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For example, by using MarkLogic to handle semi-structured data directly, I have reduced ETL prep and transformation time by roughly 30 to 40 percent, freeing up engineers to focus on more value-added tasks instead of manual data cleaning.
This led to roughly a thirty to forty percent reduction in backend development effort.
In metrics, I think they save three or four hours now daily because we have really enabled them to have the data in real time instead of waiting for another day.
Technical support is very costly for me, accounting for twenty-five to thirty percent of the product cost.
I would rate customer support 10 out of 10.
I would rate MarkLogic's customer support an eight due to its responsiveness, especially for higher priority issues.
Once the system was set up, it was quite stable, and most issues could be handled internally.
It is provided as a pre-configured box, and scaling is not an option.
Overall, it scales well, but getting the best performance depends on how well you design and configure it.
In production, when you get to know that your data is increasing and you need to add one more node, that is not easy and not straightforward.
The system handles this increase in XML workloads well, and flexible indexing helps maintain query performance even as datasets expand.
The built-in replication and failover features also help maintain uptime, ensuring the system stays operational even during maintenance or updates.
We used to have production issues, mainly due to MarkLogic failures.
The cloud version is only available in AWS, and in the Middle East, it is not well-developed in the Azure environment.
You do not need to worry about maintaining your own servers or provisioning your own servers. You simply log in and tell MarkLogic you want a certain number of clusters or nodes in a cluster and what cloud provider you want to use, then click okay, and they will build it for you.
Tooling and ecosystem support do not feel as rich as mainstream databases such as Hive or SQL servers in terms of connectors and integration or community resources.
A better UI with more features on it, something user-friendly, would be beneficial.
The initial setup cost is moderate to high, mainly due to infrastructure provisioning, licensing costs, and initial configuration and onboarding efforts.
MarkLogic is quite costly, and they are looking to move away in the longer run for that reason.
I believe the pricing and licensing are definitely on the higher side compared to open-source alternatives.
It operates as a high-speed data warehouse, which is essential for handling big data.
It has a very rich search and cts APIs to build search engines on large datasets.
I personally appreciate the built-in search feature because it indexes all data immediately upon ingestion for rapid searching, so we can perform full-text, phrase, or geospatial searches.
Its flexible schema and indexing capabilities allow me to index anything, including nested elements, which speeds up queries and reduces the need for custom code.
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 6 |
| Large Enterprise | 33 |
| Company Size | Count |
|---|---|
| Small Business | 5 |
| Midsize Enterprise | 3 |
| Large Enterprise | 11 |
IBM Netezza Performance Server offers high performance, scalability, and minimal maintenance. It seamlessly integrates SQL for efficient data processing, making it ideal for enterprise data warehousing needs.
IBM Netezza Performance Server is known for its outstanding data processing capabilities. Its integration of FPGA technology, compression techniques, and partitioning optimizes query execution and scalability. Users appreciate its appliance-like architecture for straightforward deployment, distributed querying, and high availability, significantly boosting operations and analytics capabilities. However, there are areas for improvement, particularly in handling high concurrency, real-time integration, and specific big data functionalities. Enhancements in database management tools, XML integration, and cloud options are commonly desired, along with better marketing and community engagement.
What are the key features of IBM Netezza Performance Server?Industries rely on IBM Netezza Performance Server for robust data warehousing solutions, particularly in sectors requiring intensive data analysis such as finance, retail, and telecommunications. Organizations use it to power business intelligence tools like Business Objects and MicroStrategy for customer analytics, establishing data marts and staging tables to efficiently manage and update enterprise data. With the capacity to handle large volumes of compressed and uncompressed data, it finds numerous applications in on-premises setups, powering data mining and reporting with high reliability and efficiency.
MarkLogic offers robust capabilities for data storage and retrieval, supporting multiple formats like XML and JSON. Its built-in search and indexing facilitate rapid data querying, making it efficient for industries demanding quick data management solutions.
Boasting flexibility in data management, MarkLogic supports XML and JSON formats without strict schemas, integrating storage and search within a single platform to reduce complexity. This configuration enhances data handling, performance, and development speed. Industries like publishing, insurance, and healthcare benefit from its real-time processing, enabling tasks that range from creating PDFs to complex backend services. While users appreciate these capabilities, suggestions include interface modernization and better integration with tools like VS Code and IntelliJ.
What are MarkLogic's standout features?MarkLogic sees extensive use in publishing, insurance, and healthcare, where it aids in real-time processing, querying, and transformation of data. Its indexing and search capabilities allow efficient management of semi-structured data, smoothing tasks from document creation to backend solutions, without necessitating extensive migrations.
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