Amazon EMR vs Dremio comparison

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Amazon EMR

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Executive Summary

We performed a comparison between Amazon EMR and Dremio based on real PeerSpot user reviews.

Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon EMR vs. Dremio Report (Updated: October 2024).

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Amazon EMR vs. Dremio

October 2024

Helped 816,406 peers since 2012

Categories and Ranking

Amazon EMR

Ranking in Cloud Data Warehouse

11th

Average Rating

7.8

Number of Reviews

Ranking in other categories

Hadoop (3rd)

Dremio

Ranking in Cloud Data Warehouse

10th

Average Rating

8.6

Reviews Sentiment

5.9

Number of Reviews

Ranking in other categories

Data Science Platforms (8th)

Mindshare comparison

As of November 2024, in the Cloud Data Warehouse category, the mindshare of Amazon EMR is 4.5%, up from 4.5% compared to the previous year. The mindshare of Dremio is 4.2%, up from 2.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

Quan Vu

Data Governance Manager at VPBFC

Provides efficient data processing features and has good scalability

We need to have a data pipeline tool to ensure consistent data processing for the initial setup. We create a framework, read the code, and execute it in a data catalog. The size of the maintenance team depends on the project and the use cases. Usually, one backup team of four or five DevOps executives takes care of the backend and database. We need to separate our environments into production and development. We use GitHub for source control, Jenkins for the deployment pipeline, and a standard CI/CD tool to deploy code changes into production. We need to develop a deployment framework so developers only need to provide the code for their projects. The underlying engine then deploys the code, reads it, addresses the EMR filter, executes it, and completes the data processing.

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MikeWalker

Founder/ CEO at Morphogen Data Inc.

It enables you to manage changes more effectively than any other platform.

Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it. There's another thing called data providence. They're tied together. Data providence allows you to go back and recreate the data at any particular point in time. It's extremely important for compliance and governance issues because data changes all time. How did it change? What was it three days or months ago? You may have made some decisions based on data that was three months old, so you might need to revisit those. It's essential for things like machine learning and deep learning, where you are generating AI models off data. When the model stops working or doesn't work as expected, you need to figure out why. You have to go back and adjust the datasets used to train the model. We do that through an open-source project called Nessie, which is their basis for providing data lineage and data province capabilities. It's super powerful. Arrow is another open-source project for storing data in memory and performing data query operations. Data sits on a disk in one format. If you want to do anything with data, you have to load it into your computer and put it into memory so you can work with it. Arrow provides a format in memory that enables the whole library to perform various operations on that data. Every vendor has its own way of representing data in memory. They've latched onto an industry standard and developed it so it's open. Now people can use the exact same format in memory to do operations and use the library set to perform functions on data. New developers can decide if they want to develop their own memory format or use one that's already there. Data transfer is a massive problem when you're working with large datasets, doing advanced analytics, and trying to train machine learning or deep learning models. What happens often is companies downsample their data sets to do training on models because transferring and managing data on a deep learning or machine learning platform is too much.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."

"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."

"It allows users to access the data through a web interface."

"It has a variety of options and support systems."

"The initial setup is straightforward."

"The solution helps us manage huge volumes of data."

"This is the best tool for hosts and it's really flexible and scalable."

"We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."

More Amazon EMR pros

"Dremio gives you the ability to create services which do not require additional resources and sterilization."

"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."

"Dremio allows querying the files I have on my block storage or object storage."

"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."

"Everyone uses Dremio in my company; some use it only for the analytics function."

"We primarily use Dremio to create a data framework and a data queue."

Cons

"The dashboard management could be better. Right now, it's lacking a bit."

"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."

"The solution can become expensive if you are not careful."

"The product's features for storing data in static clusters could be better."

"There is room for improvement in pricing."

"The most complicated thing is configuring to the cluster and ensure it's running correctly."

"Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services."

"The problem for us is it starts very slow."

More Amazon EMR cons

"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."

"It shows errors sometimes."

"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."

"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."

"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."

Pricing and Cost Advice

"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."

"The product is not cheap, but it is not expensive."

"The price of the solution is expensive."

"Amazon EMR is not very expensive."

"There is no need to pay extra for third-party software."

"You don't need to pay for licensing on a yearly or monthly basis, you only pay for what you use, in terms of underlying instances."

"I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you tens of thousands of dollars. That's happened to us twice, so I'm sensitive to it. We're still trying to work on that. Our smallest client probably spends a hundred thousand dollars yearly on licensing, while our largest is well over a million."

"The cost of Amazon EMR is very high."

More Amazon EMR pricing and cost advice

"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."

"Dremio is less costly competitively to Snowflake or any other tool."

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816,406 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

13%

Manufacturing Company

Educational Organization

Financial Services Firm

32%

Computer Software Company

10%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Amazon EMR?

Amazon EMR is a good solution that can be used to manage big data.

What is your experience regarding pricing and costs for Amazon EMR?

I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you...

What needs improvement with Amazon EMR?

The solution can become expensive if you are not careful.

What do you like most about Dremio?

Dremio allows querying the files I have on my block storage or object storage.

What is your experience regarding pricing and costs for Dremio?

Every tool has a value based on its visualization, and the pricing is worth its value.

What needs improvement with Dremio?

Dremio's interface is good, but it has a few limitations. I cannot do a lot of things with ANSI SQL or basic SQL. I cannot use the recursive common table expression (CTE) in Dremio because the supp...

Cloudera Distribution for Hadoop vs Amazon EMR

Comparisons

Snowflake vs Amazon EMR

Compared 36% of the time

Compared 30% of the time

Amazon Redshift vs Amazon EMR

Compared 8% of the time

Azure Data Factory vs Amazon EMR

Compared 8% of the time

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Databricks vs Dremio

Compared 48% of the time

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 8% of the time

Microsoft Power BI vs Dremio

Compared 4% of the time

Amazon Redshift vs Dremio

Compared 4% of the time

More Dremio Competitors

Product Reports

Download Amazon EMR product report

Amazon EMR

November 2024

Download Dremio product report

November 2024

Also Known As

Amazon Elastic MapReduce

No data available

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Amazon Web Services (AWS)

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Overview

Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.

Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.

At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.

What Are Dremio's Key Features?

Data Reflections: Reduces query times by creating optimized representations of source data, which can accelerate performance without the complexity of traditional data warehousing solutions.
Semantic Layer: Allows users to define business metrics and dimensions centrally, ensuring consistency and governance across all analytics tools.
Built-in Security Features: Provides robust security measures, including column- and row-level security, ensuring compliance with data governance and privacy standards.
Support for Multiple Data Formats and Sources: Enables querying directly against a variety of data formats (Parquet, JSON, etc.) and sources without the need for conversion or replication.

What Benefits Should Users Expect?

When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.

How is Dremio Implemented Across Different Industries?

Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.

What About Dremio’s Pricing, Licensing, and Support?

Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.

Sample Customers

Yelp

UBS, TransUnion, Quantium, Daimler, OVH