StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Google Cloud Data Fusion vs OpenRefine

Google Cloud Data Fusion vs OpenRefine

OverviewDecisionsComparisonAlternatives

Overview

OpenRefine
OpenRefine
Stacks33
Followers68
Votes0
GitHub Stars11.6K
Forks2.1K
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

Google Cloud Data Fusion vs OpenRefine: What are the differences?

Introduction

Google Cloud Data Fusion and OpenRefine are two data integration and transformation tools that offer various features and functionalities. However, they have key differences that set them apart from each other. In this comparison, we will highlight six main differences between Google Cloud Data Fusion and OpenRefine.

  1. Deployment Model: Google Cloud Data Fusion is a cloud-native platform that is fully managed and operated by Google. It allows users to build and deploy data pipelines in the cloud without the need for infrastructure management. On the other hand, OpenRefine is an open-source tool that needs to be installed and run locally on the user's machine. It provides a desktop application for data cleaning and transformation tasks.

  2. Data Source Connectivity: Google Cloud Data Fusion offers out-of-the-box connectors to various data sources, including Google Cloud Storage, BigQuery, and more. It allows users to easily ingest data from these sources and integrate it into their data pipelines. In contrast, OpenRefine primarily focuses on working with tabular data files and does not provide native integrations or connectors for other data sources.

  3. Data Transformation Capabilities: Google Cloud Data Fusion provides a visual interface for creating and configuring data transformation pipelines. It offers a wide range of built-in transformations and functions that can be applied to the data. Additionally, it supports custom transformations using SQL or Python. OpenRefine also offers data transformation capabilities but primarily focuses on data cleaning, standardization, and reconciliation tasks.

  4. Scalability and Performance: Google Cloud Data Fusion is designed to handle large-scale data processing and can scale resources as needed based on the workload. It leverages Google's infrastructure and resources to provide high-performance data processing. OpenRefine, being a desktop application, is limited to the resources available on the user's machine and may not be suitable for processing large volumes of data or handling complex transformations.

  5. Collaboration and Sharing: Google Cloud Data Fusion enables collaboration among team members by providing a collaborative environment where multiple users can work on the same data pipelines simultaneously. It also allows sharing and reusability of pipelines within an organization. OpenRefine, being a desktop application, does not offer native collaboration features and sharing pipelines may require manual file sharing or version control.

  6. Managed Service and Support: Google Cloud Data Fusion is a managed service provided by Google, which means that Google is responsible for infrastructure, maintenance, and support. It offers technical support and ensures the availability and reliability of the platform. OpenRefine, being open source, relies on community support and does not provide official support or guaranteed service-level agreements.

In summary, Google Cloud Data Fusion and OpenRefine differ in their deployment model, data source connectivity, data transformation capabilities, scalability, collaboration features, and support options. While Google Cloud Data Fusion offers a cloud-native managed service with extensive integration possibilities and scalability, OpenRefine provides a desktop application focused on data cleaning and transformation tasks.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on OpenRefine, Google Cloud Data Fusion

Sarah
Sarah

Jun 25, 2020

Needs adviceonOpenRefineOpenRefine

I'm looking for an open-source/free/cheap tool to clean messy data coming from various travel APIs. We use many different APIs and save the info in our DB. However, many duplicates cannot be easily recognized as such.

We would either write an algorithm or use smart technology/tools with ML to help with product management.

While there are many things to be considered, this is one feature that it should have:

"To avoid confusion, we need to merge the suppliers & products accordingly. Products and suppliers must be able to be merged and assigned separately.

Reason: It may happen that one supplier offers different products. E.g., 1 tour operator offers 3 products via 1 API, but only 1 product with 3 (or a different amount of) variations via a different API. Also, the commission may differ for products, which we need to consider. Very often, products that are live (are bookable in real-time) on via 1 API, but are not live on the other. E.g., Supplier product 1 & 2 of API1 are live, product 3 not. For the same supplier, API2 provides live availability for products 1, 2, and 3.

Summing up, when merging the suppliers (tour operators) we need to consider:

  • Are the products the same for all APIs?
  • Which booking system API gives a better commission? Note: Some APIs charge us 1-5% depending on the monthly sale, which needs to be considered
  • Which booking system provides live availability
  • Is it the same supplier, or is the name only similar?

Most of the time, the supplier names differ even if they are the same (e.g., API1 often names them XX Pty Ltd, while API2 leaves "Pty Ltd" out). Additionally, the product title, description, etc. differ.

We need to write logic and create an algorithm to find the duplicates & to merge, assign, or (de)activate the respective supplier or product. My previous developer started a module to merge the suppliers, which does not seem to work correctly. Also, it is way too time taking considering the high amount of products that we have.

I would recommend merging, assigning etc. products and suppliers only if our algorithm says it's 90- 100% the matching supplier/product. Otherwise, admins need to be able to check & modify this. E.g. everything with a lower possibility of matching will be matched automatically, but can be undone or modified.

The next time the cron job runs, this needs to be considered to avoid recreating duplicates & creating a mess."

I am not sure in what way OpenRefine can help to achieve this and what ML tool can be connected to learn from the decisions the product management team makes. Maybe you have an idea of how other travel portals deal with messy data, duplicates, etc.?

I'm looking for the cheapest solution for a start-up, but it should do the work properly.

19.2k views19.2k
Comments

Detailed Comparison

OpenRefine
OpenRefine
Google Cloud Data Fusion
Google Cloud Data Fusion

It is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

Faceting; Clustering; Editing cells; Reconciling; Extending web services
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
GitHub Stars
11.6K
GitHub Stars
-
GitHub Forks
2.1K
GitHub Forks
-
Stacks
33
Stacks
25
Followers
68
Followers
156
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
Python
Python
Dask
Dask
Ludwig
Ludwig
Vertica
Vertica
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to OpenRefine, Google Cloud Data Fusion?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase