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
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.
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.
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.
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.
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.
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.
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:
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:
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.