Sold by: Cloudera
Metadata Management and Automated Data Lineage Platform
4.2
Overview
Data Lineage Made Easy
Cloudera Data Lineage simplifies data management with an AI-powered metadata platform that provides clear insights into data flow, making it easier to understand, trust, and manage your data.
With quick setup in under an hour, Cloudera Data Lineage automates metadata harvesting across complex hybrid ecosystems, saving time on tasks like debugging, data analysis, and troubleshooting. It helps reduce full-time equivalent hours spent on ad-hoc analysis, vendor management, and system issues.
Beyond immediate efficiency gains, Cloudera Data Lineage accelerates strategic initiatives like cloud migrations, data literacy, and risk management. It offers real-time insights into data lineage, helping track changes and flow across systems; from databases and pipelines to BI tools and ML models.
Key features include: Cross-platform metadata support with more than 50 technologies Upstream and downstream impact analysis to evaluate the ripple effects of data changes Entity-relationship exploration and data transformation lifecycle insights for in-depth understanding End-to-end asset lineage tracking for compliance, optimization, and root cause analysis Automated data discovery and technical documentation to streamline data management and decision-making
Cloudera Data Lineage supports self-service, data democratization, cloud transformations, and the reduction of technical debt, all while empowering teams with reliable, real-time data insights.
Highlights
- Our Automated Metadata Harvesting Agent provides a centralized metadata knowledge hub that empowers effective data governance, ensures regulatory compliance, and enhances data quality and control. It also supports digital transformation efforts and helps organizations prepare for AI readiness.
- Cloudera Data Lineage brings the world of Generative AI to data management, with Octomize, the AI automation agent built for data teams. Octomize empowers data teams with a real-time, unified workspace that automates, optimizes, and interprets scripts while providing immediate insights into data lineage and empowers data users to easily create business and technical documentation and debug and fix data errors in the Cloudera Data Lineage platform.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
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Dimension | Description | Cost/12 months |
|---|---|---|
Octopai | Tiered-based, 5 connectors, 10 Lineage users, 25 Catalog viewers | $100,000.00 |
Dimension | Cost/unit |
|---|---|
Additional connectors, users or viewers | $0.01 |
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No refunds available
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Customer reviews
Zaid Z.
Robust Analytics at Scale with a Clear UI and Smooth Integrations
Reviewed on Oct 17, 2026
Review provided by G2
What do you like best about the product?
To analyze distributed, high-dimensional matrices, work with large feature stores, and benchmark our models’ performance through pipelines, we need infrastructure that won’t collapse under heavy analytical throughput. The Cloudera Manager web interface is laid out in a clear, convenient way, so our teams can quickly check active nodes in the cluster, memory allocation, and running jobs without getting lost in terminal flags.
Integration between our computation notebooks, shared object storage buckets, and visualization engines is straightforward, and it fits cleanly into our automated data-ingestion pathways. Distributed Impala and Spark queries over terabytes of experimental rows are fast enough for our needs, which helps us extract data quickly even when several labs are running at the same time. Workload monitoring in the platform also detects malformed data frames and cluster partitions before any distributed training is carried out. The initial walkthroughs we received helped our practitioners get started smoothly.
Integration between our computation notebooks, shared object storage buckets, and visualization engines is straightforward, and it fits cleanly into our automated data-ingestion pathways. Distributed Impala and Spark queries over terabytes of experimental rows are fast enough for our needs, which helps us extract data quickly even when several labs are running at the same time. Workload monitoring in the platform also detects malformed data frames and cluster partitions before any distributed training is carried out. The initial walkthroughs we received helped our practitioners get started smoothly.
What do you dislike about the product?
Fine-tuning resource queues in YARN and administering security policies across different analytical teams isn’t an easy task without a platform engineer. Connecting specific high-frequency data streams to the cluster also runs into handshaking issues, especially during peak hours when batch processing is busy. The recommendations around cluster performance tend to focus more on generic server sizing than on optimizing machine learning workloads. On top of that, the occasional runtime exceptions are routed through slow ticketing queues, and the enterprise licensing is not something to take lightly.
What problems is the product solving and how is that benefiting you?
Previously, our group struggled with disconnected storage servers, untracked feature matrices, and pipeline crashes from out-of-memory conditions when parallel analytical scripts competed for local processing power. With Cloudera, our distributed feature sets, model-training registries, and benchmarking analytics are now centralized through the clear interface of Cloudera Manager. This gives our researchers a straightforward way to monitor job runs and cluster node status without needing to use the command line.
