Let's start with an introduction to Cloud Data Fusion. Cloud Data Fusion provides a graphical user interface and APIs that increased time efficiency and reduce complexity. It equips business users, developers, and data scientists to quickly and easily build, deploy, and manage data integration pipelines. Cloud Data Fusion is essentially a graphical no-code tool to build data pipelines. Cloud Data Fusion is used by developers, data scientists, and business analysts alike. For developers, Cloud Data Fusion allows you to cleanse, match, remove duplicates, blend, transform, partition, transfer, standardize, automate, and monitor data. Data scientists can use Cloud Data Fusion to visually build integration pipelines, test, debug, and deploy applications. Business analysts can run Cloud Data Fusion at scale on Google Cloud, operationalized pipelines, and inspect rich integration metadata. Cloud Data Fusion offers a number of benefits. Integrate with any data through a rich ecosystem of connectors for a variety of legacy and modern systems, relational databases, file systems, cloud services, object stores, NoSQL, EBCDIC and more. Increased productivity. If you have to constantly move between numerous systems to gather insight, your productivity is significantly reduced. With Cloud Data Fusion, your data from all the different sources can be pulled into a view like in BigQuery, Cloud Spanner, or any other Google Cloud technologies, allowing you to be more productive faster. Reduce complexity through a visual interface for building data pipelines, code free transformations, and reusable pipeline templates. Increased flexibility, through support for on-prem and cloud environments. Interoperability with the open-source software, CDAP. At a high level, Cloud Data Fusion provides you with a graphical user interface to build data pipelines with no code. You can use existing templates, connectors to Google Cloud, and other Cloud services providers, and an entire library of transformations to help you get your data in the format and quality you want. Also, you can test and debug the pipeline and follow along with each node as it receives and processes data. As you will see in the next lesson, you can tag pipelines to help organize them more efficiently for your team, and you can use the unified search functionality to quickly find field values or other keywords across your pipelines and schemas. Lastly, we'll talk about how Cloud Data Fusion tracks the lineage of transformations that happen before and after any given field on your dataset. One of the advantages of Cloud Data Fusion is that it's extensible. This includes the ability to templatized pipelines, create conditional triggers, and manage and templatized plugins. There is a UI widget plug-in as well as custom provisioners, custom compute profiles, and the ability to integrate to hubs.
