A data analyst needs to prepare a raw customer dataset stored in Cloud Storage before loading it into BigQuery for reporting. The dataset contains several data quality issues, including leading and trailing whitespace, inconsistent date formats, and missing values. The analyst needs a visual, code-free interface within Google Cloud to inspect sample rows, identify anomalies, interactively apply data cleaning directives, and convert the resulting recipe into an automated pipeline. Which component of Cloud Data Fusion should the analyst use?

Wrangler: ### **What Cloud Data Fusion Wrangler is** **Wrangler** is the interactive visual data preparation and data cleansing interface built directly into **Cloud Data Fusion**. It is designed to allow data analysts and data engineers to visually inspect, clean, and transform structured and semi-structured datasets without needing to write custom code or programming scripts. ### **How it meets the requirements** - **Visual data exploration (✓):** Wrangler loads a representative data sample into an intuitive, spreadsheet-like interface where users can examine column distributions, identify data types, and spot anomalies such as null values or irregular formats. - **Code-free cleaning directives (✓):** Users can apply transformation directives (such as trimming whitespace, parsing dates, filtering records, and standardizing text) through simple dropdown menus or point-and-click actions. - **Direct pipeline generation (✓):** Once the data preparation recipe is defined, Wrangler allows users to export these directives directly into a **Cloud Data Fusion Studio** pipeline, turning interactive preparation into an automated, scalable data processing job. ### **Key advantages and capabilities** - **Interactive feedback:** Displays immediate visual previews of transformations on sample data before deploying full-scale pipelines. - **Extensive directive library:** Provides over 40 built-in transformation directives for string manipulation, date parsing, column splitting, and type conversion. - **Auditability and reproducibility:** Tracks all applied transformation steps as a sequential recipe that can be saved, edited, or reused across different datasets. - **Seamless Studio integration:** Converts cleansing workflows directly into pipeline source and transformation nodes with a single click. ### **Why this is optimal** Wrangler is the purpose-built visual tool in Google Cloud for interactive data cleansing and rapid recipe creation. It removes the need to write complex SQL, Python, or Java scripts for common data sanitization tasks, making data preparation accessible to both technical and non-technical team members.