Databricks vs Amazon EMR: Key Factors for Choosing the Best in 2025

December 05th, 2024
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Databricks and Amazon EMR are leading cloud platforms for big data processing. Databricks excels in machine learning and real-time analytics, while Amazon EMR focuses on cost-effective, scalable data processing using Hadoop and Spark.See How Atlan Simplifies Data Governance – Start Product Tour

Both platforms offer robust integration, automation, and performance, but differ in user experience and pricing.

Databricks simplifies data engineering with a user-friendly interface, whereas Amazon EMR offers flexibility with AWS ecosystem integration.

Choosing between them depends on specific use cases, such as advanced analytics for Databricks or cost-optimized batch processing for Amazon EMR. ​

Databricks and Amazon EMR are both popular cloud platforms that data teams use to handle large-scale data processing.

While comparing them, it’s crucial to note how each tool supports the adoption of Apache Spark. Since its release in 2010, Apache Spark has been the go-to analytics engine for data processing. Spark is an open-source tool and is one of the easiest ways to work with applications using Hadoop data.

For instance, Spark has helped companies manage and run their ETL and machine learning workloads smoothly. As a result, all major cloud platforms jumped in early on the Spark bandwagon, making it the de facto standard for large-scale data processing.

Now let’s explore — the prominent platforms Databricks and Amazon EMR from that lens. Starting with a little bit of background on each product.

Databricks: Background

Spark was a significant improvement over Hadoop — more efficient in processing advanced ML algorithms and easier to operate.

However, it was challenging to set up Spark clusters from scratch and manage them at scale. So in 2013, the engineers behind Spark built Databricks to make Spark deployments effortless for everyone.

Databricks significantly lowers the Spark learning curve and provides notebooks that connect to Spark at scale. Databricks also offers several other capabilities that make the lives of data engineers much easier.

Amazon EMR: Background

In April 2009, Amazon launched its Elastic MapReduce service, i.e., EMR. Initially, Amazon built EMR to support Hadoop MapReduce cluster workloads using Amazon’s EC2 infrastructure.

Today, EMR supports a range of workloads on top of Hadoop MapReduce. Since its initial launch, AWS has constantly improved its EMR service, with several annual releases catering to client requirements and a rapidly evolving data landscape.

According to the Amazon EMR Market Share and Competitor Data by Datanyze, Amazon EMR holds approximately 1.02% of the big data processing market, with 903 current websites utilizing the service.

So, how is Databricks different from Amazon EMR? Let’s look at the five main factors of comparison.

Databricks vs. Amazon EMR: 5 factors of comparison

To compare Databricks vs. Amazon EMR, let’s consider five fundamental elements of a data platform for the modern data stack:

  1. Cloud platform
  2. Data processing engines
  3. Developer experience
  4. Migration and lock-in
  5. Data ecosystem

Cloud platform

Databricks lets you choose your cloud platform


Databricks has partnered with Google Cloud, AWS, Azure, and Alibaba. As a result, it provides seamless integrations with other services of the cloud platform.

For instance, if you are deploying Databricks on Azure, you can use Azure Data Factory, Azure Blob Storage, CosmosDB, Azure ML, PowerBI, and more without hassle.

Since August 2022, Databricks has also started supporting serverless compute with AWS and Azure.

EMR works with Amazon Web Services


Amazon EMR is AWS’s service for cluster computing workloads with Hadoop MapReduce and Spark.

As mentioned earlier, the earliest version of EMR uses Amazon EC2 instances as cluster nodes for task distribution and compute.

Over time, Amazon EMR has started offering three more ways to deploy:

Also, read → Sync Autotuner Reduced Apache Spark’s EMR Cost by 25%

Data processing engines

Hadoop MapReduce vs. Spark


Traditional data warehousing systems were not designed to handle the volume and variety of data we have started seeing over the last decade. Spark was introduced to tackle this problem, a few years after Hadoop MapReduce had already been in the picture.

Databricks is built around Spark. However, it also works well with many Hadoop ecosystem components, such as Hive, YARN, and Mesos.

On the other hand, Amazon EMR was built to work with MapReduce. Today, it supports multiple data processing engines, including Spark. As a result, you can run Presto, Hudi, Hadoop, and more.

EMR gives you a pre-packaged cluster setup, which you can use for any distributed data processing engine.

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Developer experience

Databricks Notebooks


Databricks provides a notebook-style interface called Databricks Notebooks, which is slightly different from popular notebooks like Zeppelin and Jupyter.

Databricks Notebooks support four major programming languages — Python, SQL, R, and Scala.

