We performed a comparison between Apache Spark and Google Cloud Dataflow based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"The scalability has been the most valuable aspect of the solution."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"We use Spark to process data from different data sources."
"The deployment of the product is easy."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"I found the solution stable. We haven't had any problems with it."
"The product's deployment phase is easy."
"The solution allows us to program in any language we desire."
"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
"It is a scalable solution."
"The service is relatively cheap compared to other batch-processing engines."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"Apache Spark should add some resource management improvements to the algorithms."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"It should support more programming languages."
"The product could improve the user interface and make it easier for new users."
"The solution's setup process could be more accessible."
"They should do a market survey and then make improvements."
"The technical support has slight room for improvement."
"Google Cloud Dataflow should include a little cost optimization."
"The deployment time could also be reduced."
"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."
"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."
"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."
Apache Spark is ranked 1st in Hadoop with 60 reviews while Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews. Apache Spark is rated 8.4, while Google Cloud Dataflow is rated 7.8. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas Google Cloud Dataflow is most compared with Databricks, Apache NiFi, Amazon MSK, Amazon Kinesis and IBM Streams.
We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
