We performed a comparison between Darwin and Dremio based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"The thing that I find most valuable is the ability to clean the data."
"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."
"The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate."
"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."
"In terms of streamlining a lot of the low-level data science work, it does a few things there."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"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."
"Dremio allows querying the files I have on my block storage or object storage."
"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."
"We primarily use Dremio to create a data framework and a data queue."
"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."
"The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition."
"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."
"The analyze function takes a lot of time."
"Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model."
"There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."
"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."
"The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"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."
"It shows errors sometimes."
"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."
"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."
Earn 20 points
Darwin is ranked 27th in Data Science Platforms while Dremio is ranked 10th in Data Science Platforms with 6 reviews. Darwin is rated 8.0, while Dremio is rated 8.6. The top reviewer of Darwin writes "Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows". On the other hand, the top reviewer of Dremio writes "It enables you to manage changes more effectively than any other platform". Darwin is most compared with IBM Watson Studio, Databricks and Microsoft Azure Machine Learning Studio, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and Microsoft Azure Synapse Analytics.
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