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Google Cloud Data Fusion

Fully managed, code-free data integration at any scale
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What is Google Cloud Data Fusion?

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.
Google Cloud Data Fusion is a tool in the Big Data Tools category of a tech stack.

Who uses Google Cloud Data Fusion?

Developers
23 developers on StackShare have stated that they use Google Cloud Data Fusion.

Google Cloud Data Fusion Integrations

Pros of Google Cloud Data Fusion
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Lower total cost of pipeline ownership
Decisions about Google Cloud Data Fusion

Here are some stack decisions, common use cases and reviews by companies and developers who chose Google Cloud Data Fusion in their tech stack.

I am currently launching 50 pipelines in a Google Cloud Data Fusion version 6.4 instance. These pipelines are launched daily and transport data from a MySQLServer database to Google BigQuery. The cost is becoming very high and I was wondering if the costs with Google Cloud Dataflow decrease for the same rows transported.

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Will Dataflow be the right replacement for AWS Glue? Are there any unforeseen exceptions like certain proprietary transformations not supported in Google Cloud Dataflow, connectors ecosystem, Data Quality & Date cleansing not supported in DataFlow. etc?

Also, how about Google Cloud Data Fusion as a replacement? In terms of No Code/Low code .. (Since basic use cases in Glue support UI, in that case, CDF may be the right choice ).

What would be the best choice?

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Google Cloud Data Fusion's Features

  • Code-free self-service
  • Collaborative data engineering
  • GCP-native
  • Enterprise-grade security
  • Integration metadata and lineage
  • Seamless operations
  • Comprehensive integration toolkit
  • Hybrid enablement

Google Cloud Data Fusion Alternatives & Comparisons

What are some alternatives to Google Cloud Data Fusion?
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.
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine 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.
Apache Hive
Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
See all alternatives

Google Cloud Data Fusion's Followers
132 developers follow Google Cloud Data Fusion to keep up with related blogs and decisions.