Cloudera DataFlow (CDF) is a scalable, real-time streaming data platform that ingests, curates, and analyzes data for key insights and immediate actionable intelligence. DataFlow addresses the following challenges:

  • Processing real-time data streaming at high volume and high scale
  • Tracking data provenance and lineage of streaming data
  • Managing and monitoring edge applications and streaming sources
  • Gaining real-time insights and actionable intelligence from streaming data

Through our OEM partnership with Cloudera, IBM provides a one stop shop experience for organizations seeking for simpler procurement, licensing, and support to accelerate time to value.

Read this blog on deeper integration →

Read ESG First Look on Data in Motion with Cloudera DataFlow and IBM


Stream in any-to-any environments

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Deliver high-scale data ingestion, transformation and management to enterprises.

Accelerate onboarding

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Bring together and speed onboarding for data and AI, application development and administration teams.

Deploy anywhere

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Implement edge-to-cloud streaming data across on-premises, public cloud and hybrid cloud environments.


Discover the features of Cloudera DataFlow with IBM

Edge and flow management

Manage, control, and monitor the edge for streaming and IoT initiatives and deliver real-time streaming data with no-code ingestion and management.

Stream messaging

Buffer and scale massive volumes of data ingests to serve the real-time data needs of other enterprise and cloud applications.

Stream processing and analytics

Empower real-time insights to improve detection and response to critical events that deliver valuable business outcomes.

Use cases

Logging modernization

Unlock the value of machine-generated data with CDF’s Logging Modernization.

Logging Modernization is a holistic approach toward unlocking the value of machine-generated data by lowering processing costs and enabling a range of new analytics use cases. This is achieved through real-time data ingestion, edge processing, transformation, and routing log data through to descriptive, prescriptive, and predictive analytics.   

Customer 360

Get a complete view of your customer by gathering their data from multiple sources.

One of the primary digital transformation initiatives across organizations is to understand the full picture of their customers. But customer data exists across multiple data sources. CDF’s data ingestion and messaging capabilities lets you ingest, combine, enrich, and process data from all these data sources seamlessly and delivers a full 360-degree view of your customer.

Real-time insights

Predict failures and take corrective actions in real time.

Your IoT or streaming analytics implementations are only as good as your ability to harness the value of the data you ingest in real time. IoT use cases like predictive maintenance or patient monitoring require the data to be instantly consumed and processed to generate predictive and prescriptive analytics in real time. These can be truly life-saving insights in some use cases.

Video resources

Episode 1: Data-in-motion video podcast

Explore the data-in-motion business context, definition and use cases with Cloudera DataFlow.

Episode 2: Data-in-motion video podcast

Learn about the data-in-motion technical approaches and benefits with Cloudera DataFlow.

Episode 3: Data-in-motion video podcast

Review the data-in-motion lessons learned and how to get started with Cloudera DataFlow and IBM.


Data-in-motion philosophy

Explore Cloudera’s data-in-motion philosophy to help business and technology decision makers evaluate and simplify their approach to streaming data across their enterprise.

Choose the right streaming engines

Learn about the operational considerations while evaluating streaming engines. Compare Flink, Spark Streaming, Kafka Streams and Storm for the right use case.

The best Kafka ecosystem today

Learn how Cloudera’s Kafka ecosystem ensures a sustainable and adaptable end-to-end streaming architecture.

Get started with Cloudera Dataflow with IBM