MarkLogic Server Professional, Scientific, and Technical Services Reviews & Insights

Score9 out of 10

18 Reviews and Ratings

Community insights

TrustRadius Insights for MarkLogic Server are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Business Problems Solved

MarkLogic is a versatile software used by various industries to implement solutions for their clients. It is utilized in publishing workflows, enterprise search, big data analytics, and the semantic web. Users have praised its powerful geospatial search feature, which efficiently searches locations based on latitude and longitude. MarkLogic's indexing and tokenization techniques contribute to the quick execution of search queries.

Healthcare organizations rely on MarkLogic as a backend store for patient records, enabling storage, retrieval, and updates. By using a micro-services approach with patient matching and search functionality, MarkLogic helps keep patients up-to-date across multiple hospitals. It also serves as a central store for companies dealing with large amounts of data across multiple clusters, providing efficient storage and search capabilities.

In the academic publishing field, MarkLogic is extensively used for end-to-end data flow, including metadata and full-text content. Its newer features like semantics and JavaScript support are leveraged to develop cutting-edge technology.

MarkLogic's multi-model approach, scalability, and exceptional performance in handling XML data make it a preferred choice. It is also employed for reporting purposes with potential for future OLTP and OLAP services. Companies utilize MarkLogic to create DataHubs that consolidate data from various sources, enabling business teams to leverage the data with BI tools.

The technology department at Zynx Health relies on MarkLogic as the primary database layer for clinical decision support analytics. MarkLogic's XML-based solution proves valuable in handling hierarchically structured and semi-structured healthcare data.

Reviews

3 Reviews

Professional, Scientific, and Technical Services

Close to perfect NoSQL DB

Rating: 9 out of 10

Incentivized

November 19, 2018

Use Cases and Deployment Scope

We are searching some restaurants near by any location. We have used the geospatial search feature of MarkLogic. For those who do not know what geopspatial search is the next 3 line is for you. Its search based on geo location using latitude and longitude as parameters. The whole world is divided into some grids by latitude and longitude. Using that feature each and every location can be presented by 2 numbers, one how far and in which direction is it from 00 degree latitude and from 00 longitude. Geospatial search is one of the great features of MarkLogic. It has some in-built features to calculate the distance of a data point from another data point provided that both data have latitude and longitude data present in it. Another features which I like about MarkLogic is - It is really efficient for searching. The time it takes for a search query to run is really less. Thanks to the Indexing and tokenization technique of ML.

Pros

MarkLogic supports fully ACID transaction and I think this is very rare in a NoSQL system.
The recent version of MarkLogic has Integration with Node.js, REST, JSON which has really made the developers life easier to build integrated systems.
MarkLogic provides superb documentation for us. It really helps to understand which features work how. Example is- the whole dedicated website for it. https://docs.marklogic.com/
From the point of infrastructure - Installation, configuration and deployment is very fast. Compared to RDBMS , it's really easy to scale MarkLogic horizontally by adding nodes.

Cons

The licence cost is HIGH.
The amount of space required to store the data seems high hence costly.
The compatibility with legacy system is not yet available. I feel this area needs to be improved very fast.

Likelihood to Recommend

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.

Prabhudayal Acharya

Senior Software Engineering in Information Technology at Mindtree (Information Technology and Services, 10,001+ employees)

Vetted Review

1 year of experience

View profile

MarkLogic- The Swiss Army Knife of data platforms

Rating: 10 out of 10

Incentivized

October 12, 2015

Use Cases and Deployment Scope

Our company is a business partner with MarkLogic. We have a large practice dedicated to MarkLogic and have worked with many of MarkLogic's largest clients in nearly all verticals implement solutions built on MarkLogic. We have broad expertise across all areas of MarkLogic and help companies architect and deliver robust solutions powered by this database platform. We solved all sorts of business problems including publishing work flows, advanced enterprise search, intense big data analytics, and the semantic web. MarkLogic is a powerful platform for enterprise data hubs (operational data warehouses) and business tools. The "out of the box" search capabilities of this tool are unrivaled.

Pros

MarkLogic does everything well, but search is the "bread and butter" operation. All data is indexed on-the-fly and the API's offer a multitude of ways to create incredibly powerful search applications. The search engine isn't bolted on- it's at the core of the database. Search suggestion, relevance, advanced grammar, spell correction (did you mean?), paginated search over massive numbers of records, etc. is all at the fingertips of the developer. The database scales to massive size and yet search returns sub-second results for the most complex search parameters.
High availability, disaster recovery, and scaling is handled incredibly well. In the AWS cloud, it is trivial to set up a MarkLogic system to elastically scale with data and request volume- truly elastic, adding nodes and removing them as needed. Databases can replicate to a remote datacenter in real-time to provide instant cut-over for datacenter loss. Clustered servers provide highly available replication of data to instantly recover from node failures.
Security is increasingly important as data takes center stage in an enterprise. MarkLogic's role-based security is baked in to every query. This is battle-hardened content control.
Flexibility is unrivaled. Any data can be stored reliably and securely in the MarkLogic database. Records can be stored as text, XML, JSON, or binary. All text, XML, and JSON is instantly indexed and the various strategies for indexing are easy to configured and well documented. MarkLogic is also a powerful semantic triple store. Unlike any other NoSQL solution, MarkLogic can handle full documents, graphs, key-value pairs, binaries, etc. in a single database, providing powerful and unique ways of combining enterprise data.

