Amazon EMR and Snowflake compete in the data processing and warehousing space. Snowflake seems to have the upper hand thanks to its flexibility and performance advantages.
Features: Amazon EMR is highly scalable, integrates well with other AWS services, and supports frameworks like Hadoop and Spark, making it cost-effective. Snowflake offers a managed data warehousing solution with separated storage and compute, allowing multi-format access and not being tied to a single cloud service, which enhances its flexibility and performance.
Room for Improvement: Amazon EMR could improve job management, interface customization, and legacy support. Challenges also include cluster configuration and stability. Snowflake users mention the need for pricing clarity, better integration with other services, and broader analytical tools, along with enhanced ETL and governance functions.
Ease of Deployment and Customer Service: Amazon EMR benefits from AWS's ecosystem for public cloud deployment but can experience issues with third-party tool integration. Snowflake allows deployment across multiple cloud environments, offering hybrid solutions, though it may face customer service inconsistencies.
Pricing and ROI: Amazon EMR charges based on processing resources with potential savings of 20% for companies moving from on-prem systems. Snowflake's use-as-you-go model has flexible pricing but complicates cost prediction. However, it's often considered cost-effective against traditional data warehouses.
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.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Snowflake is very scalable and has a dedicated team constantly improving the product.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
Snowflake is very stable, especially when used with AWS.
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.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
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 independence of the compute and storage within Snowflake is key.
One key feature is the separation of compute and storage, which eliminates storage limitations.
Snowflake is a cloud-based data warehousing solution for storing and processing data, generating reports and dashboards, and as a BI reporting source. It is used for optimizing costs and using financial data, as well as for migrating data from on-premises to the cloud. The solution is often used as a centralized data warehouse, combining data from multiple sources.
Snowflake has helped organizations improve query performance, store and process JSON and XML, consolidate multiple databases into one unified table, power company-wide dashboards, increase productivity, reduce processing time, and have easy maintenance with good technical support.
Its platform is made up of three components:
Snowflake has many valuable vital features. Some of the most useful ones include:
There are many benefits to implementing Snowflake. It helps optimize costs, reduce downtime, improve operational efficiency, and automate data replication for fast recovery, and it is built for high reliability and availability.
Below are quotes from interviews we conducted with users currently using the Snowflake solution:
Sreenivasan R., Director of Data Architecture and Engineering at Decision Minds, says, "Data sharing is a good feature. It is a majorly used feature. The elastic computing is another big feature. Separating computing and storage gives you flexibility. It doesn't require much DBA involvement because it doesn't need any performance tuning. We are not doing any performance tuning, and the entire burden of performance and SQL tuning is on Snowflake. Its usability is very good. I don't need to ramp up any user, and its onboarding is easier. You just onboard the user, and you are done with it. There are simple SQL and UI, and people are able to use this solution easily. Ease of use is a big thing in Snowflake."
A director of business operations at a logistics company mentions, "It requires no maintenance on our part. They handle all that. The speed is phenomenal. The pricing isn't really anything more than what you would be paying for a SQL server license or another tool to execute the same thing. We have zero maintenance on our side to do anything and the speed at which it performs queries and loads the data is amazing. It handles unstructured data extremely well, too. So, if the data is in a JSON array or an XML, it handles that super well."
A Solution Architect at a wholesaler/distributor comments, "The ability to share the data and the ability to scale up and down easily are the most valuable features. The concept of data sharing and data plumbing made it very easy to provide and share data. The ability to refresh your Dev or QA just by doing a clone is also valuable. It has the dynamic scale up and scale down feature. Development and deployment are much easier as compared to other platforms where you have to go through a lot of stuff. With a tool like DBT, you can do modeling and transformation within a single tool and deploy to Snowflake. It provides continuous deployment and continuous integration abilities. There is a separation of storage and compute, so you only get charged for your usage. You only pay for what you use. When we share the data downstream with business partners, we can specifically create compute for them, and we can charge back the business."
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