We performed a comparison between Azure Data Factory and Dremio 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."Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"The data copy template is a valuable feature."
"The flexibility that Azure Data Factory offers is great."
"From what we have seen so far, the solution seems very stable."
"The overall performance is quite good."
"The best part of this product is the extraction, transformation, and load."
"Powerful but easy-to-use and intuitive."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"Dremio allows querying the files I have on my block storage or object storage."
"We primarily use Dremio to create a data framework and a data queue."
"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."
"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."
"The pricing scheme is very complex and difficult to understand."
"I have not found any real shortcomings within the product."
"The initial setup is not very straightforward."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"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."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"The pricing model should be more transparent and available online."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"It shows errors sometimes."
"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."
"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."
"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."
"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."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Dremio is ranked 11th in Cloud Data Warehouse with 6 reviews. Azure Data Factory is rated 8.0, while Dremio is rated 8.6. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Dremio writes "It enables you to manage changes more effectively than any other platform". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and Microsoft Azure Synapse Analytics. See our Azure Data Factory vs. Dremio report.
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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.
