Real-Time Streaming Data: A Complete Guide

Luke Smith
Enterprise Solutions Architect
June 13, 2023
Matt Tanner
Developer Relations Lead
June 13, 2023
Matt Tanner
Matt Tanner
Developer Relations Lead
Developer Relations Lead
June 14, 2023
18
 min read
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In today's data-driven world, businesses are constantly seeking ways to gain a competitive edge and make informed decisions in real-time. The advent of real-time streaming data has revolutionized the way organizations collect, process, and analyze data. By capturing and analyzing data as it is generated, businesses can gain the instantaneous insights they require. The benefits of this include enabling them to respond swiftly to changing market conditions, customer preferences, and operational challenges.

Real-time streaming data refers to a continuous flow of data that is processed and analyzed in near real-time, as it is generated. Unlike traditional batch processing, where data is collected and processed in large chunks in scheduled intervals, real-time streaming data enables organizations to utilize data as soon as it is created or updated. By having data available immediately, organizations are provided with immediate value and actionable insights.

Real-time streaming data has become a critical enabler for organizations across many different industries and verticals. It brings a massive amount of enablement to a wide variety of use cases such as monitoring and analyzing key performance indicators (KPIs) in real time, identifying anomalies and trends, and detecting fraud and security breaches. Real-time streaming also gives organizations the ability to personalize customer experiences, optimize operations, and make data-driven decisions with agility and precision that were not truly possible before this technology existed.

The shift towards leveraging the capabilities within real-time streaming data has been driven by advancements in technology. To truly be able to create a real-time environment, technologies such as high-speed networks, scalable cloud infrastructure, and sophisticated data stream processing frameworks needed to be created and somewhat perfected. Technologies such as Apache Kafka, Amazon Kinesis, and other real-time data streaming platforms have helped lead the way in these efforts. These technological advancements have made it possible to capture, process, and analyze vast amounts of data in real-time. With the technology in place and widely available, real-time decision-making and actionable insights that are the result of real-time streaming data are possible.

In this comprehensive guide, we will dive into the world of real-time streaming data, exploring its definition, applications, advantages, and use cases across various industries. We will also introduce Arcion, a powerful platform designed to assist users and their enterprises in harnessing the power of real-time streaming data. With Arcion, organizations can unlock the full potential of real-time data streaming, enabling them to stay ahead in today's fast-paced and data-driven business landscape. With the agenda set, let’s explore the exciting realm of real-time streaming data and its transformative impact on how we can extract value from data.

Table of Contents

What is Real-time Streaming Data?

Our first stop is to dig a little bit further into the definition of real-time streaming data. In the simplest definition, real-time streaming data is the continuous flow of data that is generated, processed, and analyzed in near real time. By having a continuous flow of data as it is generated, organizations gain the ability to garner immediate insights and take instant actions based on the latest data being produced. To understand the concept better, let's break it down further.

When we talk about data within an organization, we often encounter two processing approaches: batch processing and real-time streaming data processing. When data is moved from one platform to another with batch processing, data is collected over a period of time and processed in large chunks or batches at predefined intervals. This approach works well for analyzing historical data, generating reports, or performing offline batch data processing tasks. Especially within legacy systems that didn’t support real-time data streaming tools, batch processing was the only feasible option. However, in today's fast-paced and data-intensive world, organizations require immediate access to insights and the ability to respond quickly to dynamic situations. This is where real-time streaming data comes into play and creates the ability to fulfill this need.

Real-time streaming data, on the other hand, allows data to be processed and analyzed as it is generated. This capability enables organizations to react in real-time to events, trends, and anomalies that are detected within the data. Instead of waiting for data to accumulate and processing it in batches, streaming data provides a continuous and uninterrupted flow of information. This constant stream of data enables organizations to make faster decisions, respond promptly to changing conditions, and detect critical events or patterns as they happen. With the legacy approach of batch processing, this just wasn’t possible and if it did come anywhere close to real-time capabilities, the load and cost on the infrastructure providing the functionalities was extremely high.

