Cloudera Data Engineering
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What is Cloudera Data Engineering
Cloudera Data Engineering (CDE) is a cloud-native data engineering service within the Cloudera Data Platform that runs and orchestrates Apache Spark workloads for building, scheduling, and monitoring data pipelines. It targets data engineers and platform teams that need managed Spark execution, job orchestration, and integration with enterprise data lakehouse storage and governance. The service emphasizes Kubernetes-based isolation, elastic compute for batch/streaming jobs, and integration with Cloudera’s security and governance capabilities.
Managed Spark on Kubernetes
CDE provides a managed runtime for Apache Spark with Kubernetes-based resource isolation and scaling. This reduces the operational burden of maintaining Spark clusters and helps standardize execution environments across teams. It fits organizations that already run Cloudera Data Platform and want a consistent way to deploy Spark pipelines.
Built-in job orchestration
CDE includes scheduling and orchestration features for recurring pipelines and dependency-driven workflows. It centralizes job definitions, execution history, and operational monitoring in one service. This supports production data engineering use cases where repeatability and auditability matter.
Enterprise security integration
CDE integrates with Cloudera’s platform security controls (identity, access control) and governance-oriented services available in the broader platform. This can simplify compliance requirements compared with assembling separate open-source components. It is particularly relevant for regulated environments that need consistent policy enforcement across data and compute.
Not a full ML workbench
CDE focuses on data engineering and Spark execution rather than end-to-end model development and experimentation. Teams typically need additional tools for notebooks, feature engineering workflows, model training, and MLOps. As a result, it may not satisfy users looking for an all-in-one data science studio.
Platform dependency and lock-in
CDE is designed to operate as part of Cloudera Data Platform, so value is highest when an organization standardizes on the broader Cloudera ecosystem. Migrating pipelines to other managed Spark environments can require rework of integrations, security mappings, and operational processes. This can be a constraint for teams pursuing a multi-vendor platform strategy.
Operational complexity for newcomers
While managed, CDE still assumes familiarity with Spark, job packaging, dependency management, and production pipeline operations. Organizations without mature data engineering practices may face a learning curve in designing reliable pipelines and controlling costs. Some advanced capabilities (governance, security, observability) may require additional platform configuration and expertise.
Plan & Pricing
Pricing model: Pay-as-you-go (hourly CCU-based) Prices (CDP Public Cloud):
- Core: $0.07 per CCU/hour
- All-Purpose: $0.20 per CCU/hour
Example instance rates (from CDP Public Cloud service rates):
- AWS m5.2xlarge (4 CCU): Core rate/hr $0.2800; All-Purpose rate/hr $0.8000
- AWS m5.8xlarge (16 CCU): Core rate/hr $1.1200; All-Purpose rate/hr $3.2000
Notes:
- Pricing is expressed per Cloudera Compute Unit (CCU). CCU is a combination of core and memory; rates shown are estimates and vary by instance type. Official pages state prices do not include cloud infrastructure, networking, and related costs.
- Cloudera on-premises Data Engineering pricing is listed as Contact Sales (no published fixed rates on the vendor site).
- Additional related pricing found on official site: Observability $80/CCU (annual subscription); Data Visualization $2,000/user (annual); GPU Acceleration $7,500/CGU (annual).
Seller details
Cloudera, Inc.
Santa Clara, CA, USA
2008
Private
https://www.cloudera.com/
https://x.com/cloudera
https://www.linkedin.com/company/cloudera/
