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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Amazon Aurora vs Amazon EMR

Amazon Aurora vs Amazon EMR

OverviewComparisonAlternatives

Overview

Amazon EMR
Amazon EMR
Stacks544
Followers682
Votes54
Amazon Aurora
Amazon Aurora
Stacks813
Followers745
Votes55

Amazon Aurora vs Amazon EMR: What are the differences?

Introduction

Amazon Aurora and Amazon EMR are two popular services offered by Amazon Web Services (AWS) for different purposes. While Amazon Aurora is a relational database service, Amazon EMR is a managed big data platform. Below are the key differences between these two services:

  1. Scalability and Performance: One key difference between Amazon Aurora and Amazon EMR is their scalability and performance characteristics. Amazon Aurora is designed for online transaction processing (OLTP) workloads and offers high scalability and performance with its distributed, fault-tolerant architecture and automatic scaling capabilities. On the other hand, Amazon EMR is optimized for processing large data sets and running big data analytics workloads, such as Apache Spark, Hive, and Hadoop. It provides scalable processing power for data-intensive applications.

  2. Data Storage: Another difference lies in the type of data storage used by these two services. Amazon Aurora uses a distributed storage system that replicates data across multiple Availability Zones for durability and high availability. It offers high-performance storage with automatic scaling capabilities. In contrast, Amazon EMR uses Hadoop Distributed File System (HDFS) for storing large datasets across a cluster of EC2 instances. It provides scalable storage for big data processing.

  3. Data Processing: Amazon Aurora is designed for traditional structured data processing using SQL queries. It supports common SQL databases, such as MySQL and PostgreSQL, and offers high-performance analytics capabilities for OLTP workloads. On the other hand, Amazon EMR is focused on big data processing and analytics. It supports a wide range of big data processing frameworks like Apache Spark, Apache Hive, and Apache Hadoop, enabling users to perform complex data transformations and analysis.

  4. Data Formats and Ecosystem: Amazon Aurora supports standard SQL databases and can integrate easily with existing SQL-based applications and tools. It also provides compatibility with MySQL and PostgreSQL, allowing seamless migration of existing applications. In contrast, Amazon EMR supports a variety of data formats, including structured, semi-structured, and unstructured data. It offers a comprehensive big data ecosystem that includes popular frameworks, libraries, and development tools for processing, analyzing, and visualizing large datasets.

  5. Ease of Use: Amazon Aurora is a fully managed service that takes care of routine database management tasks, such as backups, software patching, and automatic scaling. It offers simplicity and ease of use for managing relational databases. On the other hand, Amazon EMR provides a managed Hadoop framework that simplifies the setup and management of complex big data processing environments. It automates tasks like cluster provisioning, configuration, and scaling, making it convenient for processing large datasets.

  6. Cost: The pricing model for Amazon Aurora and Amazon EMR also differs. Amazon Aurora has a pay-per-use pricing structure based on the database instance size and the amount of storage consumed. It offers cost-effective options for both small and large database workloads. Amazon EMR, on the other hand, has a separate pricing model that factors in the number and type of EC2 instances used in the cluster, storage costs, and any additional AWS services utilized. It provides flexibility in choosing the most cost-efficient configuration for big data processing.

In summary, Amazon Aurora is a highly scalable and performant relational database service, while Amazon EMR is a managed big data platform optimized for processing large datasets and running complex analytics workloads.

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Detailed Comparison

Amazon EMR
Amazon EMR
Amazon Aurora
Amazon Aurora

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster;Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.;Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.;Hadoop Tools- EMR supports powerful and proven Hadoop tools such as Hive, Pig, and HBase.
High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
Statistics
Stacks
544
Stacks
813
Followers
682
Followers
745
Votes
54
Votes
55
Pros & Cons
Pros
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
Pros
  • 14
    MySQL compatibility
  • 12
    Better performance
  • 10
    Easy read scalability
  • 9
    Speed
  • 7
    Low latency read replica
Cons
  • 2
    Vendor locking
  • 1
    Rigid schema
Integrations
No integrations available
PostgreSQL
PostgreSQL
MySQL
MySQL

What are some alternatives to Amazon EMR, Amazon Aurora?

Amazon RDS

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Google Cloud SQL

Google Cloud SQL

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

ClearDB

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Azure SQL Database

Azure SQL Database

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

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