AWS Data Exchange vs. Azure Databricks

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
AWS Data Exchange
Score 9.6 out of 10
N/A
AWS Data Exchange is an integration for data service, from which subscribers can easily browse the AWS Data Exchange catalog to find relevant and up-to-date commercial data products covering a wide range of industries, including financial services, healthcare, life sciences, geospatial, consumer, media & entertainment, and more.N/A
Azure Databricks
Score 8.5 out of 10
N/A
Azure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. The solution includes autoscaling and auto-termination to improve…N/A
Pricing
AWS Data ExchangeAzure Databricks
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
AWS Data ExchangeAzure Databricks
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
AWS Data ExchangeAzure Databricks
Features
AWS Data ExchangeAzure Databricks
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
AWS Data Exchange
8.0
2 Ratings
4% below category average
Azure Databricks
-
Ratings
Connect to traditional data sources7.02 Ratings00 Ratings
Connecto to Big Data and NoSQL9.01 Ratings00 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
AWS Data Exchange
8.2
1 Ratings
3% above category average
Azure Databricks
-
Ratings
Data model creation9.01 Ratings00 Ratings
Metadata management9.01 Ratings00 Ratings
Business rules and workflow7.01 Ratings00 Ratings
Collaboration9.01 Ratings00 Ratings
Testing and debugging7.01 Ratings00 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
AWS Data Exchange
7.0
1 Ratings
16% below category average
Azure Databricks
-
Ratings
Integration with data quality tools7.01 Ratings00 Ratings
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
AWS Data Exchange
-
Ratings
Azure Databricks
8.4
2 Ratings
0% below category average
Connect to Multiple Data Sources00 Ratings7.22 Ratings
Extend Existing Data Sources00 Ratings9.02 Ratings
Automatic Data Format Detection00 Ratings9.32 Ratings
MDM Integration00 Ratings8.01 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
AWS Data Exchange
-
Ratings
Azure Databricks
5.7
2 Ratings
38% below category average
Visualization00 Ratings5.42 Ratings
Interactive Data Analysis00 Ratings6.12 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
AWS Data Exchange
-
Ratings
Azure Databricks
8.2
2 Ratings
0% above category average
Interactive Data Cleaning and Enrichment00 Ratings7.02 Ratings
Data Transformations00 Ratings8.72 Ratings
Data Encryption00 Ratings9.32 Ratings
Built-in Processors00 Ratings7.62 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
AWS Data Exchange
-
Ratings
Azure Databricks
8.5
2 Ratings
1% above category average
Multiple Model Development Languages and Tools00 Ratings8.62 Ratings
Automated Machine Learning00 Ratings8.72 Ratings
Single platform for multiple model development00 Ratings8.32 Ratings
Self-Service Model Delivery00 Ratings8.32 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
AWS Data Exchange
-
Ratings
Azure Databricks
8.7
2 Ratings
1% above category average
Flexible Model Publishing Options00 Ratings8.02 Ratings
Security, Governance, and Cost Controls00 Ratings9.32 Ratings
Best Alternatives
AWS Data ExchangeAzure Databricks
Small Businesses
Skyvia
Skyvia
Score 10.0 out of 10
Jupyter Notebook
Jupyter Notebook
Score 8.9 out of 10
Medium-sized Companies
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
Posit
Posit
Score 9.9 out of 10
Enterprises
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
Posit
Posit
Score 9.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
AWS Data ExchangeAzure Databricks
Likelihood to Recommend
1.0
(2 ratings)
9.2
(3 ratings)
Likelihood to Renew
1.0
(1 ratings)
-
(0 ratings)
Usability
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
AWS Data ExchangeAzure Databricks
Likelihood to Recommend
Amazon AWS
AWS Data Exchange fits best for scenarios where you have datasets that you would like to sell and you want to deliver it to anyone who would like to purchase it. It really beats having to set up downloads via your own website or portal. However, it can get complicated to manage if you're trying to deliver a dataset a client has already paid for.
