Google Cloud Data Fusion: Simplifying Data Integration and Analysis

Today’s data-driven environment demands organizations dig through heaps of data, extract valuable information, and give insights. However, so many data sources are out there, making data simplification tedious and overwhelming. 

What if we told you there’s a simpler, more effective, and more efficient solution for the entire process? Here’s where Google Cloud Data Fusion enters to help integrate and analyze data. 

What is Google Cloud Data Fusion?

Google Cloud Data Fusion is a no-coding, and fully-managed cloud-native data integration service is the dream, and that is what Google Cloud Data Fusion offers to its users. 

Thus, by eliminating common data integration obstacles and reducing the need for technical expertise, Cloud Data Fusion simplifies and accelerates near-real-time analytics.

Key Features of Google Cloud Data Fusion

  1. 1

    Pre-built connectors

    Google Data Fusion makes data processing easier than ever. With its 150+ pre-built transformations and connectors, it serves as the perfect data processing tool for real-time and batch processing at no additional expense. 

  2. 2

    Drag-and-drop interface

    The intuitive GUI lets you (the user) connect to desired applications through a visual list of sinks and taps with drag-and-drop ease to help complete transformation, ingestion/extraction, and loading steps. 

  3. 3

    Data transformation capabilities

    The GUI also helps with data transformation capabilities as it directly ingests data from on-premises applications, SaaS and streaming applications, sensors, mobile applications, and other sources. 

  4. 4

    Data lineage and monitoring

    The integrated meta-data and end-to-end data monitoring and lineage capabilities help simplify the impact analysis, provenance, and root cause.

  5. 5

    Collaboration and version control

    Cloud Data Fusion also helps the growing CDAP (Cask Data Application Platform) community with a focus on data integration. You can help other users, review code, submit ideas, suggest improvements, and engage effectively with your employees.

Benefits of Google Cloud Data Fusion

Google Cloud Data Fusion is a powerful tool that helps with simplifying processes. We’ll look at some benefits that can help you streamline and automate monotonous processes. 

  • Simplifies data integration

    Data integration is simplified through the intuitive GUI and features like drag-and-drop and pre-built connectors to different data sources. All of these make Data Fusion easy and integrate data from different sources without using complex code.

  • Lowers total cost of ownership

    Google Cloud Data Fusion lowers the overall operational savings cost which ultimately lowers the cost of ownership. Organizations can reduce the amount of time and effort required for building, designing, testing, troubleshooting, and maintaining data pipelines. 

  • Reduces time-to-insight

    Removing technical obstacles enables earlier integration of data pipelines, self-service capability, and greater availability of data. These outcomes aid in faster time to insight/value, greater business agility, and flexibility. 

  • Provides greater data visibility and control

    Organizations have the flexibility to create their own connectors and customize Cloud Data Fusion more easily. 

How Google Cloud Data Fusion Works

Google Cloud Data Fusion helps users efficiently build and manage ETL/ELT (Extract Transform and load/Extract load Transform) data pipeline effectively. However, there’s a lot more that goes on behind the scenes. 

We’ll be talking about how Data Fusion works, what components it uses, the architecture, and lastly the data integration pipeline process. 

1. The architecture of Google Cloud Data Fusion:

Cloud Data Fusion is a service in Google Cloud that allows building and managing data pipelines. It runs on a GKE cluster inside a tenant project and uses Cloud Storage, SQL, Persistent Disk, Elasticsearch, and Cloud KMS to store metadata.

Cloud-native architecture

The cloud-native approach focuses on developing, building, and running scalable applications to take full benefit of cloud-based services and deliver models. Some of the applications included are: 

  • Immutable infrastructure: Servers remain unchanged after deployment.

  • Microservices: Small, independent software components that collectively perform as a complete cloud-native software, allowing for greater flexibility, scalability, and fault tolerance.

  • Declarative APIs: Used to bring loosely coupled microservices together and specify the data that a microservice wants and the results it can provide. 

  • Service mesh: A layer of infrastructure that manages communication between multiple microservices, introducing additional functions without the need to write new code in the application. 

Microservices-based architecture

The microservices architecture mainly aims at developing applications. These microservices help to segment larger applications into smaller parts. Each has its own responsibility to handle discrete tasks. 

Microservices architecture is used for different approaches including:

  • Website migration: It involves a complex website to migrate to a microservices platform that is cloud-based and container-based. 

  • Media content: It is the images and videos that are stored on a scalable object storage system using the microservices architecture. 

  • Transactions and invoices: It helps to create independent units of payment processing and ordering. 

  • Data processing: Provides cloud support for existing modular data processing services, extending and making them more flexible.

Kubernetes-based orchestration

First developed by Google, Kubernetes is a container-centric management software that has become the standard for deploying and operating containerized applications.

The Kubernetes-based orchestration focuses on integrating the following applications:

  • Increases development velocity: Supports cloud-native microservices-based apps and containerization of existing apps for fast development. 

  • Deploying applications anywhere: Built to run on any deployment, be it on-site, public clouds, or hybrid deployments.

  • Running efficient services: Enables automatic cluster size adjustments, which means your applications can be automatically scaled up or down based on demand. 

2. Components of Google Cloud Data Fusion:

The Cloud Data Fusion instance runs within one Compute Engine zone on Google Cloud. The architecture contains components including tenant project, user interface, system services, metadata storage, domain, namespaces, etc. 

