We performed a comparison between Apache Hadoop and Kovair Data Lake based on real PeerSpot user reviews.
Find out in this report how the two Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The performance is pretty good."
"High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization."
"The solution is easy to expand. We haven't seen any issues with it in that sense. We've added 10 servers, and we've added two nodes. We've been expanding since we started using it since we started out so small. Companies that need to scale shouldn't have a problem doing so."
"The tool's stability is good."
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"What comes with the standard setup is what we mostly use, but Ambari is the most important."
"The scalability of Apache Hadoop is very good."
"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."
"The tool's most valuable features for us are its combination of formatting, ETL, analytics, and storage capabilities."
"The most valuable feature is the ability to interact with teachers in real-time and manage lessons after class."
"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 not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data."
"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."
"It would be good to have more advanced analytics tools."
"In certain cases, the configurations for dealing with data skewness do not make any sense."
"The upgrade path should be improved because it is not as easy as it should be."
"I think more of the solution needs to be focused around the panel processing and retrieval of data."
"The solution is very expensive."
"Maybe the chat conversation feature could be improved."
"The solution is expensive. For future releases, it would be beneficial if Kovair Data Lake could enhance its ETL and data capabilities."
Apache Hadoop is ranked 5th in Data Warehouse with 33 reviews while Kovair Data Lake is ranked 18th in Data Warehouse with 2 reviews. Apache Hadoop is rated 7.8, while Kovair Data Lake is rated 8.0. The top reviewer of Apache Hadoop writes "Handles huge data volumes and create your own workflows and tables but you need to have deeper knowledge". On the other hand, the top reviewer of Kovair Data Lake writes "Ability to interact with teachers in real-time and manage lessons after class". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Teradata, whereas Kovair Data Lake is most compared with Oracle Exadata. See our Apache Hadoop vs. Kovair Data Lake report.
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We monitor all 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.