Real-time streaming data can originate from a wide range of sources. Not just simply for events occurring within a data store, an example of a source could be a sensor embedded in a device or equipment that can generate real-time data on temperature, pressure, location, and other variables. Another example would be social media platforms where vast amounts of streaming data are captured in the form of tweets, posts, and interactions. Other examples include clickstream data from website visits, online transactions, financial transactions, stock market feeds, and currency exchange rates, all of which are examples of streaming data. Additionally, the Internet of Things (IoT) ecosystem is a significant contributor to real-time streaming data, with various interconnected devices continuously generating data.

The beauty of real-time streaming data lies in its immediacy and the ability to process and analyze data on the fly. It allows organizations to gain insights and make informed decisions based on the most up-to-date information available. This is especially valuable in scenarios where time-sensitive actions are required, such as fraud detection, real-time monitoring of critical systems, personalized marketing, and predictive stream analytics. Generally, having data immediately available will never be a detriment to the operations dependent on that data. On the other hand, only having data available in batches can leave a lot of opportunities on the table.

By leveraging real-time streaming data, organizations can unlock new opportunities for optimization, automation, and innovation. It enables them to react swiftly to emerging trends, proactively address issues, deliver personalized experiences to customers, and gain a competitive edge in today's dynamic business landscape. The advent of advanced technologies and scalable infrastructure has made it possible to harness the power of real-time streaming data at scale. These technologies will continue to evolve and improve as the amounts of data produced and required exponentially increase well into the future.

What is Streaming Data Used For?

As we mentioned in the previous section, streaming data has emerged as a powerful tool with diverse applications across industries and domains. It enables organizations to unlock valuable insights and drive real-time actions that are only possible when there is a continuous flow of data. To elaborate on what we’ve already covered, let's explore some common use cases where streaming data is used in further detail.

Monitoring and Alerting

Streaming data allows for real-time monitoring of critical systems, ensuring their optimal performance and security. For example, organizations can monitor server performance metrics, network infrastructure data, and security events in real-time. By analyzing streaming data, they can identify anomalies, detect breaches, and trigger immediate alerts. The overall result of these capabilities is that organizations can proactively address issues, minimize downtime, and ensure the reliability and availability of their systems.

Fraud Detection

Streaming data plays a vital role in combating fraud for financial institutions. By continuously analyzing transaction data, patterns, and user behaviors in real-time, organizations can detect anomalies and identify potential fraudulent activities. Real-time fraud detection allows financial institutions to immediately flag and prevent fraudulent transactions. This helps to mitigate financial losses and safeguards the integrity of financial systems and the transactions that run within them. 

Want to learn more? Read our blog on “Leveraging Change Data Capture for Fraud Detection”.

Predictive Analytics

Streaming data is a crucial component of predictive real-time analytics, enabling organizations to make data-driven predictions and take proactive actions. By analyzing real-time data, businesses can identify emerging trends, anticipate customer needs, and optimize operations. For example, by analyzing streaming data from customer interactions, website clicks, and purchase behavior, businesses can identify patterns that help them tailor the customer's experience, improve customer satisfaction, and stay ahead of the competition.

Personalized Marketing

With the wealth of streaming data available, marketers need to be able to deliver personalized content and offers to customers in real-time. By analyzing streaming data on customer behaviors, preferences, and interactions, businesses can create tailored marketing campaigns and experiences. For instance, real-time analysis of customer browsing behavior can enable businesses to deliver relevant product recommendations and targeted promotions. The result of such efforts tends to be higher conversion rates, increased customer engagement, and improved brand loyalty.

These are just a few examples of how streaming data can be used to drive valuable insights and actions in real-time. Beyond what we looked at here, the applications of streaming data span various industries, including finance, e-commerce, healthcare, logistics, and more. As organizations continue to harness the power of streaming data, they can unlock new opportunities, optimize operations, and gain a competitive advantage in today's data-driven landscape.

