Real time data streaming: The Ultimate Guide

Luke Smith
Enterprise Solutions Architect
March 31, 2023
Matt Tanner
Developer Relations Lead
March 31, 2023
Matt Tanner
Developer Relations Lead
March 31, 2023
Get a migration case study in your inbox
Join our newsletter

Having data is only part of the process for any organization. Just like anything else in business, being able to convert something into actual insights or actions is where the true benefits lie. The usefulness of data on its own can be limited and even useless. if the data is not timely or up-to-date, then certain operations and use cases for the data are not possible. Data production continues to multiply at a staggering rate and because of this, it becomes imperative that companies use the right tools and technologies to take full advantage of the benefits within the data. Part of this push to use the correct technologies includes the need to shift from batch processing to data streams. Using data streams allows organizations to power real-time insights instead of waiting for batch-processed data to give insights in minutes, hours, days, or even weeks. 

Table of Contents

For companies that implement real-time data streaming, the slow processing of data in batches can be eliminated. Data streaming technology, unlike legacy batch approaches to moving data, allows the continuous flow of processed data in real time as soon as it is generated. Even if your organization is not using data streaming yet, chances are a company or service that you use every day has made data streaming an integral part of their service, likely improving the experience with the product. Its influence can be felt across every industry today. Far from niche, streaming systems offer solutions ranging from real-time fraud detection, social media feeds, stock trading signals, multiplayer gaming, GPS tracking used in ride-share apps, Netflix recommendations, e-commerce websites, and many others. 

In this article, we will cover the fundamentals of all you need to know about real-time data streaming. We will also dive into what it means for data to be real-time, highlighting the importance and benefits of real-time data streaming to both enterprises and individuals. First, let’s explore what data streaming is in a bit more detail.

What is Data Streaming?

Although you may have heard the term “data streaming” trickled into conversation over the last few years, the concept can sometimes be a bit fuzzy. In the simplest terms, data streaming refers to the continuous flow of data in real time from a source to a destination. This type of data processing allows organizations to process and analyze data as it's generated, rather than collecting it in batches or after the fact. The actual streaming data can come from a variety of sources, including social media feeds, sensors, log files, and other real-time sources. In the modern technological environment, the number of sources of this data is almost infinite.

Over the years, data streaming has evolved significantly due to the increase in data volume, variety, and velocity. In the past, data was typically collected and processed in batches. With legacy approaches, data was gathered over some time, then sent to a destination where it was finally analyzed. This meant that insights were often delayed and the applicability of those insights could only be applied based on historical trends, not ones currently happening in a system. However, with the advent of real-time data streaming, businesses can now analyze data as it's generated, allowing for faster decision-making and improved insights.

One of the most common use cases for data streaming is in financial services and securities trading. In this context, real-time data is essential for making split-second trading decisions based on historical and current market trends. Even a delay of a few seconds could lead to massive losses or missed opportunities for big gains. In addition, streaming data is used in industries such as healthcare, retail, and logistics to monitor patient health, track inventory, and optimize supply chain operations.

A more recent example of data streaming is its use with Internet of Things (IoT) devices. IoT devices generate a vast amount of data in real time, which can be analyzed to identify patterns and trends. Identifying these trends as they are happening enables businesses to use IoT devices to optimize their operations and improve customer experience.

The advent of modern data streaming methods has revolutionized the way businesses process and analyze data. By enabling real-time analysis of large data volumes, businesses can make faster, data-driven decisions and gain valuable insights that were previously impossible to obtain with the legacy, batch-based approach of data movement.

What is Real-Time Stream Processing Technology?

When it comes to data streaming, the underlying technologies are a mix of real-time stream processing tools, techniques, and frameworks. These components are used to process and analyze data as it's generated in real time from various sources. It enables organizations to capture, analyze, and respond to data as soon as it's generated, providing faster and more accurate insights. In a real-life implementation, a real-time stream processing technology stack typically involves the use of complex event processing (CEP) systems, data streaming platforms, and other advanced analytics tools that are purpose-built. To understand how each of these components fits into the stack, let’s take a look at the CEP systems and data streaming platform components in more detail.

CEP systems are designed to process high-speed data streams and identify patterns, trends, and anomalies in real time. They work by processing and correlating data from multiple sources simultaneously and applying predefined rules and models to detect relevant events. CEP systems are commonly used in industries such as finance, telecommunications, and manufacturing to monitor and control processes in real time. 

