We performed a comparison between Apache Hadoop 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."As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R."
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges."
"Its integration is Hadoop's best feature because that allows us to support different tools in a big data platform."
"What comes with the standard setup is what we mostly use, but Ambari is the most important."
"It is a file system for data collection. There are nodes in this cluster that contain all the information, directories, and other files. The nodes are based on the MySQL database."
"The most valuable feature is the database."
"Data ingestion: It has rapid speed, if Apache Accumulo is used."
"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."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"Dremio allows querying the files I have on my block storage or object storage."
"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 gives you the ability to create services which do not require additional resources and sterilization."
"We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it."
"The solution is very expensive."
"In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency."
"General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error."
"Hadoop's security could be better."
"In certain cases, the configurations for dealing with data skewness do not make any sense."
"The key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks."
"From the Apache perspective or the open-source community, they need to add more capabilities to make life easier from a configuration and deployment perspective."
"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."
"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."
"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."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"It shows errors sometimes."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
Apache Hadoop is ranked 5th in Data Warehouse with 32 reviews while Dremio is ranked 11th in Cloud Data Warehouse with 6 reviews. Apache Hadoop is rated 7.8, while Dremio is rated 8.6. The top reviewer of Apache Hadoop writes "A file system for data collection that contains needed information and files". On the other hand, the top reviewer of Dremio writes "It enables you to manage changes more effectively than any other platform". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and SAP BW4HANA, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and Oracle Database Appliance. See our Apache Hadoop vs. Dremio report.
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