Difference between Streaming Data and Real-time Streaming Data

Although streaming data and real-time streaming data share similarities, they have distinct differences in terms of processing speed and the immediacy of insights. Let's explore these differences further to gain a better understanding.

Streaming data refers to the continuous flow of data from various sources. It encompasses data that is generated, collected, and transmitted in an ongoing manner. This data can be processed and analyzed in different ways, depending on the requirements of the application or system. Streaming data can be processed in real-time, near real-time, or even via batch processing. The processing once again depends on the specific use case where the data will be leveraged. For instance, a system might process streaming data in near real-time, with a slight delay in stream processing to accommodate any buffering or data aggregation requirements.

On the other hand, real-time streaming data emphasizes the immediate processing and analysis of data as it is generated. Because the collection, processing, and analysis always happen instantaneously, organizations can gain immediate insights and make instant decisions. Real-time streaming data processing involves capturing data as it is produced and performing continuous analysis on the fly, without any significant delay. This approach allows for timely responses and actions based on the most up-to-date information available.

To understand the difference more concretely, let's consider an example. Imagine a retail company that wants to monitor sales data in its physical stores. The company collects data from point-of-sale systems, such as transaction amounts, product SKUs, and timestamps from when a checkout occurred. If the company processes this data in real-time streaming mode, it would analyze each sales transaction as it occurs, enabling immediate insights. For instance, the company could identify sales trends, detect out-of-stock situations, or trigger alerts for suspicious transactions in real-time.

On the other hand, if the same company decides to process the data in a stream but not in real-time, it might accumulate the sales transactions for a specific period, such as every hour, and then process and analyze them as a batch. This approach, although still considered streaming, would introduce a delay in insights and actions. The company would have to wait for the designated period to pass, in this case, an hour, before gaining insights from the accumulated data. This approach could still be valuable for certain use cases that don't require immediate actions, such as daily sales reports or inventory forecasting. In use cases such as tracking stock-level or suspicious activity, this approach is less advantageous.

Looking at this comparison, while both streaming data and real-time streaming data involve the continuous flow of data, the distinction lies in the processing speed and time frame of insights. Streaming data can be processed in various ways, including real-time, near real-time, or batch processing, depending on the specific requirements. Real-time streaming data, however, emphasizes the immediate processing and analysis of data as it is generated. Within these two options, real-time streaming data is the only way to enable organizations to respond in real-time and gain instantaneous insights for time-sensitive decision-making.

When trying to figure out which approach best fits into your data strategy, organizations should consider their specific use cases and requirements when choosing between these two approaches. Real-time streaming data processing is ideal when immediate insights and instant actions are necessary, such as in fraud detection, real-time monitoring, or personalized recommendations. On the other hand, streaming data processing that is not done in real time can still provide valuable insights and actions, but with a slight delay.

Advantages of Real-time Streaming Data

Real-time streaming data has been a game-changer for modern businesses. It offers numerous advantages that drive competitive advantage and operational excellence. By harnessing the power of real-time insights and immediate actions, organizations can make faster decisions, enhance customer experiences, and optimize operations. Below, we will explore the key advantages of real-time streaming data and some real-world examples for more context.

Faster Decision-making

Real-time streaming data enables organizations to make timely decisions based on the most up-to-date information available. By eliminating data latency, businesses can respond almost immediately to changing conditions, allowing them to gain a competitive edge. For instance, in financial trading, real-time streaming data allows traders to make split-second decisions based on the latest market information. This ability ensures that traders can capitalize on the most favorable opportunities and maximize their returns.

Immediate Actionability 

With real-time streaming data, insights can be transformed into actions instantaneously. This empowers businesses to automate processes, trigger alerts, and intervene promptly in critical situations. In supply chain management, for example, real-time streaming data can help identify disruptions or delays. Once an issue is identified, employees or automated processes can trigger immediate actions to mitigate possible risks. This proactive approach that leverages the real-time streaming data helps to ensure a seamless supply chain operation and helps to minimize potential disruptions.

