What is Streaming Analytics?

by Cloudera

Posted in Business | April 20, 2021 4 min read

What is Streaming Analytics?

Streaming Analytics is a type of data analysis that processes data streams for real-time analytics. It continuously processes data from multiple streams and performs simple calculations to complex event processing for delivering sophisticated use cases. The primary purpose is to present the most up-to-date operational events for the user to stay on top of the business needs and take action as changes happen in real-time. Streaming Analytics can be used in many industries:

Healthcare: Monitoring hospital patients to get the latest and most actionable data to inform patient interactions better.

Cybersecurity: Collecting and analyzing logs from thousands of devices and detect fraudulent activity in real-time.

Manufacturing: Process millions of messages per minute from IoT devices and sensor data and use ML models to enhance the speed of production.

Telecom: Increase customer satisfaction and reduce support call center cost when automating the classification of distributed outages geographically. The field maintenance crew can easily isolate the root cause and fix the problem immediately.

Finance/Banks: Modernize bank’s customer application by incorporating streaming capabilities to millions of customer interactions without failure and better customer experience.

Transportation: Monitor truck health and performance from smartphones and tablets, prioritize needed reports, and quickly identify the nearest dealer service locations.

Retail Service: Using real-time analytics to help brands, marketers, and retailers understand and learn how customers behave while they shop in the store.

What are the business challenges with today’s data?

For many years, batch processing has been the primary approach to deliver data for analysis. The data is often structured and doesn’t vary much. In today’s demand for more business and customer intelligence, companies collect more varieties of data — clickstream logs, geospatial data, social media messages, telemetry, and other mostly unstructured data. Companies tried processing these data through batch processing but saw workloads run much slower from hours to days. IT teams tried solving the problem by adding more clusters but noticed the rising cost for infrastructure and struggled to hire the right talent to manage them. The biggest challenge for businesses is to navigate to the future that needs real-time business intelligence. Yet, the information will always delay if they leverage an analytics system built for the past.

What are the advantages of Streaming Analytics?

Streaming Analytics transforms business information from a week ago to what is happening now. It can access data from inside the business, like ERP and asset management, outside sources, like edge devices and external assets, and correlate them for real-time predictive maintenance. Streaming Analytics leverages static data and data streams to help businesses understand the past and stay agile to handle today’s and tomorrow’s business challenges – all in real-time.

Why do some companies struggle to adopt streaming analytics?

Need Experts with Special Skills – The challenge with streaming analytics is that few experts are in the field and often hard to hire. The developers must understand lower-level languages like Java and Scala and be familiar with the streaming APIs. While developers may spend most of the time finding sources and wrangling data, the lack of a simplified method to interoperate with other applications and visualization tools makes deploying streaming analytics far difficult to be successful.

Security and Governance – Streaming data needs to be secured and obliged by the regional data protection laws for regulatory purposes. Enterprises usually don’t have the adequate resources to ensure their data streams are protected.

What is modern streaming architecture?

A modern streaming architecture consists of critical components that provide data ingestion, security and governance, and real-time analytics. The three fundamental parts of the architecture are:

Data ingestion that acquires the data from different streaming sources and orchestrates and augments the data from other sources
A messaging system that will guarantee delivery and track the consumption of messages by various consumers
A stream processing system that will allow for creating computations using these messages

How does Cloudera enable modern Streaming Analytics?

Cloudera DataFlow (CDF) enables streaming analytics by providing a scalable, real-time streaming data platform that collects, curates, and analyzes data, so enterprises gain immediate, actionable intelligence insights.

Edge and Flow Management, powered by Apache NiFi and MiNiFi, help collect data from the edge devices and transform and distribute the data stream for processing through an easy-to-use graphical interface with zero code required.
Streams Messaging, powered by Apache Kafka, buffers and scales massive volumes of data streams for streaming analytics. It provides advanced monitoring of Kafka topics and performs data replication of data streams to any environments: on-premises, hybrid-cloud and multi-cloud.
Stream Processing and Analytics, powered by Apache Flink with SQL Stream Builder, enables data analysts, developers, and data scientists with SQL expertise to easily create Continuous SQL for Streaming Analytics. It provides an advanced materialized view engine to interface with applications, toolings, and services via REST, making the deployment less complex and faster to deliver alerts, visual dashboards, and real-time analytics.
Shared Data Experience (SDX) provides the confidence and trust that the data stream is secured and governance across all components of Cloudera DataFlow.

How Cloudera DataFlow works with Cloudera Data Platform (CDP)?

Optimized for hybrid and multi-cloud, CDP seamlessly delivers the same streaming analytics capabilities across on-premises and public clouds for the enterprises.

With CDP, businesses manage and secure the end-to-end data lifecycle —collecting, enriching, analyzing, experimenting, and predicting with their data—to drive actionable insights and data-driven decision making. CDP separates data management from infrastructure strategy, enabling companies to move all data and apps from one environment to another without rewriting applications and retraining personnel.