Amazon EMR and Snowflake are cloud-based data processing platforms competing in big data analytics. Amazon EMR often stands out for its scalability and integration with AWS services, while Snowflake excels in simplicity and performance for SQL-based queries.
Features: Amazon EMR offers extensive flexibility with Apache Hadoop, Spark, and frameworks, providing large-scale data processing adaptability. It utilizes AWS services like EC2 and S3 for scalable storage and processing. Snowflake features a multi-cluster architecture and zero-copy cloning for efficient SQL analytics, with capabilities for processing structured and semi-structured data and ease of auto-scaling the resources.
Room for Improvement: Amazon EMR could benefit from enhanced user interface simplicity and better initial setup guidelines for new users. Improved cost management and more intuitive data management tools could also enhance its appeal. Snowflake might improve in streamlining integration with non-SQL tools and offering more competitive pricing. Improved clarity and flexibility in its billing model and enhancements in onboarding can increase user satisfaction.
Ease of Deployment and Customer Service: Amazon EMR benefits from seamless integration with AWS services, making it ideal for those already using AWS, although its deployment may require more expertise. Meanwhile, Snowflake simplifies deployment and management with its fully-managed model, standing out for its quick setup and responsive customer service, accommodating businesses focused on analytics.
Pricing and ROI: Amazon EMR offers a cost-effective pay-as-you-go model, particularly beneficial for users leveraging multiple AWS services. This can result in lower ownership costs for varied workloads. Snowflake uses a utility-based credit system, providing predictable billing, automatic scaling, and efficient resource management, potentially offering better ROI for constant data-intensive tasks.
We get all call support, screen sharing support, and immediate support, so there are no problems.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
The technical support from Snowflake is very good, nice, and efficient.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
The cost factor differs significantly. When you run Spark application on EKS, you run at the pod level, so you can control the compute cost. But in Amazon EMR, when you have to run one application, you have to launch the entire EC2.
There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
Cost reduction is one area I would like Snowflake to improve.
Costs are involved based on cluster resources, data volumes, EC2 instances, instance sizes, Kubernetes, Docker services, storage, and data transfers.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
Amazon EMR helps in scalability, real-time and batch processing of data, handling efficient data sources, and managing data lakes, data stores, and data marts on file systems and in S3 buckets.
The compute demand has decreased and it has reduced the cost significantly.
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses.
Being able to perform AI and Machine Learning in the same location as the data is quite advantageous.
| Product | Market Share (%) |
|---|---|
| Snowflake | 17.7% |
| Amazon EMR | 3.3% |
| Other | 79.0% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 5 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 20 |
| Large Enterprise | 56 |
Snowflake provides a modern data warehousing solution with features designed for seamless integration, scalability, and consumption-based pricing. It handles large datasets efficiently, making it a market leader for businesses migrating to the cloud.
Snowflake offers a flexible architecture that separates storage and compute resources, supporting efficient ETL jobs. Known for scalability and ease of use, it features built-in time zone conversion and robust data sharing capabilities. Its enhanced security, performance, and ability to handle semi-structured data are notable. Users suggest improvements in UI, pricing, on-premises integration, and data science functions, while calling for better transaction performance and machine learning capabilities. Users benefit from effective SQL querying, real-time analytics, and sharing options, supporting comprehensive data analysis with tools like Tableau and Power BI.
What are Snowflake's Key Features?In industries like finance, healthcare, and retail, Snowflake's flexible data warehousing and analytics capabilities facilitate cloud migration, streamline data storage, and allow organizations to consolidate data from multiple sources for advanced insights and AI-driven strategies. Its integration with analytics tools supports comprehensive data analysis and reporting tasks.
We monitor all Cloud Data Warehouse 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.
