Amazon EMR and BigQuery are leading cloud-based solutions competing in the data processing and analytics market. BigQuery holds the upper hand due to its speed and flexibility, highly valued by users.
Features: Amazon EMR provides high scalability, reliability for processing large datasets, and integrates with Hadoop clusters using EC2 and S3. It requires minimal manual intervention owing to its managed nature. BigQuery is known for its fast performance, serverless platform, and built-in machine learning capabilities, allowing rapid analysis of extensive datasets.
Room for Improvement: Amazon EMR has a steep learning curve and lacks seamless web support, while customization can be challenging. It could benefit from enhanced stability and monitoring. BigQuery struggles with special character restrictions and could improve caching for external tables. There is also a need for more integrations and better machine learning features.
Ease of Deployment and Customer Service: Both platforms are primarily deployed on public clouds, with BigQuery also offering hybrid cloud options. Amazon EMR's customer support is generally knowledgeable but inconsistent. BigQuery's customer service receives mixed feedback; deploying on the public cloud is straightforward for both.
Pricing and ROI: Amazon EMR operates on a pay-as-you-go model with costs tied to infrastructure usage, requiring caution to avoid unexpected expenses. BigQuery charges based on data processed, which can be costly for small businesses but cost-effective for enterprises. Both solutions provide substantial ROI, particularly for enterprises moving from on-premise systems.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
rating the customer support at ten points out of ten
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 scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
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.
In general, if I know SQL and start playing around, it will start making sense.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
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.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
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