Apache HBase vs. DataStax Enterprise

HBase is well suited for streaming ingest, fast lookups, massive datasets, data warehouse lookup tables, RDBMS replacement, MongoDB replacement, key-value store, data scans, logs, JSON storage and some binary storage. My preferred use case is for storing data points like time series or data produced by sensors. I often use HBase when I need data available immediately and I am not looking for transactions. This is a great store for really wide tables with tons of columns. It is also great if you are not sure what type of data you are going to have. It really excels at sparse data.

Incentivized

DataStax has a good scalable option with multiple clusters and a good write rate. Cassandra also is improving and is an open-source technology that has good community support. The UI is also easy to understand and implement required functions.

Incentivized

• Scalable and truly non-relational data
• HBase operations run in real-time on its database rather than MapReduce jobs
• Scales linearly to support billions of rows with millions of columns

Incentivized

• Datastax Cassandra provides high availability and good performance for a database. It is built on top of open source Apache Cassandra so you can always somewhat understand the internal functioning and why.
• Datastax Cassandra is fairly simple to start using, you can install/setup your cluster and be productive in 1 day.
• Datastax Cassandra provides a lot of good detailed documentation, and when starting, the detailed free videos on the Datastax site and documentation are very helpful.
• Datastax Enterprise Edition of Cassandra provides more tools, good support, and quick response SLA for enterprise business support.

Incentivized

• Write performance
• Performance support for parquet file format. supports, but performance wise still not there
• API / library availability for spark, rather than creating a new library for it

Incentivized

• Cassandra is a bit difficult to learn and understand
• The costs are slightly higher for our company
• Hardware requirement is moderate to high at the beginning

Incentivized

There's really not anything else out there that I've seen comparable for my use cases. HBase has never proven me wrong. Some companies align their whole business on HBase and are moving all of their infrastructure from other database engines to HBase. It's also open source and has a very collaborative community.

Incentivized

We will continue to use it because it scales well with commodity hardware and we are satisfied with the documentation and support.

Incentivized

There is a bit of a learning curve and tasks that are simple in traditional RDBMS systems can be complicated with DataStax Enterprise but once you get the hang of denormalizing data and getting the data model correct DataStax Enterprise is very usable. Usability from the developer's standpoint is very simple - the complication is on the architecture side with the data model.

We have had a few situations where we caused an outage or something has gone wrong and we are able to get a support person to offer live help within minutes. The escalation process is excellent - the best I've seen - and the support team is incredibly strong. Outside of emergencies, the team is very helpful with general questions and working through data model exercises and the subscription I believe still comes with some hours to help get the data model reviewed.

Compared NoSQL databases with traditional databases for faster retrieval and consistency. As MongoDB is a NoSQL supports dynamic fields, however, query performance is bad for aggregations and added maintenance. When compared with MySQL and Teradata, it could not scale up as fast as Hbase and added cost involved to it. HBase can be easily scalable to a huge volume of records, have a faster lookup and provides consistency

Incentivized

I believe DataStax Enterprise is the best in class. There are some things that are different with the schema-less systems but I found DataStax Enterprise easiest to implement while evaluating. The replication is on par or better than others in practice. We are evaluating Astra in our test environment and that has additional benefits we are looking forward to using.

• Positive: Open source, easy to use, good to store big data.
• Negative: SQL functionalities are not available.
• More memory utilization
• More troubleshooting

Incentivized

• Highly Scalable Database, Highly Available Services, and Platforms.
• High Performance, Low Latency and Highest throughput across varying workloads.
• Configured, Tuned and Monitored correctly works to provide the best user experience!
• Negative: Maintenance and Debugging Corner Cases

Incentivized