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Microsoft
Suppose you have multiple data sources and you want to bring the data into one place, transform it and make it into a data model. Azure Databricks is a perfectly suited solution for this. Leverage spark JDBC or any external cloud based tool (ADG, AWS Glue) to bring the data into a cloud storage. From there, Azure Databricks can handle everything. The data can be ingested by Azure Databricks into a 3 Layer architecture based on the delta lake tables. The first layer, raw layer, has the raw as is data from source. The enrich layer, acts as the cleaning and filtering layer to clean the data at an individual table level. The gold layer, is the final layer responsible for a data model. This acts as the serving layer for BI For BI needs, if you need simple dashboards, you can leverage Azure Databricks BI to create them with a simple click! For complex dashboards, just like any sql db, you can hook it with a simple JDBC string to any external BI tool.
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Pros
Amazon AWS
  • Simplified data delivery
  • Ability to create any amount of data products
  • Ability to integrate payment plans with data products
  • Tracking data downloads and users
  • Integration with other AWS data services
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Microsoft
  • SQL
  • Data management
  • Data access
Read full review
Cons
Amazon AWS
  • Integration with more data sources
  • Ability to deliver data to clients without AWS accounts
  • Inclusion of direct data downloads in addition to asynchronous methods
Read full review
Microsoft
  • Their pipeline workflow orchestration is pretty primitive. Lacks some common features
  • Workspace UI and navigation requires steep learning curve
  • Personally, I am not fond of their autosave feature. Its dangerous for production level notebooks scripts
Read full review
Likelihood to Renew
Amazon AWS
There have been a lot of problems with ADX. First, the entire system is incredibly clunky from beginning to end.First, by AWS's own admission they're missing a lot of "tablestakes functionality" like the ability to see who is coming to your pages, more flexibility to edit and update your listings, the ability to create a storefront or catalog that actually tries to sell your products. All-in-all you're flying completely blind with AWS. In our convos with other sellers we strongly believe very little organic traffic is flowing through the AWS exchange. For the headache, it's not worth the time or the effort. It's very difficult to market or sell your products.We've also had a number of simple UX bugs where they just don't accurately reflect the attributes of your product. For instance for an S3 bucket they had "+metered costs" displayed to one of our buyers in the price. This of course caused a lot of confusion. They also misrepresented the historical revisions that were available in our product sets because of another UX bug. It's difficult to know what other things in the UX are also broken and incongruent.We also did have a purchase, but the seller is completely at their whim at providing you fake emails, fake company names, fake use cases because AWS hasn't thought through simple workflows like "why even have subscription confirmation if I can fake literally everything about a subscription request." So as a result we're now in an endless, timewasting, unhelpful thread with AWS support trying to get payment. They're confused of what to do and we feel completely lost.Lastly, the AWS team has been abysmal in addressing our concerns. Conversations with them result in a laundry list of excuses of why simple functionalities are so hard (including just having accurate documentation). It was a very frustrating and unproductive call. Our objective of our call was to help us see that ADX is a well-resourced and well-visioned product. Ultimately they couldn't clearly articulate who they built the exchange for both on the seller side and the buyer side.Don't waste your time. This is at best a very foggy experiment. Look at other sellers, they have a lot of free pages to try to get attention, but then have smart tactics to divert transactions away from the ADX. Ultimately, smart move. Why give 8-10% of your cut to a product that is basically bare-bones infrastructure.
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Microsoft
No answers on this topic
Usability
Amazon AWS
No answers on this topic
Microsoft
Based on my extensive use of Azure Databricks for the past 3.5 years, it has evolved into a beautiful amalgamation of all the data domains and needs. From a data analyst, to a data engineer, to a data scientist, it jas got them all! Being language agnostic and focused on easy to use UI based control, it is a dream to use for every Data related personnel across all experience levels!
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Alternatives Considered
Amazon AWS
No answers on this topic
Microsoft
Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!" Far ahead of the competition, the delta lakehouse platform also fares better than it counterparts of Iceberg implementation or a loosely bound Delta Lake implementation of Synapse
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Return on Investment
Amazon AWS
  • Reduced time to publish datasets for sale by more than 80%
  • Increased net profit from dataset sales by ~10%
  • Reduced data delivery time to clients by 15%
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Microsoft
  • Helped reduce time for collecting data
  • Reduced cost in maintaining multiple data sources
  • Access for multiple users and management of users/data in a single platform
Read full review
ScreenShots