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Google Cloud Dataflow

Score8.1 out of 10

35 Reviews and Ratings

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What is Google Cloud Dataflow?

Google offers Cloud Dataflow, a managed streaming analytics platform for real-time data insights, fraud detection, and other purposes.

Categories & Use Cases

Key Features

Learn about the best (and worst!) features Google Cloud Dataflow has to offer, as determined by TrustRadius' reviewers.
Based on 35 ratings of Google Cloud Dataflow's features
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Top Performing Features

  • Real-Time Data Analysis

    Ability to analyze data in motion

    Category average: 8.5

  • Data Ingestion from Multiple Data Sources

    Ability to ingest data from many sources including Internet of Things (IoT) endpoint data, stock trading data etc, as well as static data

    Category average: 8.7

  • Low Latency

    How many milli-seconds or seconds it takes to ingest, analyze and respond to an incoming event or data-point

    Category average: 8.4

Areas for Improvement

  • Linear Scale-Out

    Easy to scale out or scale down by visually changing the resource allocation. This allows changes in load or traffic to be handled without interruptions

    Category average: 7.5

  • Machine Learning Automation

    Machine learning helps automate predictive scoring on streaming data

    Category average: 8.6

  • Data Enrichment

    Ability to enrich the data stream with static reference data

    Category average: 7.7

Reviews

What users are saying about Google Cloud Dataflow.
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Google Managed data processing service

Sathish Palanivel(He/Him)View profile
Cloud Architect in Information Technology at Cognizant (10,001+ employees employees)

Use Cases and Deployment Scope

In our company we are using Google Cloud Dataflow to create data pipe lines for data transformation and ingestion use cases before loading data into database. Flexibility to create our own flex templates for any special case handling. Capability to fit streaming and batch data loads are some benefits. We have some real time loads, which Dataflow helps alot.

Pros

  • Streaming, Real time work load
  • Batch processing
  • Auto scaling
  • flexible pricing

Cons

  • inbuild template options can be expanded
  • more data connector options
  • easy of use

Return on Investment

  • cost saving from managing our own data center for ETL servers
  • consumption based pricing
  • with auto scaling feature, we were able to expand components to support work load

Other Software Used

Google BigQuery, erwin Data Modeler, Microsoft Teams