Cons

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.

Likelihood to Recommend

There are few situations where MarkLogic is not well suited, however there's certainly use cases where it is using a missile to swat a fly. Important considerations in the selection process include:

Mission critical nature of your data
Complexity of your data- do you have polystructured data?
Data volume- MarkLogic can handle few records, but it's really meant to house significant volumes of data.
Composition of your development team
Lifecycle of your system

Harry Bakken

Principal Consultant in Information Technology at Avalon Consulting, LLC (Information Technology and Services, 51-200 employees)

Vetted Review

6 years of experience

Can You Bet on It?

Rating: 9 out of 10

Incentivized

October 7, 2015

Use Cases and Deployment Scope

MarkLogic is the database engine at the core of my company's flagship product. We use it to store and search approximately 100 TB of data across approximately 10 separate clusters. It most cases we can produce search results with amazing speed.

Pros

MarkLogic is fast and flexible. The data does not have to be structured (particularly in advance).
MarkLogic is a combination database, search engine, and application server. As a database, it is ACID compliant which is absolutely essential for mission critical production applications.
MarkLogic is dependable, in almost all cases recovers by itself, and is relatively easy to administer.

Cons

MarkLogic is not cheap, either for the software itself, the hardware to run it on, or the investment in learning necessary to use it effectively. While MarkLogic has gone to great pains to add multiple interfaces so that a deep understanding of XQuery is theoretically not necessary, I feel XQuery is essential to understanding the product well enough to use it in production applications.
Specifically, it is my understanding that switching database contexts is expensive in terms of performance. There could be be a improvement in the ability to query across databases, or even across clusters, that could drive greater flexibility in design decisions.
The security model definitely could be improved to facilitate sharing users, roles, and permissions across clusters. Building your own security model to allow users access to data on different clusters is very complex and leads to a number of performance issues.

Likelihood to Recommend

The first question is [around] how rigidly your data is structured. If it is well structured and non-volatile, an RDBMS database is an alternative. If it is not well structured or the structure changes, a NoSQL database should definitely be considered. However, there is no free lunch. NoSQL requires a different mindset and skill set. It is easy to set up a prototype that runs but much harder to design it to really take advantage of the speed it is capable of. To be able to query 10TB of data and get an accurate subsecond response is a thing of beauty. To be able to do it consistently requires a lot of deep technical knowledge. People with deep technical knowledge of MarkLogic, NoSQL, XML, and XQuery are in great demand. You will need a good plan to find, grow, and keep such talent.

Daniel Davenport

Search Engineer in Engineering at Catalyst Repository Systems (Information Technology and Services, 201-500 employees)

Vetted Review

5 years of experience

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MarkLogic- The Swiss Army Knife of data platforms

Rating: 10 out of 10

Incentivized

October 12, 2015

Use Cases and Deployment Scope

Pros

MarkLogic does everything well, but search is the "bread and butter" operation. All data is indexed on-the-fly and the API's offer a multitude of ways to create incredibly powerful search applications. The search engine isn't bolted on- it's at the core of the database. Search suggestion, relevance, advanced grammar, spell correction (did you mean?), paginated search over massive numbers of records, etc. is all at the fingertips of the developer. The database scales to massive size and yet search returns sub-second results for the most complex search parameters.
High availability, disaster recovery, and scaling is handled incredibly well. In the AWS cloud, it is trivial to set up a MarkLogic system to elastically scale with data and request volume- truly elastic, adding nodes and removing them as needed. Databases can replicate to a remote datacenter in real-time to provide instant cut-over for datacenter loss. Clustered servers provide highly available replication of data to instantly recover from node failures.
Security is increasingly important as data takes center stage in an enterprise. MarkLogic's role-based security is baked in to every query. This is battle-hardened content control.
Flexibility is unrivaled. Any data can be stored reliably and securely in the MarkLogic database. Records can be stored as text, XML, JSON, or binary. All text, XML, and JSON is instantly indexed and the various strategies for indexing are easy to configured and well documented. MarkLogic is also a powerful semantic triple store. Unlike any other NoSQL solution, MarkLogic can handle full documents, graphs, key-value pairs, binaries, etc. in a single database, providing powerful and unique ways of combining enterprise data.

Cons

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.

Likelihood to Recommend

There are few situations where MarkLogic is not well suited, however there's certainly use cases where it is using a missile to swat a fly. Important considerations in the selection process include:

Mission critical nature of your data
Complexity of your data- do you have polystructured data?
Data volume- MarkLogic can handle few records, but it's really meant to house significant volumes of data.
Composition of your development team
Lifecycle of your system

Harry Bakken

Principal Consultant in Information Technology at Avalon Consulting, LLC (Information Technology and Services, 51-200 employees)

Vetted Review

6 years of experience