Picking a tool to prepare and clean data before it lands

Google Cloud offers multiple tools for preparing and cleaning data depending on whether you need code-based SQL transformation, code-based ETL/ELT pipelines, or visual pipeline design. Dataform handles SQL-based transformation and testing within BigQuery, Dataflow provides unified batch/streaming data processing using Apache Beam, and Cloud Data Fusion offers a visual, no-code/low-code interface for building ETL/ELT pipelines using pre-built connectors and transformations. Generative AI features in BigQuery and other tools can now assist by prompting LLMs to generate or explain SQL queries, accelerating data cleaning and transformation logic development.

1 · Learn the must-know

• 1Dataform manages SQL-based ELT workflows for BigQuery, supporting version control, dependency management, and built-in data quality assertions/tests for validating cleaned data.
• 2Dataflow is a fully managed service based on Apache Beam that supports both batch and streaming data processing, making it ideal for complex transformations, deduplication, and schema normalization at scale.
• 3Cloud Data Fusion provides a graphical interface for building data pipelines using pre-built plugins and transformations, making it suitable for users who prefer visual ETL/ELT development over writing code, and it runs on Dataproc under the hood.
• 4Dataflow supports Apache Beam SQL and can integrate with Dataflow templates (including Google-provided ones) for common data cleaning tasks like masking PII or converting formats.
• 5BigQuery’s generative AI capabilities (e.g., Duet AI/Gemini in BigQuery) allow prompting an LLM in natural language to generate, explain, or debug SQL queries, which can speed up writing data cleaning and transformation logic but should be reviewed for correctness before production use.
• 6Choosing between these tools often depends on team skillset and workload: Dataform suits SQL-first analytics engineers, Dataflow suits engineers needing custom, scalable batch/streaming logic, and Data Fusion suits teams wanting a low-code, connector-rich pipeline builder.

2 · Check your understanding