Data streaming platforms are another critical component of a real-time stream processing technology stack. They provide a scalable and fault-tolerant infrastructure for processing and analyzing large volumes of data streams in real time. Data streaming platforms typically include features such as data ingestion, processing, and storage, as well as tools for data visualization and real-time analytics. We will review some of these platforms later in this article so you can see some of the available solutions.

Real-time stream processing technology has a wide range of use cases across various industries, including fraud detection, network monitoring, predictive maintenance, and real-time marketing. Including real-time stream processing technologies in your data stack is an essential tool for modern businesses looking to gain a competitive advantage by processing and analyzing data as it's generated. By enabling real-time insights and actions, businesses can improve operational efficiency, enhance customer experience, and drive innovation.

Real-Time Data vs. Streaming Data

One of the biggest confusions when being introduced to streaming data is how to relate it to the concept of real-time data. Real-time data and streaming data are related concepts, but there is some minute, yet key differences between them. Generally speaking, the terms are used interchangeably within conversations about data. However, below I will explain the subtle differences between the two.

Real-time data refers to data that is generated, processed, and analyzed immediately, as soon as it's available. Real-time data can come from a variety of sources, including sensors, IoT devices, social media, and more. The key characteristic of real-time data is that it is analyzed and acted upon instantly, as soon as it's generated. An example of real-time data is stock market data. Stock prices change rapidly throughout the day, and traders need to be able to access and act on this information in real-time to make informed investment decisions.

Streaming data, on the other hand, refers to a continuous flow of data that is processed and analyzed in real time. Streaming data is typically generated from a variety of sources, and it's processed and analyzed as it's generated. The key characteristic of streaming data is that it's processed and analyzed continuously, rather than being collected in batches. A common example of streaming data is website visitor logs. Websites generate a continuous flow of data, including visitor information, clickstream data, and more. This data is typically processed and analyzed in real-time to provide insights into visitor behavior, optimize user experience, and improve website performance.

While real-time data and streaming data share some similarities, the main difference between them is that real-time data is processed and acted upon immediately, whereas streaming data is processed and analyzed continuously. Even though both types of data are critical for modern businesses, the terminology and differentiation are usually lost in translation. Potentially this brings up the question of the usefulness of even differentiating between the two concepts when talking about them outside of core data science professionals. Regardless, both types of data enable organizations to make informed decisions and gain insights that legacy approaches were unable to deliver.

What is Real-Time Streaming ETL?

The next concept to cover on our journey of understanding real-time data streaming is real-time streaming ETL (Extract, Transform, Load). Real-time streaming ETL is a data integration process that involves extracting data from real-time data streams, transforming the data into the desired format, and loading it into a destination system. This process is typically automated and allows organizations to process and analyze real-time data streams quickly and efficiently. Real-time streaming ETL is typically used in conjunction with the data streaming platforms and complex event processing (CEP) systems we spoke about earlier. Combing the technologies together enables an organization to provide real-time analytics and insights from their data.  

Sometimes, the best way to understand a concept is to look at it in the context of examples. With real-time streaming ETL, there are a lot of ways it can be applied and used. Below are a few brief examples of use cases for real-time streaming ETL that may be familiar:

Fraud Detection

Financial institutions use real-time streaming ETL to identify fraudulent transactions as they occur. The process involves extracting transaction data from real-time streams, transforming the data into the necessary format, and loading the results into a fraud detection system. This allows financial institutions to detect and prevent fraudulent transactions in real time, minimizing financial losses. 

Predictive Maintenance

Manufacturing companies use real-time streaming ETL to predict and prevent equipment failures before they occur. The process involves extracting data from sensors on the machines and other IoT devices, transforming the data to meet the needs of the destination, and loading the results into a predictive maintenance system. This allows manufacturing companies to detect and address potential equipment failures in real time, reducing downtime and maintenance costs.

Real-Time Marketing

E-commerce and retail companies use real-time streaming ETL to deliver personalized marketing campaigns to customers in real time. The process involves extracting customer data from real-time data streams, transforming the data to identify customer preferences and behaviors, and loading the results into a marketing automation system. This allows e-commerce and retail companies to deliver targeted marketing messages to customers based on their real-time behavior, increasing customer engagement and ideally, revenue.

