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. Utilities
  3. Analytics
  4. Analytics Integrator
  5. Alation vs Google Cloud Data Fusion

Alation vs Google Cloud Data Fusion

OverviewComparisonAlternatives

Overview

Alation
Alation
Stacks14
Followers26
Votes0
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

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

  1. Deployment and Infrastructure: Alation is a data catalog solution that is typically deployed on-premises, while Google Cloud Data Fusion is a cloud-based service that operates on the Google Cloud Platform.
  2. Data Integration Capabilities: Alation focuses on data cataloging and data governance, providing insights into data usage and data lineage, whereas Google Cloud Data Fusion specializes in data integration, allowing users to create data pipelines for ETL (Extract, Transform, Load) processes.
  3. Integration with Ecosystem: Alation integrates with a wide range of data sources, data visualization tools, and data governance platforms, offering a comprehensive data management solution, whereas Google Cloud Data Fusion is specifically designed to work seamlessly with other Google Cloud services and products.
  4. Learning Curve: Alation is more user-friendly and intuitive, with a focus on business users and data stewards, whereas Google Cloud Data Fusion requires more technical expertise and familiarity with cloud technologies for effective utilization.
  5. Pricing Model: Alation typically follows a traditional software licensing model based on the number of users and data connectors, while Google Cloud Data Fusion operates on a pay-as-you-go pricing structure, where users pay for the resources consumed.
  6. Customization and Extensibility: Alation provides a rich set of customization options and extensibility through APIs and integrations, allowing organizations to tailor the platform to their specific needs, whereas Google Cloud Data Fusion offers limited customization options and is more focused on standardized, out-of-the-box functionality.

In Summary, Alation and Google Cloud Data Fusion differ in deployment options, data integration capabilities, ecosystem integration, user-friendliness, pricing models, and customization options.

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

Detailed Comparison

Alation
Alation
Google Cloud Data Fusion
Google Cloud Data Fusion

The leader in collaborative data cataloging, it empowers analysts & information stewards to search, query & collaborate for fast and accurate insights.

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.

Data Catalog; Automatically indexes your data by source; Automatically gathers knowledge about your data
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
Stacks
14
Stacks
25
Followers
26
Followers
156
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
No integrations available
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

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

Segment

Segment

Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch.

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.

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