Google Dataproc and Amazon EMR have managed Spark and Hadoop services for big data processing, analysis, streaming, and machine learning. Dataproc and EMR save hours required to create and manage clusters. In this article, we will learn about EMR and Dataproc in detail and look at some of the common differences between them.
What is Google Dataproc
Google Dataproc is a managed Spark and Hadoop service that lets you leverage open-source data technologies for querying, batch processing, streaming, and machine learning.
Dataproc automation enables you to quickly build and manage clusters and save money by turning clusters off when you are not using them. Spending less time and money on administration lets you focus on your jobs and data.
What are some of the benefits of using Google Dataproc
With Dataproc, you can use Hadoop and Spark without other software requirements. It becomes easy to connect with clusters, and when you are not using them, you can turn off the clusters. It helps you save time and money. Some of the other advantages of using Dataproc are:
- Ease of use: To utilize Dataproc, you don’t need to learn new APIs or tools, making it simple to migrate existing projects to Dataproc without redesigning them. Pig, Hive, and Spark are routinely updated, making you more productive in less time.
- High Performance: Creating Spark and Hadoop clusters on-premises or through IaaS providers is usually a long task. It can take anything from five to thirty minutes. Dataproc clusters are quick to start, scale, and shut down, taking an average of 90 seconds or less for each activity. As a result, you’ll be able to spend less time on creation and more time working directly with your data.
- Cost-Effective: Dataproc costs only 1 cent per virtual CPU in your cluster per hour. Aside from the low price, Dataproc clusters might include preemptible instances with lower compute prices, significantly lowering costs. In addition, Dataproc charges you only for what you use, with second-by-second pricing and a one-minute billing period.
- Easy Integration: It is easy to integrate Dataproc with other offerings of GCP like Cloud Bigtable, Cloud Monitoring, BigQuery, Cloud Storage, and Cloud Logging.
What is Amazon EMR
Amazon EMR (formerly Amazon Elastic MapReduce) is a managed cluster service that makes it easier to run big data frameworks like Hadoop and Spark on AWS to process and analyze massive volumes of data.
Amazon EMR also enables you to transform and move massive amounts of data into and out of other AWS databases and data stores, including Amazon DynamoDB and Amazon Simple Storage Service (Amazon S3).
What are the benefits of using Amazon EMR
Some of the significant advantages of using Amazon EMR are:
- Cost-Effective: Amazon EMR cost is calculated by the number and type of EC2 instances deployed and the Region in which your cluster is launched. On-demand pricing model makes it highly cost-effective, and you can save even more money by purchasing Spot Instances or Reserved Instances.
- Easy Integration: Amazon EMR can easily integrate with other AWS services like S3, VPC, EC2, CloudTrail, AWS Lake Formation, and CloudWatch.
- Highly Secure: As you can connect EMR with other AWS services, it becomes easier to maintain a robust security level. For example, you can use IAM to manage permissions, security groups to control inbound and outbound traffic, server-side and client-side encryption, and EC2 key pairs to form a secure connection between the master and remote nodes.
- Reliability and Scalability: You can quickly scale up and down clusters per your needs. And also, in case of failure, EMR will automatically terminate and replace the instance.
Amazon EMR vs. Google Dataproc: The Difference
The major differences between Amazon EMR and Google Dataproc:
- Popularity: Amazon EMR is more popular than Google Dataproc. EMR has a market share of 12.22% in the Big data world compared to 1.09% of Google Dataproc.
- Pricing: Google Dataproc pricing depends on the size of the cluster and the time duration you are using the cluster. Further, the size depends on the number of vCPUs used in the cluster. Google has given a simple formula to calculate the pricing, which is $0.010*(number of vCPUs)*hourly duration. You will be charged for it separately if you use any other GCP resource with your cluster.
- Free Tier: Amazon does not have any trial or free tier availability of EMR, but on the other hand, Google offers Dataproc for a free trial.
- In Amazon EMR, you are charged per second with a minimum of a 1-minute duration. The pricing will depend on which service you deploy your EMR applications, such as Amazon EC2, EKS, EMR Serverless, and AWS Outposts. For every service, there is a different pricing model. You can check the pricing model of AWS EMR.
Amazon EMR and Google Dataproc offer the same services, and it isn’t easy to differentiate between them.
Conclusion
Amazon EMR and Google Dataproc are excellent services, and it isn’t easy to differentiate between them. Although, if we look at the popularity, EMR is a winner. AWS has been in the market for so long, and people trust it more than any other cloud provider, but in terms of pricing, Google Dataproc has a simple and easy-to-understand structure.
That’s it for comparing Amazon EMR vs. Google Dataproc.
See also
GCP vs. AWS Comparison: Which one is right for you
Firebase v.s Heroku: Which is Better
Firebase v.s AWS: Which to Choose For Your Project
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Machine Learning frameworks like PyTorch and Tensorflow is a testament to his versatility and commitment to the craft.