MarkLogic Multi-Model Database: Enterprise Edition v. 10

My main use case for MarkLogic  is that we have MarkLogic  as an enterprise-level database. We have many source systems that dump data into MarkLogic. Then we have DHF flows, which handle transformation, harmonization, mapping, and curation of this data and create a final record in MarkLogic. We also have REST APIs built in, so these REST APIs can consume the data that is in MarkLogic and can also send data from outside to MarkLogic. This can change the data that is already present in MarkLogic, and users can retrieve the data in MarkLogic. We also have TDEs built in. We have data that is already in MarkLogic, and the NoSQL data can be presented in the form of SQL in a tabular format using TDE, and this can be used by other systems such as Qlik and Power BI.

MarkLogic fits into my daily workflow as we frequently deal with both data ingestion and the API. There is another team that takes care of UI and other tools. In my daily workflow, from a customer perspective, MarkLogic is used in a client who is a manufacturer and processing plant. Things are deployed on MarkLogic, and this data is stored in MarkLogic. Using DHF, the data is stored in MarkLogic from different sources. We get the data and store it in MarkLogic. Then from the UI, many customers can interact with the data that is present, and if they want to make a change or add new things into the data, they can interact using APIs.

The best feature of MarkLogic is the built-in search capability. There are two search capabilities available: CTS search and search:search. Both search capabilities drive faster query results in MarkLogic, and it is really fast. We also have indexes present in MarkLogic where we can make the performance of the query faster. Indexes allow us to know exactly where to search for the data.

The data indexes and search capability impact my work in a very positive way. When we do any transformation, when we search any data, and when we retrieve the data, the search capability is a main thing in MarkLogic that enables us to find what we need. Even if we have 10 million data entries in our MarkLogic, when we search using the search capability and utilize the indexes to make the search faster, it is really easy to get the data we need from even billions of data. This is really saving us time when we want to search and retrieve the data.

MarkLogic has impacted my organization positively because we have seen efficient results. Instead of keeping the data in a lot of databases and MDM  databases with every source system trying to keep their data in some place and then fetch it and give results, now that we have started using MarkLogic, everybody wants to fetch the data from MarkLogic because this is the main place where the data is stored, it is easy for retrieval, and it is fast. The end-to-end applications in MarkLogic architecture are really fast when it comes to performance because MarkLogic gives responses in very quick real time. We have seen improved efficiency, and a lot of time is saved because we do not have to check with a lot of teams to get an understanding of the data. We can just check with MarkLogic and its relative data in MarkLogic itself.

MarkLogic can be improved by introducing a lot of languages to querying. Although I think it is self-sufficient if you know JavaScript and XQuery, for new people who want to onboard into MarkLogic, if Python, C, and these kinds of languages can be integrated and used on MarkLogic, then it will be a good product for everybody.

I have been using MarkLogic for about five years.

MarkLogic is stable.

MarkLogic is very good in terms of scalability. We can scale it horizontally as well as vertically.

The support resources and documentation for MarkLogic available on the website are really good, and I am satisfied. On a scale of 1 to 10, I will recommend 10 for the support.

We did not use any other solution before.

The setup process of MarkLogic is pretty straightforward. We have to install it and there are very few configurations to be made, and it is really easy if you have admin experience.

We have seen a return on investment with MarkLogic. We have saved 20% to 40% of time in data retrieval. Also, the discussions have been reduced when it comes to finding out how the data behaves because the data is stored in one place now.

As far as I know, I am not directly associated with pricing, setup cost, and licensing, but MarkLogic is a bit more expensive than other technologies available. However, the support is really good, and the product is really good.

We did not evaluate another options.

In comparison, for about one week, we are almost saving two days now. So two of five means we are saving 40% of time compared to discussing or trying to understand the data. So 40% of time is reduced.

The learning curve for new users or developers on MarkLogic is a bit tough at first, but afterwards it is really easy to understand for developers new to MarkLogic.

MarkLogic is very flexible when it comes to integrating with other tools or systems because they have a lot of authentication systems and connection systems available. We also have a lot of app servers with HTTP auth, OAuth 2.0, and basic digest authentication. MarkLogic is really flexible when it comes to integrating with other tools or systems.

MarkLogic is very good at handling larger data sets. As we already have billions of data, it is really handling it well. We just need to take care of the CPU, RAM, and storage.

MarkLogic handles heavy loads at peak times and performs well. It automatically manages itself with the configurations we can have, parallel processing, and load balancers in place. This takes care of the performance during heavy loads at peak times.

MarkLogic is also available in a free format, so you can deploy MarkLogic into your own systems and try it out to see how it works, how fast it is, and how it can solve your own data problems. It would be beneficial if you give it a try before purchasing.

Regarding MarkLogic's governance and security, I think data governance is really good. We can keep the data private to some roles, and we have user-based access control. We can give restrictions on the data and govern the data. Security-wise, MarkLogic is really good because without proper credentials, MarkLogic does not have any ways of getting hacked.

I would rate this review a 9 out of 10.

Private Cloud

Amazon Web Services  (AWS )

Our main use case for MarkLogic  is as a centralized repository where we store our data. MarkLogic  functions as a NoSQL database, allowing us to store XML, JSON, and text format data. MarkLogic is also a very fast database, providing really fast results when we query something.

