What is Streaming Analytics? Use Cases & Examples

As data grows in volume and velocity, streaming services have gained the focus of many business leaders. Modern organizations aim to gain instantaneous insights from there in hopes of taking early actions for business development. This has given rise to the concept of streaming analytics.

A stream analytics engine works with continuous data streams to provide updates regarding events just as they happen. With streaming analytics, businesses do not have to wait for weeks or months to obtain actionable reports. The low-latency system allows organizations to become efficient and keep up with the competition.

What is streaming analytics?

Streaming analytics refers to collecting and processing real-time data streams from various applications. Conventionally, data is processed in batches on an hourly, daily, or even a weekly basis. This batch-processing methodology introduces significant delays in analytical processing and causes businesses to miss out on time-sensitive opportunities. With real-time analytics, data is processed just as it is produced. It requires a continuous data stream that undergoes various transformations and is forwarded to relevant endpoints, all in a matter of seconds.

Stream analytics process messages as they are produced to present a real-time image of the system attributes and performance. This can help businesses solve problems without delay, help business leaders make quick decisions, and improve system quality.

However, real-time data processing does pose some challenges. Stream analytics requires a powerful, always-up system optimized for handling data streams. Due to this, most real-time applications work with small packets of data (usually in kilobytes) for easy transmission and processing. Some common applications of data streaming analytics, including

  • Real-time system monitoring

  • Sensor data monitoring for IoT devices

  • Machine learning processing

We will be discussing all these factors in more detail in subsequent sections. For now, let’s discuss the importance of streaming analytics

Importance of streaming analytics in modern data

Data has significantly changed over the past several years. In the last decade, the count of internet users has risen significantly. This means we have exponential growth of eCommerce customers, blog readers, video viewers etc. Due to this unprecedented growth, modern data is generated every second in large quantities and various formats. Traditional data analytics and real-time processing methodologies fail to keep up with this volume and velocity.

Organizations turn to streaming analytics to explore their data’s full potential. The ability to analyze data on the fly brings several advantages to businesses and their revenue like

Efficient system monitoring: Organizations can view and monitor key performance indicators (KPIs) in real-time. They are notified of any unusual behaviour without delay and can investigate and apply fixes immediately.

Analysis with updated information: As data is processed in a continuous flow, every analytics and dashboard represents fresh information. This ensures that every business decision is made with up-to-date information, covering all present realities.

Market competitive edge: Real-time processing allows for immediate system improvements and decision-making. These quick actions allow organizations to improve business value and stay ahead of the competition.

Increased profits: Making early decisions means businesses are first to introduce experience-improving features. This allows them to expand their user base and hence profits. It also allows business leaders to identify abnormalities and improve them before any losses are incurred.

Overall, processing event streams has become vital for modern organizations. Not only does it improve internal procedures, but it also helps with revenue growth.

Comparison with traditional analytics

Traditionally, data analytics relies on batch processing using the ETL/ELT processing methodologies. Traditional data analytics tools extract data from various sources over time and stored it in its raw form. This raw data is periodically (daily, weekly, monthly, etc.) transformed and then sent to end users like BI developers, Machine Learning engineers etc. While batch analysis has been a popular method for many years, it has several downsides compared to stream analytics.

Stream analytics

Traditional batch analytics

Near real-time processing

Periodic processing

Data is analyzed just as it is produced

Used more for historical data analysis

Operate in a low-latency environment

High-latency during processing

Require a high bandwidth network and a capable system for real-time transmission and processing

Network bandwidth and system power are less of a concern compared to streaming data

Ideal for time-critical scenarios like machine learning processing and sensor data from IoT devices for real-time outputs

Ideal for trend and comparative analysis of data between different periods of time

Stream analytics eliminates any delays and latency caused by traditional analytics techniques. It can be configured to analyze critical trends in real-time, such as server health status, and automatically detect unusual behaviour and generate alerts. Stream analytics also has business advantages due to its instantaneous behaviour. It allows businesses to make critical decisions early on and improves system efficiency and productivity. This, in turn, has a direct impact on profitability and revenue growth.

How does streaming analytics work?

A stream analytics architecture comprises data producers, stream processors and end-user applications. In simple terms, producers are responsible for the event or data generation. This data is transmitted to the stream processing engine, which undergoes several transformations, including cleaning, joining and engineering. This processed data is then transmitted to a relevant end-user application such as a Machine Learning application, analytics dashboard etc.