Overall, real-time streaming ETL is a powerful tool for modern businesses looking to process and analyze real-time data streams quickly and efficiently. By automating the data integration process, organizations can gain valuable insights and make informed decisions in real time. Outside of the examples given above, almost all businesses could benefit from real-time streaming ETL whether it be to drive revenue, deliver better customer experience, or optimize operational efficiencies.

Data Streaming vs. ETL

Data streaming and ETL (Extract, Transform, Load) are two related but distinct concepts in the field of data integration and processing.

Data streaming involves processing and analyzing continuous and real-time data feeds as they occur. On the other hand, ETL is more focused on extracting data from disparate sources, transforming it into a desired format, and loading it into a destination system. When it comes to using cases, data streaming is often used for real-time analytics, monitoring, and alerting, whereas ETL is typically used for batch processing and data warehousing.

Diving into the differences in a bit more detail, here are some key differences between data streaming and ETL:

Data Sources

Data streaming typically deals with live data feeds that are generated from sources such as sensors, social media, or IoT devices. In contrast, ETL processes data from batch files or databases, which are typically static and updated at regular intervals.

Processing Speed

Data streaming processes data in real time, which requires high processing speeds and low latency. ETL generally processes data in batches or micro-batches, which allows for more time-consuming transformations and analyses. This processing could take seconds to hours, whereas data streaming processes data instantly so that potential actions can be taken when they matter most.

Use Cases

As mentioned in the earlier part of this section, data streaming is often used for real-time monitoring and analysis, as well as alerting. ETL is typically used for data warehousing, analytics, and reporting. 

To even further elaborate on the differences between the two, it helps to dig into some real-world use cases. Here are some examples of how data streaming and ETL are used in a familiar light:

Data Streaming

A great example of data streaming would be a weather monitoring system that collects live data from sensors to provide real-time weather updates to users. Another example is a social media monitoring system that collects live data from various social media platforms and analyzes it to track brand reputation or sentiment. In both of these cases, users get the most impact from the data if they can view or analyze it as it is happening. 

ETL

An example of a common ETL process is when a marketing team extracts customer data from multiple sources, such as a CRM system, web analytics platform, and social media profiles, to create a unified customer profile. Another example is an ETL process that aggregates data from multiple transactional databases into a data warehouse for business analytics and reporting. It’s likely still best that these processes happen in a time-bound fashion, but not having to-the-second or millisecond updates likely won’t have much impact on the outcome. 

In summary, while data streaming and ETL share some similarities, they have distinct differences in terms of data sources, processing speed, and use cases. Organizations can choose which approach to use depending on their specific needs and the nature of their data. In most cases, a company will likely use a mix of both technologies or a tool that supports both.

Why Do We Need Real-Time Streaming?

Before implementing a real-time streaming solution, being able to create a business case or answering “why is it important for our business” is likely of top importance. The actual need for real-time streaming versus some other solution can usually be easy to decide. A company may want to use real-time streaming as part of their data solution for several reasons, including:

Faster Decision-Making

For most companies, the fast a decision is made, the better the outcome. Real-time streaming allows companies to analyze data as it is generated, which can help them make quicker and more informed decisions. 

Improved Operational Efficiency

Counting on humans to notice operational inefficiencies or issues can be unpredictable and unreliable. By using real-time streaming, companies can identify and address operational issues in real time, which can improve overall efficiency.

Better Customer Experience

Real-time streaming can help companies personalize customer experiences by analyzing customer data in real time. This can help to drive opportunities to increase revenue and customer satisfaction. 

As with any technology, there will always be cases when it may not be suitable or may hinder progress. Below we will look at a few scenarios where real-time streaming may not be necessary or appropriate for a company. These include: 

Limited Data Volume

If a company generates low-volume data or data that is not time-sensitive, real-time streaming may not be necessary. In such cases, batch processing may be sufficient for data processing and analysis. 

Limited Resources

Real-time streaming requires significant processing power and resources, which can be expensive to implement and maintain. If a company does not have the necessary resources or budget, it may not be able to effectively use real-time streaming and another solution may be a better fit.

