Google Cloud Data Fusion vs Amundsen

Overview

Google Cloud Data Fusion

Stacks25

Followers156

Votes1

Amundsen

Stacks17

Followers42

Votes0

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

Google Cloud Data Fusion: Fully managed, code-free data integration at any scale. 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; Amundsen: A metadata driven application for improving the productivity of data analysts, data scientists and engineers. It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Google Cloud Data Fusion and Amundsen can be categorized as "Big Data" tools.

Some of the features offered by Google Cloud Data Fusion are:

Code-free self-service
Collaborative data engineering
GCP-native

On the other hand, Amundsen provides the following key features:

Datasets (Tables) schema and usage frequency/popularity
Users bookmark, owner, frequent user
Dashboard popularity, lineage to datasets

Amundsen is an open source tool with 889 GitHub stars and 163 GitHub forks. Here's a link to Amundsen's open source repository on GitHub.

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Detailed Comparison

Google Cloud Data Fusion	Amundsen
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.	It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement	Datasets (Tables) schema and usage frequency/popularity; Users bookmark, owner, frequent user; Dashboard popularity, lineage to datasets
Statistics
Stacks 25	Stacks 17
Followers 156	Followers 42
Votes 1	Votes 0
Pros & Cons
Pros 1 Lower total cost of pipeline ownership	No community feedback yet
Integrations
Google Cloud Storage Google BigQuery	Google BigQuery Snowflake AWS Glue Superset Apache Hive

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

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

Distributed SQL Query Engine for Big Data

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 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

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 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™ 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

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

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.