StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Amazon EMR vs Microsoft SQL Server

Amazon EMR vs Microsoft SQL Server

OverviewDecisionsComparisonAlternatives

Overview

Amazon EMR
Amazon EMR
Stacks542
Followers682
Votes54
Microsoft SQL Server
Microsoft SQL Server
Stacks21.3K
Followers15.5K
Votes540

Amazon EMR vs Microsoft SQL Server: What are the differences?

Comparison between Amazon EMR and Microsoft SQL Server

Amazon EMR and Microsoft SQL Server are two popular tools used for data processing and analytics. While both serve similar purposes, there are several key differences between them.

  1. Scalability and Flexibility: Amazon EMR is a cloud-based service that allows for easy scaling of resources based on demand. It enables the use of a wide range of data processing frameworks, such as Apache Spark and Apache Hadoop. On the other hand, Microsoft SQL Server is a relational database management system (RDBMS) that is primarily designed for structured data and does not offer the same level of scalability and flexibility as Amazon EMR.

  2. Data Processing Capabilities: Amazon EMR is specifically designed for big data processing and analytics tasks. It provides a wide range of tools and frameworks for processing and analyzing data at scale. In contrast, Microsoft SQL Server is more focused on traditional SQL-based data management and analytics, making it suitable for smaller-scale and structured data analysis.

  3. Cost Model: Amazon EMR follows a pay-as-you-go model, where users only pay for the resources they consume. It offers the advantage of low upfront costs and allows for easy scaling and cost optimization. On the other hand, Microsoft SQL Server is typically licensed based on a per-core or per-server basis, making it more suitable for fixed workloads and predictable usage patterns.

  4. Data Integration: Amazon EMR provides seamless integration with various data storage and processing services within the AWS ecosystem, such as Amazon S3, Amazon Redshift, and Amazon Athena. It also supports integration with external services and data sources. In comparison, Microsoft SQL Server offers integration with other Microsoft products and services, such as the Azure ecosystem and Microsoft Power BI.

  5. Development and Management Tools: Amazon EMR provides a range of development and management tools, such as the AWS Management Console, AWS CLI, and SDKs, making it easy to provision, manage, and monitor EMR clusters. Microsoft SQL Server, on the other hand, offers a comprehensive set of development tools, including SQL Server Management Studio (SSMS), SQL Server Data Tools (SSDT), and SQL Server Profiler.

  6. Supported Workloads: Amazon EMR is well-suited for processing large volumes of structured, semi-structured, and unstructured data, making it ideal for big data analytics, machine learning, and data transformation tasks. On the contrary, Microsoft SQL Server is primarily designed for transactional workloads and structured data analytics, making it suitable for OLTP (Online Transaction Processing) systems and business intelligence applications.

In summary, Amazon EMR and Microsoft SQL Server differ in terms of scalability, data processing capabilities, cost model, data integration, development tools, and supported workloads. The choice between the two depends on the specific requirements of the data processing and analytics tasks at hand.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Amazon EMR, Microsoft SQL Server

Erin
Erin

IT Specialist

Mar 10, 2020

Needs adviceonMicrosoft SQL ServerMicrosoft SQL ServerMySQLMySQLPostgreSQLPostgreSQL

I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

  1. I need to use either @{MySQL}|tool:1025| or @{PostgreSQL}|tool:1028| on a @{Linux}|tool:10483| based OS. Which would be better for this application?
  2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
668k views668k
Comments

Detailed Comparison

Amazon EMR
Amazon EMR
Microsoft SQL Server
Microsoft SQL Server

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

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

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.
-
Statistics
Stacks
542
Stacks
21.3K
Followers
682
Followers
15.5K
Votes
54
Votes
540
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
  • 139
    Reliable and easy to use
  • 101
    High performance
  • 95
    Great with .net
  • 65
    Works well with .net
  • 56
    Easy to maintain
Cons
  • 4
    Expensive Licensing
  • 2
    Microsoft
  • 1
    Replication can loose the data
  • 1
    Allwayon can loose data in asycronious mode
  • 1
    The maximum number of connections is only 14000 connect

What are some alternatives to Amazon EMR, Microsoft SQL Server?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase