Cloudera
As a Cloudera partner, Data Dynamics supports on-premises platform planning, deployment, operations and training.
From enterprise data to analytics and AI
Collect data from equipment and business systems, then run analytics and AI on a shared data foundation.
Read diagram, product and service details
- 1.Data sources
- Bring together sensor data, business databases, logs and documents.
- 2.Ingestion and delivery
- Configure NiFi data flows and Kafka event streams for the workload. They can operate as parallel paths.
- 3.Shared data foundation
- Manage data in Cloudera storage and processing services, and transform it with engines such as Spark.
- 4.Analytics and AI
- Use the same foundation for SQL/BI analytics and model training or inference as separate workloads.
Cloudera on-premises data platform
Cloudera provides a platform for storage, processing, analytics and AI. On premises, combine Cloudera Base and Data Services with SDX technologies for security and metadata management. Select services according to existing assets and data residency requirements.
- Cloudera Base: HDFS, Ozone, Hive, Impala, Spark, Kafka and core data services
- Cloudera Data Services: Kubernetes-based CDW, CDE, and Cloudera AI with separated compute
- SDX (Shared Data Experience): unified security and governance via Ranger and Atlas
- Integrated operations and monitoring through Cloudera Manager
Conceptual diagram. Design security, governance and integrations for the actual deployment and supported components.
Official documentationHow Cloudera Base and Data Services fit together
Separate the shared data foundation from service-specific compute to understand product and operational responsibilities.
Read diagram, product and service details
- 1.Cloudera Base
- Provide storage such as HDFS or Ozone and foundational processing services.
- 2.Data Services
- Kubernetes-based Data Warehouse, Data Engineering and Cloudera AI use the shared data foundation.
- 3.Security and governance
- Define access control and metadata management using SDX technologies such as Ranger and Atlas.
- 4.Operations
- Distinguish Cloudera Manager administration of base clusters from Data Services management.
CDP Private Cloud Base/Data Services and CML are former names. Confirm supported configurations and version compatibility during planning.
Official documentationCloudera CDP data, services and governance
Map Cloudera Base and Kubernetes Data Services with explicit management boundaries.
Read diagram, product and service details
- 1.Ingestion
- Connect databases, events and files through suitable NiFi or Kafka ingestion paths.
- 2.Cloudera Base
- Provide HDFS/Ozone, metadata and core processing services, operated through Cloudera Manager.
- 3.Data Services
- Warehouse, CDE and Cloudera AI independently access shared data and use Management Console administration.
- 4.SDX governance
- Apply identity/groups, Ranger policies, Atlas lineage and metadata management.
Illustrative on-premises composition. Verify Base, Kubernetes and service compatibility; cloud deployment requires its own design.
Official documentationCFM and Apache NiFi flow engineering
Detail processors, queues, back pressure, failure paths and data provenance.
Read diagram, product and service details
- 1.Collect
- Read databases, APIs and event files as FlowFiles and records.
- 2.Validate and route
- Transform and route by schema/attributes, regulate queues and separate quarantine from retry paths.
- 3.Deliver
- Send independently to S3 Parquet, Kafka or APIs and record status and object keys.
- 4.Operate and trace
- Separate FlowFile, content and provenance repositories; deploy versioned flows and parameter contexts.
Available components depend on the CFM/NiFi release. Retries alone do not guarantee exactly-once delivery; S3 uploads need an Iceberg metadata commit before querying them as an Iceberg table.
Official documentationCDE virtual clusters and Spark job execution
Connect code and dependency submission to Spark drivers/executors, quality checks and operations.
Read diagram, product and service details
- 1.Submit and orchestrate
- Attach versioned Python/JAR resources and dependencies to CDE jobs, scheduled directly or through Airflow DAGs.
- 2.Virtual clusters
- Run drivers and executors within team-specific CPU and memory ranges.
- 3.Process data
- Read supported storage, transform and validate data, then publish curated tables.
- 4.Observe and recover
- Inspect run IDs, logs, metrics and Spark History Server before reviewing failed-job retries.
Cloud and on-premises storage/runtime support differ. Validate virtual-cluster resource ranges, scaling limits and dependencies for the environment.
Official documentationCloudera AI development, registry and inference
Connect model development and evaluation with AI Registry and Inference service execution.
Read diagram, product and service details
- 1.Develop
- Train or adapt models with governed data and project environments; evaluate on separate holdout data.
- 2.Review and register
- Review experiments and register approved model versions and artifacts in Cloudera AI Registry.
- 3.Serve
- Configure supported NIM/Triton runtimes and endpoints in the KServe-based inference platform.
- 4.Operate
- Review request logs, inference metrics, quality and capacity; manage staged rollout and recovery.
Validate runtime, GPU, deployment and access-control support by release. RAG retrieval and prompt assembly belong to the example application, not an implied vector store in the inference service.
Official documentationCloudera services from Data Dynamics
Connect product and workload choices to assessment, validation and operational handover.
Read diagram, product and service details
- 1.Assess requirements
- Inventory existing systems, data, workloads and operating constraints to define scope.
- 2.Select products and configuration
- Review products, deployment, capacity, access and procurement scope for the intended workloads.
- 3.Validate and transition
- Check correctness, performance and integration with representative workloads, acceptance criteria and recovery procedures.
- 4.Operate and train
- Prepare monitoring, runbooks, upgrade plans and team training for operational handover.
Cloudera product portfolio
- Cloudera Base · Data Services
Combine the Cloudera Base data foundation with Kubernetes-based Data Services for data warehousing, engineering and AI workloads.
Cloudera Base · Data Services · SDX · Cloudera Manager
- CFM (Cloudera Flow Management)
Enterprise data flow management built on Apache NiFi. Hundreds of connectors ingest, transform, and deliver data in real time across edge, on-prem, and cloud.
Apache NiFi · NiFi Registry · MiNiFi · Flow Designer
- Cloudera AI
With Cloudera Machine Learning (CML) and Cloudera AI Inference, data scientists can train, deploy, and serve models on CDP data — including generative AI and RAG workloads.
CML · MLflow · Model Serving · LLM / RAG
Plan around your actual workload
Define scope using data requirements and operating conditions, not a feature checklist alone.
- Environment fit
Assess on-premises operations, existing Hadoop assets and data residency requirements.
- Sizing and configuration
Review data volume, ingestion rates, concurrency and Kubernetes or GPU requirements.
- Delivery and operations
Agree on assessment, architecture, migration validation, operating procedures and training.
Cloudera services from Data Dynamics
- License sales
As an official partner, we offer Cloudera Base/Data Services, CFM, and Cloudera AI licenses at competitive terms, with sizing and quotes tailored to your environment.
- Cloudera Base and Data Services licenses
- CFM (NiFi) licenses
- Cloudera AI (CML) licenses
- Workload-based sizing and TCO analysis
- Technical support
End-to-end technical support for CDP, CFM, and Cloudera AI — from solution design and implementation to incident response and performance tuning.
- CDP Private Cloud deployment and upgrades
- CDH/HDP to CDP migration
- Cloudera Manager, Ranger, and Atlas operations
- NiFi flow design and performance tuning
- CML environment setup and MLOps support
- Incident response and performance optimization
- Training
Hands-on Cloudera training for administrators, developers, and data scientists to help customers build internal expertise quickly.
- CDP administrator course
- Spark, Hive, and Impala developer courses
- NiFi (CFM) data flow hands-on course
- Cloudera Machine Learning hands-on course
- Customized workshops and on-site training
Illustrative adoption workflow. Confirm product/version availability, acceptance criteria and service scope for your environment and contract.