Sold by: Cloudera
Deployed on AWS
An enterprise data cloud that manages, secures and connects the data lifecycle in AWS.
Cloudera delivers powerful self-service analytics across hybrid and multi-cloud environments, along with sophisticated and granular security and governance policies that IT and data leaders demand.
4.2
Overview
Cloudera on AWS is an enterprise data platform that is easy to deploy, manage, and use. By simplifying operations, Cloudera reduces the time to onboard new use cases. Cloudera manages data in any environment, including multiple public clouds, private cloud, and hybrid cloud. With Cloudera's Shared Data Experience (SDX), IT can confidently deliver secure and governed analytics running against data anywhere. Cloudera is a new approach to enterprise data, running anywhere from the Edge to AI.
Cloudera on AWS delivers easy-to-use analytics that support the most complex, demanding use cases
Complete: All functions needed to ingest, transform, query, optimize, and make predictions from data are available, eliminating the need for point products
Integrated: Unified analytic functions work together eliminating data silos and copies of data
Cloudera SDX technologies ensures and enterprise data cloud is secure by design:
Consistency: Security and governance policies are set once and applied across all data and workloads
Portability: Policies stay with the data even as it moves across all supported infrastructures
Pricing: Use of Cloudera on AWS requires a prepay commitment (in dollars) of cloud credits. For more information on usage rates and instance types, see cloudera.com/products/pricing.html.
You may use the platform until your commitment is consumed (used against prepaid commitment amount), any additional usage beyond the prepaid commitment will require negotiation with Cloudera for the purchase of additional prepaid credits.
Highlights
- Provides elasticity, agility, and ease of use for hybrid and public cloud by intelligently autoscaling workloads up and down for more cost-effective use of cloud infrastructure. Consistent user experience makes it faster and easier to analyze data.
- Optimizes the data lifecycle with multi-function analytics that solves demanding business use cases. Cloudera on AWS is composed of three primary services with a standardized user experience: Data Warehouse, Machine Learning and Data Hub for custom analytics.
- Ensures all workloads on the platform share common security, governance, and metadata. Users can efficiently find, curate, and share data, enabling self-service access to trusted data and analytics
Details
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Deployed on AWS
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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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Cloudera | Subscription Cloudera on AWS | $50,000.00 |
Dimension | Cost/unit |
|---|---|
Consumption by Customer based on Cloud usage | $0.01 |
Dimensions summary
This listing uses a contract pricing model with two dimensions. The Cloudera subscription dimension covers your commitment to the platform on AWS. The consumption dimension charges you based on actual cloud usage. Usage is measured in Cloudera Compute Units, which combine core and memory. This means your costs scale with how much you run, on top of the subscription. The two dimensions work together: one anchors your commitment, and the other tracks variable usage. Infrastructure, networking, and related cloud costs are billed separately and are not included in these units.
Top-of-mind questions for buyers
A CCU is a combined measure of processor cores and memory used by a service. You are billed per CCU based on how much you run. The exact CCU rate can vary by the instance types your workload uses. This unit tracks compute, not the workload's data volume.
The CCU rates cover Cloudera software only. Underlying infrastructure, networking, and other related cloud costs are not included in these units. Those charges depend on the AWS services you choose and are billed separately from your Cloudera consumption and subscription.
Both charges apply at the same time. The Cloudera subscription anchors your platform commitment on AWS. The consumption dimension adds variable charges measured in CCUs as you run workloads. Your consumption cost rises and falls with actual usage, while the subscription stays with your commitment.
www.cloudera.com
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
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Positive reviews
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Negative reviews
AI generated from product descriptions
Auto-scaling Capability
Intelligently autoscales workloads up and down across hybrid and public cloud environments for optimized cloud infrastructure utilization.
Multi-function Analytics Platform
Provides integrated data warehouse, machine learning, and custom analytics services with standardized user experience for ingesting, transforming, querying, optimizing, and making predictions from data.
Unified Security and Governance Framework
Implements Shared Data Experience (SDX) technology that sets security and governance policies once and applies them consistently across all data and workloads regardless of infrastructure.
Cross-infrastructure Policy Portability
Maintains security and governance policies with data as it moves across multiple public clouds, private cloud, and hybrid cloud environments.
Centralized Data Management and Discovery
Provides unified metadata management enabling efficient data curation, discovery, and sharing while eliminating data silos and redundant data copies.
Lakehouse Architecture
Unified data foundation built on lakehouse architecture providing open, unified foundation for data and governance with support for open standards and formats
Data Intelligence Engine
Powered by Data Intelligence Engine that enables organization-wide access to data and insights across all users and roles
Multi-Workload Unification
Consolidates data engineering, analytics, business intelligence, data science and machine learning workloads on a single common platform
Open Source Foundation
Built on open source data projects and open standards to maximize flexibility and interoperability across data ecosystem
Collaborative Capabilities
Native collaboration features enabling unified data teams to collaborate across entire data and AI workflow
In-Database Analytics Capabilities
Advanced in-database analytics including machine learning, geospatial, time series, graph, text, and path analytics built into the database engine
Multi-Format Data Support
Native support for structured, semi-structured, and unstructured data with integrated analytics and AI capabilities
Vector Embedding Management
Vector Store with SQL Interface for storing, managing, and querying vector embeddings directly in the platform to support RAG, semantic search, and generative AI use cases
Open Table Format Interoperability
Native support for Apache Iceberg and Delta Lake Open Table Formats enabling seamless interoperability with lakehouse architectures on shared object storage
Federated Query Engine
QueryGrid technology providing federated query access across Teradata and third-party systems with optimizer-driven pushdown across cloud and on-premises environments without data movement
Standard contract
No
No
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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.