MarkLogic Server vs. Scylla

If you are storing META data then MarkLogic is super useful as it retrieves everything so fast, while storing the whole data shows performance issues some times. If you have legacy systems then migrating from it would really require sweat and blood, on the other hand if you are in systems like Node.js you can simply integrate two systems easily. If you don't know how in the end your your data schema will look like then it's better to make a prototype using MarkLogic.

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.

• Search was really advanced. Hard to set up and had limitations about semantical meanings between xml nodes, but provided very good search abilities.
• The organization of documents across collections and metadata was particularly useful.
• The REST abilities were very advanced and worked with XQuery well.

Incentivized

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

• MarkLogic still has a long way to go in fostering the developer community. Many developers are gravitating to the simple integrations and do not delve into the deeper capabilities. They have made tremendous strides in recent months and I am sure this will improve over time.
• Many of the best features are left on the floor by enterprises who end up implementing MarkLogic as a data store. MarkLogic needs to help customers find ways to better leverage their investment and be more creative in how they use the product.
• Licensing costs become a major hurdle for adoption. The pricing model has improved for basic implementations, but the costs seem very prohibitive for some verticals and for some of the most advanced features.

Incentivized

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

MarkLogic is expensive but solid. While we use open source for almost everything else, the backend database is too critically important. At this point, re-tooling for a different back end would take too much time to be a viable option.

Incentivized

Very little about it can be done better or with greater ease. Even things that seem difficult aren't really that bad. There's multiple ways to accomplish any admin task. MarkLogic requires a fraction of administrative effort that you see with enterprise RDBMS like Oracle. MarkLogic is continually improving the tools to simplify cluster configuration and maintenance.

Incentivized

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.

There's always room for improvement. Some problems get solved faster than others, of course. MarkLogic's direct support is very responsive and professional. If they can't help immediately, they always have good feedback and are eager to receive information and details to work to replicate the problem. They are quick to escalate major support issues and production show-stopping problems. In addition to MarkLogic's direct support, there are several employees who are very active among the community and many questions and common issues get quick attention from helpful responses to email and StackOverflow questions.

Incentivized

The Scylla cloud support team is incredibly responsive and proactive.

We had Fast in place when Microsoft had bought it up and was going to change / deprecate it. One of the biggest advantages of MarkLogic for search actually had to do with the rest of the content pipeline - it allowed us to have it all in one technology. On the NoSQL side, we looked at MongoDB a couple years back. At that time, MarkLogic came in stronger on indexing, transaction reliability, and DR options. For us, that was worth using a commercial product.

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.

• MarkLogic reduced the amount of time that the DevOps team needed to dedicate to database updates, as the engineering team was mostly able to easily design and maintain database upgrades without requiring specialists such as database architects on the DevOps side. This capability flowed from the product's speed and the versatility of its XQuery language and libraries.
• MarkLogic required significant education and buy-in time for the engineering team.

Incentivized

• Addresses latency requirements of our platform