Associate Data Practitioner

Select a data transformation tool (e.g., Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform) based on business requirements

When building a data pipeline, it is important to choose the right tool for your needs. Data transformation tools help convert raw data into a usable format. You should look at factors such as scale, cost, and complexity when deciding which service to use.

Dataproc and Dataflow are popular for large-scale batch and streaming jobs. Dataproc runs Apache Hadoop and Spark clusters, making it ideal when you already use those frameworks. Dataflow offers a fully managed service for both streaming and batch, removing the need to manage servers.

Cloud Data Fusion provides a visual interface for building pipelines without writing code. It works well for teams that prefer a drag-and-drop experience. Cloud Composer uses Apache Airflow to schedule and orchestrate workflows, which is helpful when you need complex dependencies.

Dataform is designed for SQL-based transformations in a warehouse. It allows developers to manage tables and views with code. When selecting a tool, consider:

• Data volume you need to process
• Skill set of your team
• Integration with existing systems

By matching these factors to each service’s strengths, you can design an efficient pipeline that meets your business requirements.

Evaluate use cases for ELT and ETL

Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are two common approaches to moving data. In ETL, data is transformed before loading into a target system. In ELT, data is loaded first and then transformed inside the destination.

ETL is useful when you need to clean and shape data before it lands in your data store. It is common in traditional data warehouses where storage is more expensive than compute. With ETL, only refined data is stored, which can save space.

ELT shines when using modern cloud warehouses that offer cheap storage and powerful compute. By loading raw data first, you can run multiple transformations later. This approach supports ad hoc analysis and lets you keep the original data for future use.

To decide between the two, consider:

• Data processing needs: real-time versus batch
• Infrastructure costs: compute versus storage
• Data exploration: do you need to keep raw data?

Evaluating these factors will help you pick the best pattern for your pipeline.

Choose products required to implement basic transformation pipelines

A basic data pipeline involves several key stages: ingestion, storage, transformation, and loading. Each stage can use different GCP products based on your needs. Selecting the right combination ensures a smooth flow of data.

For ingestion, you might use Cloud Pub/Sub for real-time streams or Cloud Storage for batch files. These services can handle large volumes and integrate with other GCP tools. They also provide built-in durability to keep your data safe.

For transformation, consider Dataflow, Dataproc, or Cloud Data Fusion. Dataflow works with both streaming and batch, while Dataproc is great for Spark and Hadoop workloads. Cloud Data Fusion offers a low-code interface that speeds up development.

To orchestrate the pipeline, you can use Cloud Composer or Dataform. Composer schedules and manages complex workflows with Airflow. Dataform handles SQL-based transformations inside your data warehouse. By combining ingestion, transformation, and orchestration tools, you can build a pipeline that meets your performance and maintenance needs.

Conclusion

In summary, designing simple data pipelines on GCP involves three main steps. First, selecting the right transformation tool means weighing factors like scale, cost, and team skills. Second, evaluating ETL versus ELT helps you choose the best data flow pattern for your storage and processing needs. Third, choosing core products—from ingestion to orchestration—ensures each stage works together smoothly. By understanding these concepts, you can build reliable and efficient pipelines that meet your business requirements.