Overall the streaming data architecture works on a publish-subscribe model. Under this model, multiple streams from various sources are connected to the stream processing engine. The stream processing engine contains multiple message queues, each corresponding to event streams. The end-user application subscribes to its relevant message queue and directly connects the event source and the output use case. This way, data is generated, processed, and utilized in a near real-time environment.

A few examples of common stream processing technologies are Apache Kafka, Azure stream analytics, and Google Cloud Dataflow. These tools integrate your existing data flows and form a streaming data pipeline.

What is the purpose of streaming analytics?

Streaming analytics allows organizations to derive actionable insights from real-time data streams. The purpose of data streaming is to remove any bottlenecks between data acquisition and report generation. This zero-delay system gives organizations ample time to fix problems before they cause any loss or make swift important decisions. Overall, streaming analytics improves process efficiency and helps tech leaders understand their business.

Streaming analytics use cases

Some common use cases of streaming analytics include:

  • Real-time Patient Monitoring in Healthcare: Patient vitals are tracked as a continuous data stream. These are analyzed for anomalies and generate alerts when any are detected.

  • Fraud detection in financial transactions: Machine Learning algorithms analyze financial transactions in real-time. The algorithm predicts whether the transaction is possibly fraudulent or not. The transaction can be immediately blocked before any losses occur.

  • IoT sensor data analysis in agriculture: Agricultural sensors measure factors like temperature, humidity, soil constituents, etc. This data is streamed to a processing engine that takes action upon analysis, e.g., if the temperature is too high, it may increase the watering frequency.

  • Industrial process tracking: Processes like manufacturing and assembly are tracked in industries using machine sensors and data. Real-time analysis of these metrics allows managers to identify bottlenecks and improve process efficiency. Moreover, they also improve quality control as manufacturing abnormalities are instantly identified and corrected.
    Stream analytics improves process efficiency within organizations and allows businesses to make quick, informed decisions.

TripleTen replaces Metabase & builds powerful BI with DoubleCloud

How Doublecloud can help you with streaming analytics?

Doublecloud platform empowers organizations with real-time analytics solutions. It integrates with open-source technologies like Apache Kafka and Clickhouse and captures incoming data streams from various sources using the DoubleCloud Transfer.

‘Transfer’ is an extract and load data service that connects to popular data sources like MySQL, Google BigQuery, Snowflake, and AWS S3. It builds a seamless streaming pipeline between the source and the target, which allows for real-time processing and analytics. Moreover, Datacloud also offers a built-in visualization tool for easy data analysis, and it integrates with various data connectors, including ClickHouse.

Benefits of real-time streaming analytics

Let’s discuss some ways in which streaming analytics benefits organizations.

Self-service analytics

With self-service analytics, business users utilize the organization’s easy-to-use, existing BI tools to perform relevant analysis. These BI tools have a user-friendly interface allowing users to form complex visualizations easily. Moreover, a real-time data pipeline means users are not dependent on data experts to load and transform data.

Self-service analytics provides business leaders the freedom to build their required reports by removing technical dependencies. This improves business productivity and creates ease of decision-making.

Preset smart rules

Organizations can establish smart rules for incoming data to detect significant business events like anomalies. These rules can trigger corrective measures or other proceeding workflows, such as an ETL process. This brings automation to the data pipeline and improves all existing workflows' accuracy, speed, and efficiency.

Deploy machine learning models

Streaming analytics can benefit machine learning applications in several ways. An efficient machine learning model accepts a continuous data stream and provides continuous outputs. These outputs might be revenue projections or demand forecasts, and a streaming input allows the model to factor in up-to-date information like inflation or consumer price index.
Moreover, machine learning models also need to be constantly monitored for performance. Streaming analytics allows data scientists to analyze the results in real-time and apply corrective measures instantly.

Available at the edge and/or in the cloud

Depending on the business use case and needs, a streaming analytics engine can be deployed on your local systems or in a cloud environment. Both these deployment methods have specific benefits.

A cloud setup enjoys scalability and flexibility to endure high data traffic. Several popular cloud providers like Google and Azure also offer serverless environments with pre-configured analytics to save users from the tedious process of setup and configurations. Finally, cloud services often have industry-standard security protocols, ensuring the data streams are secure from intrusions.

