Data orchestration

Workato offers a powerful and flexible platform for data orchestration (opens new window), designed to streamline your data orchestration processes while maintaining simplicity. As a platform that supports hyper-automation, Workato enables users to accomplish a wide range of tasks while offering a seamless building experience and user interface (UI). This empowers citizen builders to build data orchestrations, without sacrificing on robust data orchestration capabilities.

Workato enables you to build effective data pipelines that can combine and harmonize data from different sources, applications, and systems within your organization, transform the data, and load it to databases or data warehouses to gather insights that can help to better understand your business and customers.

Workato Data orchestration strengths

As a data orchestration platform, Workato has the following strengths.

• LCNC (Low-Code/No-Code)

• Workato is built on a low-code/no-code foundation, enabling users to create powerful data orchestration workflows with minimal coding. This approach provides flexibility without sacrificing simplicity, making it a versatile platform for users with varying technical backgrounds.

• Flexible

• The flexibility of Workato's recipe-based structure allows you to design and execute data orchestration processes tailored to your specific needs. You can customize your workflows and connect with any system, automate complex workflows, or orchestrate data transformations, Workato's flexibility ensures a customizable solution.

• Scalable

• Workato offers bulk actions and triggers that give you the ability to scale and handle large volume data in data orchestration Workflows.

• Reusable components

• Using reusable components such as Recipe functions in your data orchestration pipelines, enables you to build efficient and maintainable data orchestration workflows. This approach reduces the overhead of managing numerous recipes and promotes a more streamlined and organized data orchestration process.

• Observability

• Observability is a key aspect of Workato's data orchestration solution. Leveraging our logging service and job report, users can gain insights into the performance and status data orchestration pipelines. This ensures transparency and facilitates proactive monitoring and issue resolution.

• Performance

• Workato offers high performance through bulk operations and file storage capabilities. These features contribute to the efficient execution of data orchestration tasks, ensuring optimal performance even with large datasets.

ETL/ELT

Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT) are processes used in data orchestration and data warehousing to extract, transform, and load data from various sources into a target destination, such as a data warehouse or a data lake.

Bulk vs Batch

Bulk/Batch actions/triggers are available throughout Workato. Bulk processing gives you the ability to process large amounts of data in a single job, especially suited for ETL/ELT. Batch processing is restricted by batch sizes and memory constraints, and are generally less suitable in the context of ETL/ELT.

Extract, Transform, and Load (ETL)

ETL begins with the extraction phase, where data is sourced from multiple heterogeneous sources, including databases, files, APIs, and web services. This raw data is then subjected to a transformation phase, such as cleaning or filtering before it is loaded into a target system, typically a data warehouse.

Extract, Load, and Transform (ELT)

Similar to ETL, ELT starts with the extraction phase, where data is extracted from various sources. ELT focuses on loading the extracted data into a target system such as a data lake or distributed storage. Once the data is loaded, transformations occur within the target system.