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 Matillion

Amazon EMR vs Matillion

OverviewComparisonAlternatives

Overview

Amazon EMR
Amazon EMR
Stacks542
Followers682
Votes54
Matillion
Matillion
Stacks51
Followers71
Votes0
GitHub Stars0
Forks0

Amazon EMR vs Matillion: What are the differences?

### Introduction

Key differences between Amazon EMR and Matillion are highlighted below:

1. **Architecture**: Amazon EMR is a managed Hadoop framework, while Matillion is a cloud-native ETL/ELT tool. EMR provides infrastructure provisioning and management for big data processing, whereas Matillion focuses on transforming and loading data into cloud data warehouses.

2. **Cost Structure**: Amazon EMR follows a pay-as-you-go model, where users pay for the EC2 instances and storage they use. In contrast, Matillion offers subscription-based pricing with fixed tiers based on the user's needs and usage, making it easier for budget planning and scalability.

3. **Supported Integrations**: Amazon EMR integrates seamlessly with various open-source big data frameworks like Apache Spark, Hadoop, and Presto, providing users with flexibility to choose the tools that best fit their needs. Matillion, on the other hand, specializes in integrations with cloud data warehouses such as Amazon Redshift, Snowflake, and Google BigQuery, enabling easy access to cloud-native analytics platforms.

4. **Ease of Use**: Amazon EMR requires more technical expertise to set up and manage clusters, as users need to configure Hadoop ecosystem components manually. In comparison, Matillion offers a user-friendly graphical interface that allows users to create data pipelines visually, reducing the need for coding and making it more accessible to non-technical users.

5. **Scalability**: While both Amazon EMR and Matillion are designed to scale based on workload demands, EMR provides more control over cluster scaling by allowing users to modify instance types and sizes dynamically. Matillion, on the other hand, automatically scales processing power based on the volume of data being processed, simplifying the scalability process for users.

6. **Community Support**: Amazon EMR benefits from a large community of users and developers contributing to its ecosystem, providing a vast array of resources, tutorials, and best practices. Matillion, although growing rapidly, has a smaller community but offers dedicated customer support and training to help users make the most of the platform.

In Summary, Amazon EMR and Matillion differ in architecture, cost structure, supported integrations, ease of use, scalability, and community support, catering to different needs in the big data and analytics space.

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

Detailed Comparison

Amazon EMR
Amazon EMR
Matillion
Matillion

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

It is a modern, browser-based UI, with powerful, push-down ETL/ELT functionality. With a fast setup, you are up and running in minutes.

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.
Edit, Transform and Load Data intuitively; Load Data from Dozens of Sources; 50% reduction in ETL development and maintenance effort ; Rich orchestration environment; Work as a team; Cheap; Billing via AWS.
Statistics
GitHub Stars
-
GitHub Stars
0
GitHub Forks
-
GitHub Forks
0
Stacks
542
Stacks
51
Followers
682
Followers
71
Votes
54
Votes
0
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
No community feedback yet
Integrations
No integrations available
Amazon S3
Amazon S3
Zendesk
Zendesk
MongoDB Stitch
MongoDB Stitch
Amazon Redshift
Amazon Redshift
Cassandra
Cassandra
Salesforce Sales Cloud
Salesforce Sales Cloud
Mixpanel
Mixpanel

What are some alternatives to Amazon EMR, Matillion?

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.

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.

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.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dremio

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

Cloudera Enterprise

Cloudera Enterprise

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

Airbyte

Airbyte

It is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses lakes & DBs.

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