Native integration also works smoothly with our Python environments and cloud data buckets, so we can feed high-throughput data directly into Apache Spark and Impala without running into file-locking issues. Query performance has been extremely fast and reliable, even with terabytes of experimental data and the concurrent demands of day-to-day laboratory work.
In addition, the platform’s intelligent workload engine can detect skewed partitions and abnormal data frames before training cycles begin. Thanks to guided onboarding and dedicated technical assistance, our group was able to scale the infrastructure successfully and achieve a reliable operational return on investment by skipping unnecessary preprocessing steps and maintaining reproducibility across experiments.
Native integration also works smoothly with our Python environments and cloud data buckets, so we can feed high-throughput data directly into Apache Spark and Impala without running into file-locking issues. Query performance has been extremely fast and reliable, even with terabytes of experimental data and the concurrent demands of day-to-day laboratory work.
In addition, the platform’s intelligent workload engine can detect skewed partitions and abnormal data frames before training cycles begin. Thanks to guided onboarding and dedicated technical assistance, our group was able to scale the infrastructure successfully and achieve a reliable operational return on investment by skipping unnecessary preprocessing steps and maintaining reproducibility across experiments.
Duane H.
Consistently Fast Analytics Performance with Flexible Storage and Integrations
Reviewed on Oct 13, 2026
Review provided by G2
What do you like best about the product?
The software enables very fast data processing and is well optimized for analytics workloads. It offers solid storage capacity and integrates smoothly with many other services. I can store data from virtually any file source, and I really like the unstructured data filtering features. Overall, it delivers consistently fast performance.
What do you dislike about the product?
It has limited integration with certain services, and it takes time to become familiar with all aspects of the tool. Integrating legacy systems can be difficult, and at times it fails to connect to storage.
What problems is the product solving and how is that benefiting you?
We frequently use it to store raw data, and it helps us manage and store large volumes efficiently. It allows us to keep vast amounts of information and supports a wide range of storage capacities. We also use it to maintain the right data in the warehouse, and the product continues to deliver constant innovation.
Henry M.
Effortless Scaling for Large Datasets and Workloads
Reviewed on Oct 09, 2026
Review provided by G2
What do you like best about the product?
I love the platform’s scalable processing for large datasets because it helps me manage workloads efficiently. It makes it easier to handle large volumes of data and offers very straightforward scaling capabilities. Overall, I really appreciate how simple it is to scale when needed.
What do you dislike about the product?
The configuration can be confusing at times, and I occasionally run into cluster-related issues. Application logs aren’t easy to access or view, which makes troubleshooting harder. Scaling also isn’t always immediate and can be delayed.
What problems is the product solving and how is that benefiting you?
It helps us save time during development and provides strong flexibility for data processing. It has enabled us to optimize our processes, and we rely on it to store large volumes of data. It also reduces infrastructure management efforts and helps us manage workloads efficiently.
Ahmad A.
Impressive Open Data Architecture That Unifies Multiple Sources for AI
Reviewed on Oct 08, 2026
Review provided by G2
What do you like best about the product?
I love its open data storage architecture, and the data preparation for AI is impressive. It gathers data from various sources into a single location, and I really appreciate how well it unifies data from multiple sources. It also provides access to source data for AI, and several aspects of the overall approach are particularly compelling.
What do you dislike about the product?
Optimizing performance can take some expertise, and troubleshooting may require a fair amount of effort. The interface can feel a bit complex at first, and I think the onboarding process would benefit from more guidance to help new users get started.
What problems is the product solving and how is that benefiting you?
Performance has improved, and it supports a wide range of analytical workloads. It helps me gather data from various sources and has made our workflow design much simpler. I’m able to analyze data more efficiently and spend less time on data preparation. It also helps us organize and process large volumes of data in a more structured way.
Information Technology and Services
Cloudera Pros and Cons
Reviewed on Sep 09, 2026
Review provided by G2
What do you like best about the product?
A hybrid data platform approach that enables organizations to manage, govern, and analyze data across on-premises systems, public clouds, and edge environments in a more unified and consistent way. Its main strength is the unified platform for data engineering, analytics, and AI.
What do you dislike about the product?
Cloudera could be complex and expensive, especially for organizations that do not need a full enterprise-scale data platform. Its steeper learning curve, and the need for skilled teams to manage it.
What problems is the product solving and how is that benefiting you?
To help handling large, messy, distributed data that is too expensive or too rigid for traditional data warehouses. It helps organizations store, process, govern, and analyze data across different systems while keeping batch, SQL, search, and real-time workloads in one platform.