Enhanced Customer Experience 

Real-time streaming data allows businesses to deliver personalized and context-based experiences to their customers. By leveraging real-time insights, organizations can respond promptly to customer needs, resulting in higher satisfaction and loyalty. For instance, in the e-commerce industry, real-time streaming data powers recommendation engines that provide personalized product suggestions in real-time. This augments the customer shopping experience by offering tailored recommendations aligned with individual preferences and browsing behavior. The result for the business is higher conversion rates and increased consumer spending.

Operational Efficiency 

Real-time streaming data enables organizations to optimize operations and resource allocation in real-time. By continuously monitoring and analyzing data, businesses can identify bottlenecks, detect inefficiencies, and make data-driven improvements. In manufacturing, for example, real-time streaming data helps optimize production lines by identifying potential issues and suggesting corrective actions, such as a proactive repair to a system or machine, in real-time. This leads to increased productivity, reduced downtime, and improved overall efficiency.

Although not all-encompassing, these examples above highlight the significant advantages that real-time streaming data brings to businesses across various industries. By leveraging real-time insights and immediate actions, organizations can stay agile, respond effectively to changing market dynamics, and gain a competitive edge. Implementing a real-time streaming data strategy can unlock a massive amount of potential and use cases for any organization that needs real-time capabilities.

Real-time Streaming Data Use Cases

After looking at the advantages of real-time streaming data, let's go even more in-depth to explore some compelling use cases that demonstrate the power of real-time streaming data. Below are a few further examples of how real-time streaming data is being used in the real world around us.

Intelligent Transportation Systems

Real-time streaming data is crucial for managing traffic flow, optimizing routes, and predicting congestion. By analyzing data from various sources, such as GPS sensors, traffic cameras, and weather reports, transportation authorities can make informed decisions in real-time. These real-time insights allow for improved traffic management and reduced commute times. Real-time streaming data enables intelligent traffic management systems that can dynamically adjust traffic signals, reroute vehicles, and provide real-time traffic information to commuters. Without instantaneous data being available, this would not be as effective.

IoT-enabled Smart Homes

Real-time streaming data is at the core of smart home automation. By analyzing data from connected devices, such as thermostats, security systems, and energy meters, homeowners can monitor and control their homes remotely. Real-time insights enable benefits such as energy optimization, enhanced security, and personalized comfort based on previous patterns. For example, real-time streaming data can allow homeowners to adjust their home's temperature, turn on/off lights, and receive security alerts through a mobile app. With the capabilities of modern smartphones and cloud computing, all of this can be done irrespective of the homeowners' physical location.

E-commerce Recommendation Engines

Real-time streaming data is instrumental in powering recommendation engines for e-commerce platforms. By analyzing customer browsing behavior, purchase history, and real-time interactions, personalized product recommendations can be generated. As many of us have experienced, this helps e-commerce businesses to drive sales and enhance the user experience. Real-time streaming data helps e-commerce platforms deliver personalized recommendations that adapt to user preferences and behaviors in real-time.

Healthcare Monitoring

A driver in better health outcomes, real-time streaming data plays a vital role in healthcare monitoring systems. By continuously monitoring vital signs, patient data, and sensor readings, healthcare providers can detect anomalies, trigger alerts, and intervene promptly in critical situations. Especially with the introduction of AI and machine learning into many of these systems, even the smallest anomaly in the real-time streaming data can trigger an alert that may have been missed by a human. Real-time streaming data enables remote patient monitoring, early detection of health issues, and timely interventions, improving patient outcomes and reducing healthcare costs.