Security and Compliance

Real-time streaming may not be appropriate for sensitive data or data that is subject to strict regulatory requirements. With data moving so quickly, it may be tough to enforce compliance and data governance. In such cases, batch processing may be preferred since it is easier to ensure data security and compliance. 

Overall, companies may choose to use real-time streaming for faster decision-making, improved operational efficiency, and better customer experience. As mentioned, as exciting as implementing real-time data streaming may be, there may be situations where real-time streaming is not necessary or appropriate.

Benefits of Real-Time Streaming

Real-time streaming provides several benefits to businesses that are looking to improve their data processing and analysis capabilities. Below are some of the most common benefits of real-time streaming from a business perspective. As you’ll notice, a few of these have already been covered in the paragraphs above but we will elaborate further on them below.

Improved Operational Efficiency

Real-time streaming can help businesses identify and address operational issues in real time, which can improve overall efficiency. For example, businesses can use real-time streaming to monitor equipment performance and detect failures before they occur, reducing downtime and maintenance costs.

Better Customer Experience

Real-time streaming can help businesses personalize customer experiences by analyzing customer data in real time. For example, businesses can use real-time streaming to recommend products to customers based on their real-time browsing behavior, which can increase sales and customer satisfaction.

Cost Savings

Real-time streaming can help businesses identify cost-saving opportunities by analyzing data in real time. For example, businesses can use real-time streaming to identify and prevent fraud in real-time, which can save them money on chargebacks and lost revenue. 

Competitive Advantage

Real-time streaming can provide businesses with a competitive advantage by enabling them to respond quickly to market changes and customer needs. This can help businesses stay ahead of their competitors and maintain their market position.

Challenges and Limitations of Real-Time Streaming 

Real-time data streaming provides several benefits, but it also has some challenges and limitations that businesses need to consider. In contrast with the benefits mentioned above, below are some of the biggest challenges and limitations of real-time data streaming and potential ways to limit or mitigate their impacts. 

Data Quality

Real-time data streaming requires accurate and high-quality data to be effective. If the data is incomplete or inaccurate, it can lead to erroneous results and unreliable analysis. To overcome this challenge, businesses need to implement data validation and cleansing processes to ensure the quality of data.

Network Latency

Real-time data streaming relies on fast and reliable network connections to transmit data in real time. However, network latency can cause delays and impact the speed of data processing. To overcome this challenge, businesses can consider implementing edge computing solutions that process data closer to the source, reducing network latency.

Data Security

Real-time data streaming can expose businesses to security risks, as the data is transmitted and processed in real time. To mitigate this challenge, businesses need to implement robust security measures such as encryption, authentication, and access controls. 

Scalability

Real-time data streaming requires significant processing power and resources, which can be challenging for businesses to scale as their data volumes grow. To overcome this challenge, businesses can consider implementing cloud-based solutions that provide scalability and flexibility by default. 

Data Privacy and Compliance

Real-time data streaming can expose businesses to legal and regulatory risks if the data being streamed is subject to privacy and compliance requirements. To mitigate this challenge, businesses need to ensure that their real-time streaming processes comply with applicable laws and regulations, likely by choosing a tool or platform that can assist.

Best Tools for Real-Time Data Streaming 

After reviewing what real-time data streaming is and exploring its benefits and drawbacks, let’s look deeper into potential solutions. Various enterprise and open-source tools available in the market today can be used to ingest data streams from real-time stream sources. Each has its advantages, disadvantages, and use cases. Below are a few tools that can be used to set up real-time data streaming.

Arcion

Arcion, a real-time data integration platform, has a wide range of data management capabilities and can be used as a solution for streaming data. Arcion is a go-to solution for many enterprises who are looking to select a data real-time data streaming tool that is scalable, reliable, and extremely easy to configure and use. It provides robust data pipelines that offer high availability and leverage log-based CDC. 

A major benefit to using Arcion is that it can be implemented with multiple deployment options. Arcion can migrate data to and from on-prem data sources, cloud-based data sources, or a mix of both (see supported connectors).

Data can be moved from one source to multiple targets or multiple sources to a single target depending on the use case. All of this is easily configured through Arcion’s no-code platform which allows users to build their data pipelines for real-time data streaming without having to write any code. Arcion can be set up and configured strictly through configuration files or by using Arcion’s intuitive and easy-to-use UI. Both approaches allow users to set up pipelines in a matter of minutes.

