HPE Data Fabric vs. ScyllaDB

If you need Hadoop and just need raw speed for I/O and have a Hadoop savvy group of engineers who don't need/like web UIs, then MapR is a great fit for you. If you are new to Hadoop or have DevOps folks that are not Hadoop gurus, choosing MapR as your Hadoop vendor will have a steeper learning curve as you will need to do more training and build more admin consoles for them.

Incentivized

Scylla is well suited for high-throughput scenarios where keyed data must be read or written with consistently low latency. It's less appropriate for use cases requiring relational queries, secondary indexes, or more structured data sets.

• MapR allows easy integration with HBase and MapR DB.
• Easy trial server setup for product testing.
• Excellent training program to help new users get up-to-date with MapR and related products.

Incentivized

• Low-latency reads
• CQL has a familiar syntax
• Parity with Cassandra
• Practical features

• I think MapR's main problem is name recognition. Hortonworks and Cloudera both are big names in the industry, but their deployment mechanisms are a little more difficult to use, especially when trying to fully automate it's deployment.
• Documentation could always be better. But really, if that's your main weakness, it's everybody's weakness.

Incentivized

• Better documentation for best practices (e.g., how to effectively use connection pooling)

Very easy-to-understand syntax--uses CQL (same as Cassandra), which has many similarities to standard SQL. There are some gotchas, however, that must be known during schema development.

The Scylla cloud support team is incredibly responsive and proactive.

Hortonworks and Cloudera are both sort of hacky. We have to do a lot of extra steps to automate those two. MapR has far fewer issues and doesn't force you into a once size fits all deployment scenario. There are multiple ways to deploy and some are more amenable to automation, MapR just has that in spades

Incentivized

Scylla has a quick learning curve (same as Cassandra) compared to other proprietary solutions like BigTable. It supports higher throughput and lower latency that other NoSQL databases like MongoDB, which sacrifice those features for more flexibility and unique features.

• Less manual intervention for maintaining a cluster.

Incentivized

• Addresses latency requirements of our platform