For developers used to Jupyter, Databricks offers several Jupyter widgets and libraries that smoothen the entire migration process. Databricks also furnishes other widgets and libraries with rich visualization options to further enrich the user experience.

Amazon EMR Notebooks and EMR Studio


Amazon EMR gives you two great options to interact with the services running on the cluster:

4. Migration and lock-in

Homogeneous and heterogeneous migrations


A homogeneous migration—Amazon EMR with Spark workloads to Databricks and vice-versa—will be less cumbersome than a heterogeneous migration, where you move Presto workloads to Databricks.

Even with homogeneous migrations, the required effort can be costly and complex, which is why both AWS and Databricks provide migration guides (Amazon EMR, Databricks) for you to follow.

Benefits of the open-source ecosystem


Getting locked in isn’t a significant issue with either Amazon EMR or Databricks, except for the dependency on the cloud-platform-specific ecosystem. Both Amazon EMR and Databricks are built around open-source technologies. So, you can easily migrate to and from these systems. The migration will still be a non-trivial exercise in terms of risk, cost, and effort.

5. Data ecosystem

Both Databricks and AWS offer mature data ecosystems with extensive support for external services. They also provide comprehensive support via native services for data functions, such as orchestration, data discovery, storage flexibility, cataloging, ETL, ML, and AI.

To summarize, here’s a comparison table on Amazon EMR vs. Databricks:

Aspect Amazon EMR Databricks
Cloud providers supported Amazon Web Services Amazon Web Services, Google Cloud, Microsoft Azure
Infrastructure EKS, EC2, EMR Serverless Options provided by Azure, Google Cloud, AWS, and Alibaba Cloud
Workflow orchestration MWAA, Apache Airflow, Step Functions Databricks Data & ML Pipeline orchestrator
Data cataloging AWS Glue Data Catalog Unity Catalog
Data processing engines Spark, Hadoop MapReduce, Presto, Trino Spark
Data warehousing Amazon Redshift Lakehouse Architecture using Delta
Data lakes Amazon S3 Any object-based storage from any of the supported cloud platforms

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How organizations making the most out of their data using Atlan

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

Databricks vs. Amazon EMR: What’s best for you?

In summary, Databricks and Amazon EMR have unique features, supporting services, and capabilities.

EMR is supported by many data-centric web services to help build data lakes and warehouses or perform data migrations. Databricks is versatile and supports all major cloud platforms, including AWS. It also has native capabilities designed to solve problems with modern data systems, such as data discovery, governance, ownership, and stewardship.

Databricks distinguishes itself from its competitors with its Unity catalog and a Spark-centric architecture. Meanwhile, Amazon’s data catalog is just a technical metadata catalog and can’t be used directly by businesses.

Meanwhile, EMR distinguishes itself by providing you with a standard infrastructure layer to run many types of distributed applications, Spark being just one of the many.

So, pick a platform that best caters to your business needs and available resources.

FAQs about Databricks vs. Amazon EMR

1. What is the difference between Databricks and Amazon EMR?


Databricks is a unified analytics platform that integrates deeply with Apache Spark, offering collaborative features like notebooks and seamless machine learning pipelines. Amazon EMR is a cloud big data platform optimized for Hadoop, Spark, and other distributed data processing engines, with tight integration into the AWS ecosystem.

2. Which platform is more cost-effective: Databricks or Amazon EMR?


Cost-effectiveness depends on workload and usage patterns. Databricks typically offers better performance for complex machine learning tasks, whereas Amazon EMR may be more cost-efficient for basic Hadoop or Spark batch processing, leveraging AWS’s flexible pricing.

3. How do Databricks and Amazon EMR handle big data processing?


Databricks excels in interactive and streaming data processing, with its notebooks providing real-time collaboration. Amazon EMR is optimized for large-scale batch processing and integrates with a wide range of AWS services for a comprehensive big data workflow.

4. Which tool is better for Spark workloads: Databricks or Amazon EMR?


Databricks is generally better for Spark workloads due to its high optimization level and built-in features for data engineering and machine learning. Amazon EMR is suitable for running Spark at scale within the AWS environment.

5. How does Amazon EMR integrate with the AWS ecosystem?


Amazon EMR integrates seamlessly with AWS services like S3, Lambda, and CloudWatch, enabling efficient data storage, processing, and monitoring within the AWS infrastructure.

6. What are the use cases for Databricks vs. Amazon EMR?


Databricks is ideal for collaborative data science, machine learning, and real-time analytics. Amazon EMR suits batch processing, ETL workflows, and large-scale data transformations in enterprise environments.

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