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What is Stream Processing & Analytics?

The Stream Processing and Analytics capabilities within Cloudera DataFlow (CDF), powered by Apache Flink, help businesses democratize real-time streaming analytics across the organization. This also improves detection and response to critical events that deliver business outcomes. With the advent of IoT and other streaming sources, unbounded data streams and events are constantly flowing into the enterprise. CDF’s stream-processing capabilities help analyze them in real time, identify key event patterns, and escalate key alerts based on predictive insights and actionable intelligence.

By enabling key stakeholders with access to real-time data, business decisions are made much faster and create the right business impact.

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Use cases

  • Fraud Detection
  • Log Analytics
  • Customer Analytics

Fraud detection

Prevent millions of dollars in loss due to financial fraud by detecting it proactively. 

Enterprises across retail, financial services, and other sectors struggle to protect customer data and prevent financial fraud from happening.  Cloudera DataFlow’s Streaming Processing and Analytics capabilities can process real-time streams of customer transactions, identify patterns, create predictive alerts and actionable intelligence to prevent potential fraud.

PT Bank Rakyat Indonesia: Using big data, AI, and ML to better understand customers

Achieved a 40 percent reduction in fraud.

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Log analytics

Modernize your logging infrastructure to get real-time analytics.

Log data is increasingly valuable to enterprises. But IT organizations are struggling with effective log collection processes, distributing relevant information upstream, and generating key metrics. Cloudera DataFlow's Streaming Processing and Analytics capabilities help scale up log processing, deliver real-time insights across the firm, and significantly reduce operating costs.

Customer analytics

Real-time customer analytics improves engagement, retention, and satisfaction.

Every organization needs real-time analytics to improve customer engagement but struggles to implement it due to an excessive volume of data. Cloudera DataFlow’s Stream Processing and Analytics enables customer analytics by processing massive amounts of data with subsecond latencies while detecting customer interactions and recommending better offerings in real time.

Shoppermotion: Reinventing in-store analytics to give brick-and-mortar retailers meaningful insight

9% retail category sales increase

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Key features

Deliver real-time monitoring and parallel processing of millions of data points in subseconds. Distribute parallel processing, detecting patterns continuously without failure, to deliver predictive and prescriptive real-time insights.

Deliver real-time complex event processing across microservices, batch, and stream processing and analytics. Enable various windowing techniques to build sophisticated event-driven analytics, including the detection of mission-critical events that impact real-time decisions and automation.

Ease of use and scalability encourage adoption across the enterprise. Data analysts, who normally build analytics with SQL, can use the same querying language to adopt streaming analytics, and streaming developers can build streaming analytics using Java or Scala.

Set queries to handle event processing dynamically, base processing streams on state and time, and use watermarks to handle late and out-of-order delivery.

Use cases across industries and enterprises vary when it comes to real-time analytics. Cloudera supports three stream processing engines: Apache Flink, Spark Streaming, and Kafka Streams. Compare these engines for your use case in this white paper.

Getting started

Product documentation

Read technical specifications, architecture, tutorials, and how-to articles about Apache Flink.

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CDP Data Hub pricing

Evaluate CDP Public Cloud pricing for Data Hub across various instance types and cloud providers.

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Flink on the cloud

Extend your streaming processing and analytics capabilities to the cloud with CDP Data Hub.

Watch a quick intro video

Streaming analytics resources

Get all the key assets to learn more about the power of real-time stream processing and analytics.

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Cloudera Community on Flink

Connect with your peers, ask questions, troubleshoot, and learn more about Apache Flink.

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stream processing engine comparison

Choose the right solution by comparing Flink, Spark Streaming, Kafka Streams, and Storm.

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Flink PowerChat: An introduction to Apache Flink


Choose the right stream processing engine for your data needs

Analyst Report

The Forrester Wave™: Streaming Analytics, Q3 2019

Solution Brief

Data-in-motion philosophy: A blueprint for enterprise-wide streaming data architecture

World-class training, support, & services

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