The examples showcased above illustrate the diverse and transformative applications of real-time streaming data in various industries. From intelligent transportation systems that optimize traffic flow to the healthcare sector, where real-time streaming data enables remote patient monitoring and timely interventions, real-time streaming data is having significant and positive impacts on our world. These use cases demonstrate the immense value that real-time streaming data brings to organizations by enabling faster and more informed decision-making and improving efficiency. Not a single aspect of modern life and business wouldn’t benefit from the advantages of real-time streaming data.

How Arcion Can Help You with Real-time Streaming Data

When it comes to implementing real-time streaming data capabilities, Change Data Capture (CDC) plays a crucial role. Arcion is a CDC platform that offers an easy way to build data pipelines that can capture and deliver real-time streaming data from a variety of sources to a variety of destinations, such as Apache Kafka.

By leveraging Arcion’s Change Data Capture technology, organizations can bridge the gap between traditional batch processing and real-time streaming data capabilities. CDC captures and delivers data changes in near real-time, reducing latency, enabling immediate data availability, and empowering organizations to make data-driven decisions. Below are some highlights of using Arcion’s Change Data Capture to implement real-time streaming data capabilities.

Sub-Second Latency 

Many other existing CDC solutions don’t scale for high-volume, high-velocity data, resulting in slow pipelines, and slow delivery to the target systems. Arcion is the only distributed, end-to-end multi-threaded CDC solution that auto-scales vertically & horizontally. Any process that runs on Source & Target is parallelized using patent-pending techniques to achieve maximum throughput. There isn’t a single step within the data pipeline that is single-threaded. It means Arcion users get ultra-low latency CDC replication and streaming capabilities that can always keep up with the forever-increasing data volume on source systems.

100% Agentless Change Data Capture

Arcion is the only CDC vendor in the market that offers 100% agentless CDC to all its supported enterprise connectors, including the most popular database and big data platforms. Arcion reads directly from the database transaction log, never reading from the database itself. Previously, data teams faced administrative nightmares and security risks associated with running agent-based software in production environments. You can now replicate data in real-time, at scale, with guaranteed delivery — but without the inherent performance issues or the security concerns.

Data Consistency Guaranteed

Arcion provides transactional integrity and data consistency through its CDC technology. To further this effort, Arcion also has built-in data validation support that works automatically and efficiently to ensure data integrity is always maintained. It offers a solution for both scalable data migration and replication while making sure that zero data loss has occurred.

Automatic Schema Conversion & Schema Evolution Support

Arcion handles schema changes out of the box requiring no user intervention. This helps mitigate data loss and eliminate downtime caused by pipeline-breaking schema changes by intercepting changes in the source database and propagating them while ensuring compatibility with the target's schema evolution. Other solutions would reload (re-do the snapshot) the data when there is a schema change in the source databases, which causes pipeline downtime and requires a lot of compute resources, which can get expensive!  

Conclusion

In this comprehensive guide, we have explored the world of real-time streaming data and its significance in today's data-driven landscape. We have learned that real-time streaming data refers to the continuous flow of data that is generated, processed, and analyzed in near real-time, enabling organizations to gain immediate insights and make instant decisions. By contrast, streaming data encompasses the continuous flow of data, which can be processed in various ways, including real-time, near real-time, or batch processing modes.

We have examined the advantages of real-time streaming data, including faster decision-making, immediate actionability, enhanced customer experiences, and operational efficiency. The ability to analyze data as it is generated empowers organizations to stay ahead of the competition, deliver personalized experiences, detect anomalies in real-time, and optimize operations for long-term success.

In conclusion, real-time streaming data has become an essential component of the modern data landscape. Its ability to provide immediate insights, drive real-time actions, and enable personalized experiences is reshaping businesses across industries. By embracing real-time streaming data and leveraging platforms like Arcion, organizations can seize new opportunities, optimize operations, and gain a competitive advantage in today's fast-paced and data-driven world.

To get started with Arcion, the real-time, in-memory Change Data Capture (CDC) solution, connect with our team today!

Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
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