Modern data teams need a reliable platform to efficiently handle big data volumes. Fortunately, there are three popular services available on the Google Cloud Platform, namely: Dataform, Google Cloud Dataprep, and Cloud Data Fusion that does it well. They complement each other in data pipeline construction but differ in functionalities and working.
Data Fusion focuses on data ingestion and integration, Dataprep on data cleaning and data set preparation, and Dataform on SQL query-based transformation and orchestration directly in BigQuery.
Understanding each can help the organization choose the right service to accomplish its data pipeline goals more effectively. This blog explains the differences between Data Form, Dataprep, and Data Fusion to help you make a wise choice.
What is Dataform?
Data Form is Google Cloud’s SQL-based environment for developing, testing, and orchestrating data pipelines directly in BigQuery using SQL. It allows data analysts and data engineers to apply various practices, such as creating table definitions, adding column descriptions, configuring dependencies, and more, in a single repository using SQL.
You don’t need to change the web browser while performing the actions. Users can also connect repositories with GitHub and GitLab. Alongside, address issues with real-time error messages, showcase dependencies from a single interface.
Advantages:
- A serverless solution that cuts down the burden on your DevOps team.
- Supports, testing, documentation, and version control
- Has an intuitive interface, mainly for those working with SQL
- Can be integrated with GitHub and GitLab
Use Cases:
- Companies standardizing on BigQuery
- Teams that require SQL transformation
- Testing and documentation of datasets for maintaining reliability.
What is Dataprep?
Google Cloud Dataprep is an intelligent data-wrangling service that focuses on cleaning and preparing structured and unstructured data before analysis. This is an effective tool for data professionals to explore, clean, and transform datasets, with an easy drag-and-drop workflow rather than writing complicated code. Trifacta powers it. There is also an integration of AI-driven features to help you boost your wrangling experience.
Advantages:
- No-code interface
- Efficiently handles semi-structured data
- Offers suggestions for data cleaning
- Prepared data can be exported easily to Google Cloud Services
Use Cases:
- Teams that need data cleaning without coding
- Analysts working on preparing raw data for analytics
What is Data Fusion?
Data Fusion is a fully managed, cloud-native data integration solution at scale. Its visual point-and-click interface allows code-free deployment of ETL/ELT data pipelines. Data Fusion is directly integrated with Google Cloud Services and thus focuses on data security, ensuring data is available for analysis quickly.
Advantages:
- Offers a code-free graphical interface
- Fully managed Google Cloud-native architecture with the reliability, scalability, and security of Google Cloud.
- Comes with 150+ preconfigured connectors and transformers at no extra cost.
Use Cases:
- Analytics environment
- Helps customers break down data silos and allows the development of an agile data warehouse with BigQuery
Key Differences Between Dataform, Dataprep , and Data Fusion
Even though Dataform, Google Cloud Dataprep, and Cloud Data Fusion all belong to the Google Cloud ecosystem, they are used at different stages of the data pipeline, alongside differ in certain parameters.
Data Fusion is mainly concerned with data ingestion and the integration of various data into cloud data storage or data warehouses.
Dataprep is responsible for cleaning, exploration, and preparation to be analyzed in a visual interface. Whereas Dataform works with SQL-based transformations and data workflow management right within Google BigQuery.
These services combined help at various phases of the modern data lifecycle - ingestion to preparation and ultimately transformation.
Comparison Table: Dataform vs Dataprep vs Data Fusion
Below is the summary of the key differences between Dataform, Dataprep, and Data Fusion.
Parameter |
Dataform |
Dataprep |
Data Fusion |
| Purpose | SQL-based data transformation in Big Query | Supports data cleaning and preparation | ETL/ELT data integration |
| Skills required | SQL knowledge and version control | Low, no-code platform | Moderate to high pipeline design |
| Pricing | Free to use | Is dependent on the complexity | Moderate pricing |
| Automation & Orchestration | Supports scheduling and workflows | Limited to preparation | Very high – supports batch and streaming |
| Data Types Supported | Structured data | Structured, and unstructured data | Structured, and semi structured data |
| Use Cases | Ideal for BigQuery pipelines | Logs, semi-structured data, raw CSVs, and more | Building end-to-end
Ingestion pipelines from on/prem cloud |
Wrapping it Up
Dataform, Dataprep, and Data Fusion do not compete with one another; they complement each other across the modern data stack. In this blog, we explore the key differences between them. In a nutshell, Data fusion is mainly used for ingestion, Dataprep for cleaning, and Dataform for warehouse transformation.
All these platforms help to build scalable, efficient, and budget-friendly pipelines available on the Google Cloud platform. After all, the specific choice depends on your business requirements.
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FAQs
1. What is DataPrep used for?
Answer: DataPrep is mainly used for cleaning and improving the data in business apps such as help desks, CRM, and for removing any duplicate records.
2. When should you use Dataform rather than Dataprep or Data Fusion?
Answer: Make sure to use Dataform when your team requires version-controlled SQL-based data transformations and analytics workflows in the case of modern data warehouses.
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