Great Asynchronous Data Messaging Services, Reliable, Scalable and fully managed by Google
May 26, 2021

Great Asynchronous Data Messaging Services, Reliable, Scalable and fully managed by Google

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with Google Cloud Pub/Sub

Google [Cloud] Pub/Sub is used for high Volume, high speed data stream from multiple data sources towards a Data Platform. This is setup by Central Data and Analytics team to serve business use cases for most of the departments like Marketing, Product, Sales, Editorial etc. within our organization

Business Problems addressed
  • Identify Non App users , their inactivity and show such users a personalized promo on the website when they visit
  • Identify low engaged users overall, ingest their past usage patterns and cluster them in lookalike user segments to show them next best actions
  • Capture users' behavior and interactions as "Topics" in Pub/Sub and show them next best action as appropriate
  • With a pub/sub architecture the consumer is decoupled in time from the publisher i.e. if the consumer goes down, it can replay any events that occurred during its downtime.
  • It also allows consumer to throttle and batch incoming data providing much needed flexibility while working with multiple types of data sources
  • A simple and easy to use UI on cloud console for setup and debugging
  • It enables event-driven architectures and asynchronous parallel processing, while improving performance, reliability and scalability
  • It is limited to work with the same platform but with different datasets at the same time, you must request a prior security authentication.
  • It can sometimes lead to unexpected charges, as Pub/Sub will automatically keep on retrying messages continuously, even if failures are due to permanent code-level issues.
  • Message re-deliveries don't apply for ingested services like with Python based client. Push messages tried to be delivered immediately and if your service is busy dealing with some other task, it won't be done OR goes into a queue
  • Ability to create Big Data infrastructure without bothering much about managing it: We create topics and subscriptions programmatically without having to set up any queues in advance. This makes deployments of new versions easier as well
  • Asynchronous communication provides an incredible advantage in reading messages, connections to services and data systems installed inside and outside the platform, is easy to manage and integrate via APIs
  • it's easy to set up between apps locally as well as globally. My team can use it to send messages that trigger front end messages to our users, or to send large chunks of data around our global system for the storage purpose as well
  • Increased Efficiency with reliable and Google managed services up all the time wit Disaster Recovery in place as well
  • Definitely Lower costs being a cloud based solution and easier to setup
  • Faster Project delivery and go to market plan for the business use cases basis this technology at the back end
  • Easy to setup Publisher, Subscribers and Message Queue service
  • More Reliable and Easy Scalable with Google Managed services
  • Easily integrated with most of the data sources we typically use for Data Storage and Analysis
  • 10k Topics is a good enough number to build and deliver the business use cases
  • Asynchronous and fallback mechanisms are great to ensure parallel delivery of the messages

Do you think Google Cloud Pub/Sub delivers good value for the price?


Are you happy with Google Cloud Pub/Sub's feature set?


Did Google Cloud Pub/Sub live up to sales and marketing promises?


Did implementation of Google Cloud Pub/Sub go as expected?


Would you buy Google Cloud Pub/Sub again?


Well Suited for
  • One Source, multi subscribers scenario, where there is no issue with errors on multiple source datasets and anyone who is subscribed to a Topic receives the messages
  • The load of the messages are totally comfortable, the workspace inside the platform, the functions of messaging and calls of the properties are easy to use
  • Reliable and Scalable model for creating and maintaining a big data pipeline
Less Appropriate for:
  • Ingesting multiple datasets from same data sources at the same time
  • Many Google Cloud Pub/Sub classes are concrete and not interfaces, making them harder to plan for when writing unit or integration tests