Apache Kafka

Another popular real-time data streaming tool is Apache Kafka. Kafka, one of the most well-known and widely used platforms for streaming, is a distributed streaming platform that can handle high volumes of data in real time. It provides a reliable and scalable platform for streaming data across different systems and applications. Some of its key benefits include high-throughput, low-latency messaging, fault-tolerant data replication, and vast support for multiple data sources and sinks.

Confluent

Confluent is a data streaming platform built on Apache Kafka, providing a complete set of tools and services to build, deploy and operate real-time data pipelines at scale. Although it is built on Apache Kafka, Confluent is a more refined tool with noticeable performance gains. Confluent is available on-premise and also through Confluent Cloud, where users can rely on a managed instance of Confluent. 

The platform includes enterprise-grade features for data replication, fault tolerance, and disaster recovery, all of which help to ensure data integrity and availability. Confluent also supports a wide range of data sources and sinks, making it easy to integrate with existing data infrastructure and tools. An added benefit is that Confluent provides a streamlined developer experience with a range of tools and services to simplify building, deploying, and operating data pipelines.

Amazon Kinesis

With multiple products under its banner, Amazon Kinesis is a cloud-based streaming platform available through AWS that can ingest and process large amounts of data in real time. It provides a scalable and cost-effective solution for real-time data processing and can integrate with other AWS services like Lambda, S3, and DynamoDB. Some of its key features include support for multiple data sources and destinations, real-time data analytics, and flexible scaling options. One of the biggest benefits is the ease with which it integrates with other products hosted on AWS. 

Apache Flink

Another product under the Apache name, Apache Flink is a distributed stream processing engine that can process high volumes of data in real time. It provides a powerful platform for real-time data analytics and machine learning and can integrate with other big data technologies like Hadoop and Spark. Some of its key features include support for stream and batch processing, low-latency processing, and support for complex event processing. Overall, Flink is very flexible and can accommodate a wide variety of use cases compared to tools that cover a narrower scope.

Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed streaming data processing service that can ingest and process large amounts of data in real time. Available on GCP, Google Cloud Platform, It provides scalable and flexible real-time data processing and can integrate with other Google Cloud services like BigQuery and Cloud Storage. Some of its key features include support for stream and batch processing, flexible data pipelines, and real-time data analysis. Much like Amazon Kinesis within AWS, Dataflow has the benefit of easily integrating with multiple products within the GCP ecosystem.

Microsoft Azure Stream Analytics

Microsoft Azure Stream Analytics is a fully-managed streaming data processing service that can ingest and analyze large amounts of data in real time. It provides a scalable and easy-to-use platform for real-time data processing and can integrate with other Azure services like Cosmos DB and Event Hubs. Some of its key features include support for real-time data analysis, machine learning, and flexible data processing. Bundled with Microsoft’s recent investment into OpenAI and expanding those functionalities into the Microsoft ecosystem, Azure Stream Analytics could get some very cool functionality in the coming months and years.

Although not an exhaustive list, the tools covered above should give a great starting point for those looking to implement real–time data streaming. Each comes with its own set of strengths and weaknesses. The first step to choosing the right tool is to figure out your needs and the next step is to figure out which tools best fit those needs.

Conclusion

This article has covered a lot of ground concerning real-time data streaming and hopefully has given you a better understanding of the technology. As part of our review, we explored multiple facets of what data streaming is about and compared it to real-time data and streaming ETL. We also covered why it is important and its benefits, and the challenges and limitations of real-time data streaming. Lastly, we looked at some of the most popular tools available to give you a head start on your implementation. 

As mentioned in our summary of available tools, Arcion is a data integration platform with a wide range of data management capabilities that can be used as a solution for your data streaming needs. Available on the cloud or on-premise, getting started starts with a quick chat with our database and real-time streaming experts. To chat with us today, book a slot in our calendar and implement real-time data streaming with Arcion.

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.
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.
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.
Join our newsletter

Take Arcion for a Spin

Deploy the only cloud-native data replication platform you’ll ever need. Get real-time, high-performance data pipelines today.

Free download

8 sources & 6 targets

Pre-configured enterprise instance

Available in four US AWS regions

Contact us

20+ enterprise source and target connectors

Deploy on-prem or VPC

Satisfy security requirements