend, Apache NiFi and Microsoft SQL Server Integration Services (SSIS).

ETL testing tools also play a critical role in allowing testers and administrators to verify the integrity of a data pipeline and, therefore, data accuracy. These tools verify that data is correctly extracted, transformed and loaded without loss or corruption. ETL testing tools can automate testing processes, validate data transformations, ensure data consistency across different systems and flag any discrepancies or errors. This is particularly important when engaging in regression testing or working with relational databases, as the complexity of schema changes and data relationships must be rigorously tested.

Enterprise resource planning (ERP) and extract, transform, load (ETL) serve different functions within an organization’s IT ecosystem, though they often interact.

ERP is a type of business management software that integrates various business functions, such as finance, human resources, supply chain, manufacturing and procurement, into a unified system. It helps organizations manage their day-to-day operations by providing a centralized platform for data entry, storage and analysis. Examples of ERP systems include SAP, Oracle ERP and Microsoft Dynamics. The goal of an ERP system is to streamline operations, improve efficiency and provide real-time visibility into key business processes.

ETL is a process and set of tools used for data integration. It moves data from multiple source systems and consolidates it for further analysis, often in a data warehouse or cloud storage solution. The ETL process extracts data from databases, applications or flat files, transforms it into a usable format by cleaning and validating it and then loads it into a target system for reporting or analytics. ETL is critical for maintaining clean, consistent and accurate data across an enterprise.