Edge deployment gives users more flexibility for customizing the deployment. Since the entire setup is done from scratch, developers can employ business-specific configurations for improved efficiency. Furthermore, a locally deployed engine reduces network latency issues and allows offline operations.

Coding for advanced use cases

The requirements for data analysis often go beyond the capabilities of pre-defined functions and modules. In this case, custom coding helps cover advanced use cases and achieve specific goals. The custom code performs advanced operations like table joins, data aggregation, and feature engineering. Moreover, custom coding adds flexibility and customization to streaming analytics, providing niche and complex insights.

Limitations of streaming analytics

While streaming analytics enjoy several benefits, it also brings a lot of challenges and limitations.

Time-sensitive applications

Many streaming analytics applications are sensitive to the latency of data transmission and analysis. A few seconds' delays can be critical in scenarios like healthcare and industrial work. It can lead to loss of quality, productivity, and health and safety risks.

A streaming data environment needs a reliable network connection and always-up processing hardware. In practical applications, these requirements are challenging to achieve and often limit the system’s usability.

Error streaming

No matter how carefully the pipeline is planned, some form of logical error creeps into the data transformation flow. This is less trouble with traditional analytical methods since the erroneous data sits for some time before it is used in applications. Data engineers can run multiple tests to verify the data quality and integrity.

However, things are different with streaming analytics. If the pipeline contains errors and adequate quality tests are not applied, the corrupt data enters end-user applications. The errors are only detected after they have caused trouble for the customer, causing dissatisfaction.

Irregular data volume

Many streaming analytics applications deal with thousands of events per second. These products gather information from various data sources, such as social media feeds or customer data from e-commerce platforms.

However, the data volume remains inconsistent, and the application can experience sudden spikes. Streaming platforms must be scalable to deal with such surges and ensure unhindered operations.

DoubleCloud Transfer

A cloud agnostic service for aggregating, collecting, and migrating data from various sources.

Data visualization and real-time actions

A real-time dashboard provides users an updated outlook on the organization’s performance and growth. Data visualization increases operational awareness and provides deep insights regarding customers and business operations. Furthermore, a streaming dashboard allows businesses to trigger real-time actions. These actions respond to the insights generated by the dashboard and are targeted toward improving operations and/or customer experience.

The DouleCloud data visualization platform is a smart platform that integrates with your streaming pipeline. It supports multiple database connectors, including MySQL and MS SQL, and offers a fast and simple UI to accelerate business and market intelligence.

What is the difference between real-time and streaming analytics?

While often interchangeable, ‘real-time’ and ‘streaming’ have subtle distinctions. A real-time system is strictly tied to its response latency, i.e., it is supposed to respond within a certain time frame, or the system is not considered real-time.

A real-time system has two categories:

  • Soft Real-Time: The system can have delayed responses up to a few minutes or hours.

  • Hard Real-Time: Response latency is usually in milliseconds to seconds.

Streaming analytics, on the other hand, are more focused on processing a continuous stream of data. The stream may or may not have a defined start or end, but the data flow remains constant.

The current state of streaming analytics

In today’s fast-paced environment, streaming analytics has entered several industries. Most large organizations and SMEs are rushing to build a data streaming platform to understand their operations and customers and remain ahead of the competition. Moreover, the gaming industry and the metaverse rely on the real-time transfer and processing of information and events. The recent boom in these industries has further opened opportunities for streaming analytics.

Furthermore, the advancements in networking, such as the introduction of 5G, have made streaming analytics accessible to a wider audience.

Final words

Streaming analytics has become an integral part of most modern enterprises. It helps organizations generate data-related insights, improve operational efficiency, and generate alerts and automated actions with pre-defined rules in a real-time environment.

Stream data processing requires a streaming platform, such as Apache Kafka. These platforms ingest data in real time and build an end-to-end pipeline to collect information from sources, process it and deliver it to target applications. Some common and useful streaming analytics applications include patient health monitoring using medical sensors, cheater detection in multiplayer video games, monitoring industrial processes, and financial data analysis.

Despite its usefulness, setting up a streaming data pipeline can be challenging, for starters, organizations need experts familiar with the technology. Subsequently, once the stream is set up, data security and regulation become a bigger issue. However, every new initiative is met with challenges hence it is important to focus on the bigger picture and the benefits it brings in the longer run.

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