Data orchestration is the process of managing and coordinating data from multiple sources, combining and organizing it so that it can be analyzed. It harmonizes disparate data sources, providing businesses with a unified view of their data and facilitating more informed decision-making. Data orchestration differs from ETL (extract, transform, load) in its scope, timing, flexibility, and automation capabilities. By automating workflows and integrating various data pipelines, data orchestration supports real-time or near-real-time data processing and is crucial for businesses that require timely insights from their data. The benefits of data orchestration include improved data governance, reduced roadblocks, reduced costs, faster time to insights, and enhanced scalability. However, challenges such as data silos, issues with data quality, and data misalignment can hinder the effectiveness of data orchestration. To overcome these challenges, businesses must adopt a holistic approach to data management, ensure high data quality, standardize data formats and conventions, and implement automated validation tools. Data orchestration workflow typically involves five steps: data collection and organization, data transformation, data integration, data validation, and data analysis and visualization. Various data orchestration tools are available, including Metaflow, Stitch, Apache Airflow, Prefect, Luigi, and CData Connect Cloud, which can help simplify and streamline the process.