Therefore, it’s important to focus on these components and how they can help you utilize resources on the cloud.  

  • Fusion Studio

    Fusion Studio is an intuitive interface designed for data engineers and scientists to effortlessly manage and maintain data pipelines on the cloud. With Google Cloud's ETL and ELT services, users can easily execute data pipelines without needing to write any code.

  • Fusion Engine

    Google Cloud Platform's Fusion Engine is another powerful tool that provides a scalable environment for managing, deploying, and testing containerized applications.

    The Google Kubernetes Engine offers a graphical user interface to easily manage multiple Compute Engine instances clustered together for improved performance.

3. Data Integration Pipeline Creation Process:

Managing pipelines with integrated data means you need no code. We’ll be overviewing the basic steps you need to create an effective data integration pipeline in Google Cloud Data Fusion. 

  • Creating a new pipeline

    To create a data pipeline on Google Cloud, you need to create a cloud data fusion instance and deploy a sample pipeline. You will have to provide instance details like name, description, and region.

  • Adding sources and destinations

    After creating the cloud data fusion instance and sample pipeline, you need to add data sources and assign their destinations correctly. Google Cloud provides various data types such as databases, streaming sources, and cloud storage to be used correctly.

  • Adding transformations

    Google Cloud Data Fusion offers pre-built transformations for data collection, filtering, processing, and more. You can use these transformations to make necessary changes to the data as required.

  • Configuring execution settings

    The last step is to apply the configurations. This means providing the details for input and output schema, and parameters, and validating the data to ensure the data pipeline is processing the data as desired. 

Best Practices for using Google Cloud Data Fusion

Google Cloud Data Fusion can be used for the better good when managing data pipelines. If you’re getting frustrated with the workflows and trying to wrap your hands around the concept, we’ve outlined a few practices that help you use Google Cloud Data Fusion optimally.

  • It’s best to plan your data integration pipeline first before carrying on with deploying the pipelines. 

  • Google Cloud Storage provides a great storage platform so you can benefit from great features like object lifecycle management and data transfer service. 

  • It’s also very important to test your pipeline before deploying it. You can use the preview feature for validating the data. 

  • Make sure to keep up with the latest developments in security features and use secure passwords along with strong firewalls. 

  • Lastly, it’s important to review your data pipeline regularly and make sure there are no anomalies or irregular data instances in the pipeline that may have occurred during data transformations. 

Use Cases for Google Cloud Data Fusion

We’ll be exploring some insightful implementations for Google Cloud Data Fusion in this section. Read on to know more about these use cases and how they can help solve common data integration challenges. 

  1. 1

    Cloud Data Warehousing

    A use case for cloud data warehousing is to create a data warehouse in BigQuery using Google Cloud Data Fusion to read data tables from an on-premises Oracle Data Warehouse, ingest them into BigQuery, and perform data manipulations to clean and denormalize the tables.

  2. 2

    Real-time Data Processing

    Data Fusion's replication feature enables easy duplication of transactional and operational databases such as SQL Server, Oracle, and MySQL into BigQuery.

    Integration with Datastream allows for continuous analysis of changes, while feasibility assessment and performance/health monitoring provide observability and faster development iterations.

  3. 3

    IoT Data Integration

    IoT service providers can benefit from using Google Cloud Data Fusion. It assists in processing and analyzing the data gathered by IoT sensors that monitor temperature, humidity, air quality, and other variables, such as DHT11 and MQ135.

  4. 4

    Legacy System Integration

    In order to bridge the gap between the networks, the task of linking APIs between on-premises, and cloud-based systems is called legacy system integration.

    Google Cloud Data Fusion helps a legacy system by providing pre-built connectors and a UI to make it easy to connect the data to different sources. 

  5. 5

    Cloud Data Migration

    Let’s say a manufacturing firm migrates from an on-premises data warehouse to the cloud due to the company's expansion and increased data requirements.

    The migration process can be done effectively using Google Cloud Data Fusion to extract, transform, and load data into the new cloud data warehouse quickly.

Getting Started with Google Cloud Data Fusion

Now that we’ve explored so much about Google Cloud Data Fusion. Let’s see how you can hop on the bandwagon to get started with the platform. 

  • Setting up a Google Cloud account

    Setting up an account is no hard job, but you might have to pay for a Google Cloud account. However, you can always start with the free trial and initiate a new project in the Google Cloud Console after sign-up. 

  • Creating a new pipeline

    You can start a new Data Fusion instance inside of your project after setting up your Google Cloud account and project.

    The option from the Data Fusion interface may then be chosen to start a new pipeline. Then, you can choose the source and destination data stores for your pipeline using the drag-and-drop interface.

  • Managing and monitoring pipelines

    The next thing to worry about is managing and monitoring pipelines after creating them. The Data Fusion interface lets you do this effortlessly.

    You can look at any errors or warnings that may arise during the pipeline execution and make the necessary actions using the interface. 

  • Troubleshooting common issues

    Lastly, you can tackle issues since you are using the interface too. It’ll provide a detailed overview of all the errors or warnings in the pipeline. If that doesn’t work, you can always reach out to the Google Cloud support team for assistance. 


We’ve explored everything possible on the Google Cloud Data Fusion and how it helps simplify data and help you analyze it effectively. It’s a handy tool for engineers out there looking to simplify tedious processes and work with a fully-managed service to do so. 

About the author


Youssef is a Senior Cloud Consultant & Founder of

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