

BigID Next and Cloudera Data Platform are key competitors in the data management and security category. While BigID Next excels in advanced AI tools and cloud-based updates, Cloudera Data Platform has strong integration capabilities with open-source components for effective large-scale data management.
Features: BigID Next provides advanced machine learning and AI development tools with extensive scanning capabilities, excellent data mapping visibility, and numerous predefined classifiers for sensitive data. Cloudera Data Platform leverages open-source Hadoop components, focusing on robust data storage, processing, and analytics capabilities, integrating tools like Spark and Ranger for comprehensive data management.
Room for Improvement: BigID needs better automation, improved data connection configuration, scan reliability, UI navigation, and enhanced security features. Cloudera Data Platform should improve ease of use, stability, and security, and enhance support for AI, cloud storage integration, and governance initiatives.
Ease of Deployment and Customer Service: BigID Next offers flexible deployment options in public, private, and hybrid clouds with generally good technical support but sometimes faces delays. Cloudera Data Platform has flexible deployment with strong integration of open-source components and generally responsive customer service, though it can vary in speed.
Pricing and ROI: BigID is considered expensive due to its modular approach but delivers good ROI in compliance and data management efficiencies. Cloudera Data Platform, while complex in pricing, is often more cost-effective, providing solid ROI by supporting large-scale data management needs at a potentially lower cost.
It is one of the best tools in the market.
We have seen returns across all three aspects: fewer employees needed, money saved, and time saved with BigID.
I have seen a return on investment from using BigID, particularly as it is a regulatory and compliance tool that helps avoid potential penalties for non-compliance.
There are licensing costs that have been saved when we moved some of the data platforms, decommissioned them, and moved on to this platform.
In terms of return on investment, I see great changes in operational effectiveness measured by RTO when comparing on-premises solutions with cloud solutions.
A specific example of the positive impact of Cloudera Data Platform is the clearly saved time and improved performance, which is the main result of it.
BigID has one of the best technical support teams.
I would rate the customer support a six because you cannot directly reach out to L3 or L2 support if there's a major issue.
developing the custom connectors was relatively easy because of the courses I attended at BigID University and the support given by the BigID engineering team.
I would rate the customer support of Cloudera Data Platform ten out of ten.
I have communicated with technical support, and they are responsive and helpful.
Cloudera support is timely and responsive, adhering to the SLAs they provide.
I have added very large data sources to the BigID environment, and it remains stable.
BigID is scalable, allowing you to purchase as many scanners as you want.
CDP allows for easy, mostly automated scalability where I can schedule job workflows, fine-tune system resource metrics, and add nodes with just a click.
They have the cloud burst feature available where if the on-premises capacity is not sufficient at a point in time, you can run that Spark job on the cloud itself.
The ability to scale processing capacity on demand for batch jobs without impacting other workloads, and support for a growing number of concurrent users and teams accessing the platform simultaneously are significant advantages.
BigID is generally stable, however, there is a noted issue with bulk tagging that can affect scan results.
Sometimes the end user is not experienced or does not have all the expertise related to Cloudera specifically, making it very difficult to manage properly
Sometimes a node goes down, but it automatically returns to a healthy state.
Cloudera Data Platform is pretty stable in my experience; there are not any downtime or reliability issues.
There is also an issue with incident tagging, where all objects get tagged without an option to untag specific ones, and reverting changes is only possible through MongoDB Central, which can lead to data loss for certain periods.
I want them to focus on data mapping, assessment, automation workflow, and privacy incident management.
BigID deserves recognition for the data discovery part, which has been wonderful and quite accurate, along with the confidence level process that allows us to fine-tune results for better accuracy from the database.
We aim to address these issues with a Kubernetes-based platform that will simplify the task of upgrading services.
Cloudera Data Platform should include additional capabilities and features similar to those offered by other data management solutions like Azure and Databricks.
Cloudera Data Platform can be improved by addressing the feasibility of using it in the cloud; there are some complexities around the components used in cloud by Cloudera Data Platform that are not really convenient.
BigID might be expensive as it involves various paid services, like data retention and graphic rights management.
The pricing is competitive in the market, however, I need to ask for the right price.
Initially, CDH had a straightforward pricing model based on nodes, but CDP includes factors like processors, cores, terabytes, and drives, making it difficult to calculate costs.
We find Cloudera Data Platform to be cost-effective.
So far, I would say that it is competitive pricing that we have received.
One of the best features of BigID is its strength in data discovery and governance.
BigID simplifies things by integrating both data protection and data privacy in one environment, making it easier for end users.
The most valuable feature of BigID is its large number of classifiers, which allow us to scan for specific data such as SSN numbers.
By using the Hadoop File System for distributed storage, we have 1.5 petabytes of physical storage with 500 terabytes of effective storage due to a replication factor of three.
The Ranger integration makes it more flexible and reliable for me by allowing control over data access, specifying who can access at what level, such as table level, masking, or data layer level.
What stands out the most in Cloudera Manager are SDX, which provide centralized control for governance, security, and data lineage across multiple sources.
| Product | Market Share (%) |
|---|---|
| BigID | 2.7% |
| Cloudera Data Platform | 0.7% |
| Other | 96.6% |


| Company Size | Count |
|---|---|
| Small Business | 5 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 7 |
| Large Enterprise | 26 |
BigID Next offers advanced data discovery, classification, and governance tools, streamlining compliance with privacy laws while integrating seamlessly with Microsoft platforms.
BigID Next provides comprehensive data management through machine learning-enhanced capabilities, supporting data discovery and classification for both structured and unstructured data. By simplifying processes for GDPR and CCPA compliance, and facilitating data scanning and mapping across databases, it optimizes data management. Automation is central to its design, with solutions for DSAR requests, organizing data with security labels, and ensuring a holistic organizational data view. Improvements in navigation, bug fixes, and scan reliability remain essential, along with enhancing classifiers for broader region coverage.
What features does BigID Next offer?BigID Next is commonly implemented in industries needing robust data governance, such as finance and healthcare, where data privacy and compliance with regulations are critical. It aids in scanning and classifying extensive data volumes, helping businesses maintain regulatory compliance while managing data risks effectively.
Cloudera Data Platform offers a powerful fusion of Hadoop technology and user-centric tools, enabling seamless scalability and open-source flexibility. It supports large-scale data operations with tools like Ranger and Cloudera Data Science Workbench, offering efficient cluster management and containerization capabilities.
Designed to support extensive data needs, Cloudera Data Platform encompasses a comprehensive Hadoop stack, which includes HDFS, Hive, and Spark. Its integration with Ambari provides user-friendliness in management and configuration. Despite its strengths in scalability and security, Cloudera Data Platform requires enhancements in multi-tenant implementation, governance, and UI, while attribute-level encryption and better HDFS namenode support are also needed. Stability, especially regarding the Hue UI, financial costs, and disaster recovery are notable challenges. Additionally, integration with cloud storage and deployment methods could be more intuitive to enhance user experience, along with more effective support and community engagement.
What are the key features?Cloudera Data Platform is implemented extensively across industries like hospitality for data science activities, including managing historical data. Its adaptability extends to operational analytics for sectors like oil & gas, finance, and healthcare, often enhanced by Hortonworks Data Platform for data ingestion and analytics tasks.
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