AWS EMR vs Google Dataproc: A Comparision
When it comes to cloud deployment for big data processing, AWS EMR and Google Dataproc are two of the most popular solutions. Both these platforms offer the ability to process vast amounts of data and provide scalable computing power. In this post, we will compare the capabilities, features, and pricing of AWS EMR and Google Dataproc to determine which one is better.
Features
Both AWS EMR and Google Dataproc offer similar features for big data processing. Both platforms let you scale your cluster according to your workload and provide the capability to run analytics in real-time. Both platforms also come with pre-built frameworks such as Apache Hadoop, Spark, and Hive, which can help you get started quickly. The key difference between the two platforms is the degree to which they support other data processing frameworks.
Google Dataproc has native support for several other data processing frameworks, such as Apache Flink, Apache Beam, and Apache Druid, along with integrations with Dataflow and BigQuery. AWS EMR, on the other hand, has limited native support for other data processing frameworks, but you can still use many other open-source frameworks by installing them manually.
Pricing
Pricing is a critical factor when comparing cloud deployment solutions. AWS EMR pricing is based on the size of your cluster, and you are charged per hour for each instance. Also, AWS charges you for storage used by your cluster. Google Dataproc, on the other hand, has a more simplified pricing model, charging a single price per instance per hour.
When considering the cost, it is important to note that Google Dataproc machines typically have more resources than AWS EMR machines of the same size. Therefore, Dataproc machines can handle more processing at a lower overall cost. Overall, Google Dataproc is generally cheaper than AWS EMR.
Performance
Both AWS EMR and Google Dataproc offer similar performance capabilities, but it largely depends on the workload you are running. AWS EMR platforms feature the latest-generation EC2 instances, including C5, M5, R5, and I3. Moreover, AWS EMR also features integration with other AWS services like S3, Redshift, and EC2.
Google Dataproc offers virtual machines with more memory and more CPU resources than AWS EMR machines. Dataproc also supports autoscaling, a feature which enables adding or removing instances in response to changes in the cluster workload.
Conclusion
Both AWS EMR and Google Dataproc are robust big data deployment platforms that can handle complex data workflows. However, each platform has its unique features and limitations. While AWS EMR offers excellent integration with the AWS ecosystem, Google Dataproc’s cost-effectiveness and native support for various processing frameworks like Apache Beam and Druid make it an ideal platform for organizations looking for ease of use and greater flexibility.