

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.
I would rate the technical support from Amazon as ten out of ten.
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.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
It can handle large datasets.
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.
I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.
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.
Pentaho Business Analytics is hard to learn and not suited for initial users as it requires knowledge of operating systems, Java, and other technical skills.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
I would rate the price for Amazon EMR, where one is high and ten is low, as a good one.
Pentaho Business Analytics is priced similarly to other competitors such as QlikView and Tableau.
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 features at Amazon EMR that I have found most valuable are fully customizable functions.
Amazon EMR provides out-of-the-box functionality because we can deploy and get Spark functionality over Hadoop.
It is a stable product, and it can handle large datasets.
| Product | Mindshare (%) |
|---|---|
| Amazon EMR | 9.7% |
| Cloudera Distribution for Hadoop | 14.4% |
| Apache Spark | 14.1% |
| Other | 61.8% |
| Product | Mindshare (%) |
|---|---|
| Pentaho Business Analytics | 1.0% |
| Microsoft Power BI | 7.1% |
| Tableau Enterprise | 5.8% |
| Other | 86.1% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
| Company Size | Count |
|---|---|
| Small Business | 22 |
| Midsize Enterprise | 7 |
| Large Enterprise | 15 |
Amazon EMR simplifies big data processing by offering integration with popular tools. It's scalable and cost-efficient, enabling fast processing while managing infrastructure effortlessly. It's designed for users aiming to streamline data workflows and leverage its batch processing capabilities effectively.
Amazon EMR is a managed service that provides robust features for big data processing. It integrates seamlessly with S3, EC2, Hive, and Spark to facilitate sophisticated data transformation tasks and infrastructure management. It allows organizations to run data lakes, Spark, and Hadoop clusters effortlessly, offering flexibility with on-demand execution and extensive scalability. The platform is valued for its strong processing speed and comprehensive security features, making it ideal for complex data engineering projects. It supports both batch processing and real-time workflows, designed to eliminate hardware management while maintaining cost efficiency and stability.
What are the key features of Amazon EMR?Amazon EMR is implemented by industries such as healthcare and tech processing for complex data tasks like building data lakes or financial data processing. It supports AI-driven analytics and data engineering projects, integrating with SageMaker for predictions and maintaining workflows in public health applications, allowing professionals in different fields to manage data pipelines, resource utilization, and job execution efficiently.
Pentaho Business Analytics, recognized for its powerful ETL capabilities, delivers robust data management and analytics. Its adaptable interface and custom plugins enable effective data transformations, appealing to enterprises seeking efficient data handling and integration.
Pentaho Business Analytics offers a comprehensive suite for data warehousing, ETL processes, and business intelligence. Known for integrating and analyzing data from multiple systems, it supports industries like marketing, automotive, telecom, and insurance. Despite critiques on its interface and Java reliance, its ability to manage both small and complex data loads makes it a cost-effective choice.
What are the key features of Pentaho Business Analytics?Pentaho Business Analytics finds application in sectors requiring extensive data storage and management like telecom and insurance. Companies utilize its capabilities for creating ETL pipelines, managing data flows, and enabling data-driven decision-making.
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