We use MarkLogic on a daily basis. Our transactions include REST APIs that are created in MarkLogic. Day to day, we receive many calls that interact with and update the data in MarkLogic. We have the Data Hub Framework installed in MarkLogic, enabling data to come from multiple sources. We then tune this data and maintain it as a golden record after harmonization and curation.

MarkLogic is known for several strengths, particularly its multi-model database capability.

The multi-model database capability in MarkLogic can handle documents, graphs, and relational data all in one place. This is where MarkLogic has helped us considerably because a single database is able to accomplish a lot of enterprise work. We do not have to reach out to many products, as a single product having extensive capability is beneficial for our organization.

MarkLogic also covers many things around security and has strong search capability. The combination means we can confidently handle large, complex datasets in MarkLogic.

MarkLogic has had a very positive impact on our client and our organization. Because it is a multi-model database, we can handle multiple data types. A single platform takes all the data from all sources, reducing our dependency on multiple systems. The transactions in MarkLogic are ACID, providing atomicity, consistency, isolation, and durability of the transactions and data. We have used it for many purposes, including a stewardship platform that runs complex code in MarkLogic and helps us perform deduplication by identifying duplicate data.

We observed a significant improvement in data retrieval time with MarkLogic. Queries that previously took seconds or longer are now executed much faster because the data was previously in SQL and is now in a NoSQL XML format. We use CTS search, and the data we need already sits in the index, allowing us to query the documents we need. The indexing capability is an advantage that adds real value to saving query time.

Regarding AI capabilities and its governance and security, MarkLogic has very good security. AI usage is not extensive in MarkLogic, but it provides fine-grained access and role-based access to every document or even element level. This means AI models or applications can access data while we restrict them to see only what we want them to see. MarkLogic has built-in data governance features such as metadata management, data lineage, and auditing. These features help track where the data is coming from and how it is used, which is important when feeding data into an AI system to ensure trust and accountability.

MarkLogic itself is not an AI model, but it plays a critical role in providing high-quality, trusted data to an AI system. In terms of reliability, features like ACID transactions and consistency ensure that data remains correct and stable, which reduces the chance of incorrect and inconsistent AI results. Since AI is not heavily involved in MarkLogic, the data gives us correct results.

MarkLogic can be improved by allowing multiple language support, as it currently supports JavaScript, XQuery, and SQL, whereas other languages are not supported at this time.

Regarding improvements to MarkLogic, the learning curve is an area that could be enhanced. MarkLogic offers many features, but for new developers or teams unfamiliar with the ecosystem, it can take time to become comfortable with concepts like XQuery and server-side JavaScript. Improving onboarding resources or simplifying development workflows could help teams adopt it faster. MarkLogic has its own courses, but it would be better if there were more documentation and videos available.

I have been using MarkLogic for approximately four or five years.

How we query MarkLogic depends on our data structure and how we are trying to obtain the output from it.

Regarding improvements to MarkLogic, the learning curve is an area that could be enhanced. MarkLogic offers many features, but for new developers or teams unfamiliar with the ecosystem, it can take time to become comfortable with concepts like XQuery and server-side JavaScript. Improving onboarding resources or simplifying development workflows could help teams adopt it faster. MarkLogic has its own courses, but it would be better if there were more documentation and videos available.

I rate MarkLogic as a nine out of ten. I give it a nine out of ten because it is a very powerful and reliable platform with strong performance and offers flexibility and enterprise-grade capabilities. The only reason I would not give it a ten is because of the learning curve and some limitations in tooling and integrations. Overall, it is a highly capable and robust platform that has delivered strong business outcomes for us.

Private Cloud

Amazon Web Services  (AWS )

What do you like best about the product?

I like Progress MarkLogic's ability to handle multiple types of data on a single platform while providing very fast search and query performance. It's a big plus that it combines tools for document storage, search, and integration, simplifying the architecture significantly. I appreciate its flexibility since it supports schema and flexible data models, allowing new data sources to be onboarded quickly without a lot of time spent on schemas. The built-in search capability with indexing and full-text search is powerful and performs well even with large enterprise data sets, which improves the application's performance and user experience. I also appreciate its scalability and reliability. It supports clustering, high availability, and enterprise-grade security features, making it suitable for mission-critical applications. Overall, the flexibility, strong search capabilities, scalability, and enterprise security are what I really like about MarkLogic.

What do you dislike about the product?

One big challenge is the learning curve. Progress MarkLogic has so many advanced features like semantic search and multimodal capabilities, which can make it take a long time for new developers to fully understand the ecosystem, query language, and best practices. This can slow down onboarding compared to more commonly used databases. Another area that needs improvement is the cost. Being an enterprise-grade solution, the licensing and infrastructure costs are relatively high, which can sometimes make it hard for small teams to adopt it compared to open-source alternatives. Additionally, the development and debugging experience could be better. Troubleshooting complex queries or performance issues can require deep platform knowledge. More effective debugging tools, monitoring dashboards, and documentation would make daily operations easier. Also, because it's such a feature-rich platform, I sometimes end up using only a small percentage of its capabilities. So for simpler use cases, it can occasionally feel heavier than necessary.

What problems is the product solving and how is that benefiting you?

Progress MarkLogic centralizes data management, enhancing fast search and real-time retrieval. It streamlines complex data integration from multiple formats, improving consistency without heavy transformations. Its built-in search engine boosts query performance even with large datasets, reducing development effort while offering strong security features.