We performed a comparison between Amazon EMR and Azure Data Factory 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."The project management is very streamlined."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"The solution helps us manage huge volumes of data."
"It allows users to access the data through a web interface."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"The solution is pretty simple to set up."
"It has a variety of options and support systems."
"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."
"The solution has a good interface and the integration with GitHub is very useful."
"The initial setup is very quick and easy."
"From what we have seen so far, the solution seems very stable."
"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."
"The flexibility that Azure Data Factory offers is great."
"The best part of this product is the extraction, transformation, and load."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."
"The product's features for storing data in static clusters could be better."
"The problem for us is it starts very slow."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"The initial setup was time-consuming."
"The legacy versions of the solution are not supported in the new versions."
"Modules and strategies should be better handled and notified early in advance."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."
"It can improve from the perspective of active logging. It can provide active logging information."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"The pricing model should be more transparent and available online."
Amazon EMR is ranked 9th in Cloud Data Warehouse with 20 reviews while Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews. Amazon EMR is rated 7.8, while Azure Data Factory is rated 8.0. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Amazon Redshift, Apache Spark and Microsoft Azure Synapse Analytics, whereas Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics. See our Amazon EMR vs. Azure Data Factory report.
See our list of best Cloud Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
