Amazon EMR (Elastic MapReduce) vs. Databricks Lakehouse Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon EMR
Score 8.5 out of 10
N/A
Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.N/A
Databricks Lakehouse Platform
Score 8.0 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Pricing
Amazon EMR (Elastic MapReduce)Databricks Lakehouse Platform
Editions & Modules
No answers on this topic
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
Offerings
Pricing Offerings
Amazon EMRDatabricks Lakehouse Platform
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon EMR (Elastic MapReduce)Databricks Lakehouse Platform
Considered Both Products
Amazon EMR
Chose Amazon EMR (Elastic MapReduce)
Compared to Databricks, Amazon EMR is a much cheaper option to get the work down. And compared to Amazon ec2, Amazon EMR is a much more powerful tool to get large datasets transformation down in a fairly short amount of time. The drawback is that amazon EMR would be very costly …
Chose Amazon EMR (Elastic MapReduce)
EMR is more suited for developers. Databricks feel more for data science-oriented with its notebooks and customs visualizations. With EMR you can more easily add additional capacity on-damnd on the instance. With others is a more cumbersome process. And then, you can also …
Chose Amazon EMR (Elastic MapReduce)
The alternatives to EMR are mainly hadoop distributions owned by the 3 companies above. I have not used the other distributions so it is difficult to comment, but the general tradeoff is, at the cost of a longer setup time and more infra management, you get more flexible …
Chose Amazon EMR (Elastic MapReduce)
Having one of these enterprise edition license comes at its own costs. But, the flexibility to have the cluster spin up with the workbenches and code snippets on the same is really beneficial. Especially, if one had to move out of EMR and consider an option which reduces the …
Databricks Lakehouse Platform
Chose Databricks Lakehouse Platform
The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when …
Chose Databricks Lakehouse Platform
Easier to set up and get started. Less of a learning curve.
Top Pros
Top Cons
Best Alternatives
Amazon EMR (Elastic MapReduce)Databricks Lakehouse Platform
Small Businesses

No answers on this topic

No answers on this topic

Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Snowflake
Snowflake
Score 9.1 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
Snowflake
Snowflake
Score 9.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon EMR (Elastic MapReduce)Databricks Lakehouse Platform
Likelihood to Recommend
8.4
(19 ratings)
8.4
(17 ratings)
Usability
8.3
(3 ratings)
9.4
(3 ratings)
Support Rating
9.0
(3 ratings)
8.5
(2 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
8.0
(1 ratings)
Professional Services
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Amazon EMR (Elastic MapReduce)Databricks Lakehouse Platform
Likelihood to Recommend
Amazon AWS
We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.
Read full review
Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
Read full review
Pros
Amazon AWS
  • Amazon Elastic MapReduce works well for managing analyses that use multiple tools, such as Hadoop and Spark. If it were not for the fact that we use multiple tools, there would be less need for MapReduce.
  • MapReduce is always on. I've never had a problem getting data analyses to run on the system. It's simple to set up data mining projects.
  • Amazon Elastic MapReduce has no problems dealing with very large data sets. It processes them just fine. With that said, the outputs don't come instantaneously. It takes time.
Read full review
Databricks
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
Read full review
Cons
Amazon AWS
  • Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR.
  • Like some of the enterprise editions EMR does not provide on premises options.
  • No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well.
Read full review
Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
Read full review
Usability
Amazon AWS
I give Amazon EMR this rating because while it is great at simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data, it can be run slow, freeze and glitch sometimes. So overall Amazon EMR is pretty good to use other than some basic issues.
Read full review
Databricks
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
Read full review
Support Rating
Amazon AWS
There's a vast group of trained and certified (by AWS) professionals ready to work for anyone that needs to implement, configure or fix EMR. There's also a great amount of documentation that is accessible to anyone who's trying to learn this. And there's also always the help of AWS itself. They have people ready to help you analyze your needs and then make a recommendation.
Read full review
Databricks
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
Read full review
Alternatives Considered
Amazon AWS
Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
Read full review
Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
Read full review
Return on Investment
Amazon AWS
  • Positive: Helped process the jobs amazingly fast.
  • Positive: Did not have to spend much time to learn the system, therefore, saving valuable research time.
  • Negative: Not flexible for some scenarios, like when some plugins are required, or when the project has to be moved in-house.
Read full review
Databricks
  • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
  • DB has the ability to terminate/time out instances which helps manage cost.
  • The ability to quickly access typical hard to build data scenarios easily is a strength.
Read full review
ScreenShots