Apache Hive is database/data warehouse software that supports data querying and analysis of large datasets stored in the Hadoop distributed file system (HDFS) and other compatible systems, and is distributed under an open source license.
N/A
Astra DB
Score 8.3 out of 10
N/A
Astra DB from DataStax is a vector database for developers that need to get accurate Generative AI applications into production, fast.
Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.
We use Astra DB to improve our management systems. Storing data has become hassle-free and quite simple. When launching a Cassandra-based cloud application, Astra DB is exactly what you need. In addition to the standard training programs and videos, the extended support and training require significant additional effort to activate and cover which I feel is a bit more tedious task.
Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
Relatively easy to set up and start using.
Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.
The support team sometimes requires the escalate button pressed on tickets, to get timely responses. I will say, once the ticket is escalated, action is taken.
They require better documentation on the migration of data. The three primary methods for migrating large data volumes are bulk, Cassandra Data Migrator, and ZDM (Zero Downtime Migration Utility). Over time I have become very familiar will all three of these methods; however, through working with the Services team and the support team, it seemed like we were breaking new ground. I feel if the utilities were better documented and included some examples and/or use cases from large data migrations; this process would have been easier. One lesson learned is you likely need to migrate your application servers to the same cloud provider you host Astra on; otherwise, the latency is too large for latency-sensitive applications.
Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.
Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.
Their response time is fast, in case you do not contact them during business hours, they give a very good follow-up to your case. They also facilitate video calls if necessary for debugging.
Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed
We know Astra is built on Cassandra / Kubernetes / Stargate and can work on any cloud. The competitors we reviewed are cloud specific and create a lock in. We also have the option to run Cassandra / Stargate ourselves if we wanted to. The competitors don’t give that option
Our use case and requirements for scaling don't allow me to comment on ability to scale to millions of users, however from what I know of other use cases where Astra has been applied, and Astra's architectural design makes me confident it will be able to scale to meet most requirements
The high availability capabilities of Astra DB can assist in reducing downtime, which is crucial for revenue-generating applications.
The developer-friendly features of Astra DB, as well as support for known query languages, can help expedite development, save development time, and minimize labor costs. This can result in a shorter time to market and a higher ROI.
