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June 4, 2024
5
min read

6 Myths Preventing You from Embracing Real-Time Data

By
Eric Sammer
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Less than a decade ago, it was common to hear "We don’t do cloud. We don’t need it." Back then, on-premises infrastructure was the default. But as time marched on, the landscape shifted, and today cloud-hosted infrastructure is the backbone of nearly every major enterprise. The advantages of embracing the cloud—agility, scalability, cost savings—became too compelling to ignore. 

Now, we find ourselves at the precipice of a similar transformation with real-time data. Just as there were naysayers about the cloud, there are those who may proclaim, "We don’t do real-time. We don’t need it." But much like it did for the cloud, the tide is turning, and the benefits of real-time data are becoming increasingly apparent. However, despite numerous public examples across every major industry, several myths about real-time data processing remain. Let's debunk these misconceptions and explore why real-time data is not only achievable but essential for modern businesses.

Myth #1: Real-Time Data Is Just for Constantly Ticking Graphs

When people hear "real-time," they often envision graphs in a dashboard being updated once per second or typing indicators in a chat application. In actual practice, real-time data is more about ensuring data and analytics align with reality, delivering accurate and up-to-date analytics, training machine learning models, and providing data to customer-facing services.

Real-time data movement is fast becoming the default way data is captured, processed, and delivered, supporting both online and offline use cases. This approach allows organizations to power all applications and services with the same data, delivering intuitive customer experiences that meet their expectations and respond to the real world.

Myth #2: Real-Time Data Means Ripping Out Your Data Warehouse

Real-time data pipelines complement your existing infrastructure, enabling continuous processing between systems. This ensures that your analytics accurately reflect the present reality without requiring a fundamentally different skill set or data platform architecture. The value of data warehouses, analytical processing systems, and other repositories remains intact and is further enhanced by allowing a new class of analytics and workloads. You can choose to do as little or as much ingest-time processing of data as necessary for your use case, and can continue to do batch processing in these systems where that makes sense.

Myth #3: Real-Time Data Comes with Complex Reconciliation Processes

Real-time streaming systems come with well-defined data processing guarantees, such as exactly-once or at-least-once processing, minimizing or eliminating the need for cumbersome reconciliation processes. Each event is processed automatically and accurately, maintaining data integrity by design—even in the case of failures.

In contrast, batch processing environments often require reconciliation processes that “fix up” data to ensure quality and consistency. This reconciliation process involves multiple steps, including collecting, matching, comparing, and resolving discrepancies between datasets. While some discrepancies can be resolved automatically based on predefined rules, others may require manual intervention, especially in cases of complex errors that require human judgment.

Overall, modern real-time streaming systems streamline data processing by handling potential duplicates or failures automatically, while batch processing environments may require more extensive reconciliation efforts to maintain data integrity.

Myth #4: Real-Time Data Is Expensive and Complex

Contrary to popular belief, real-time data is no longer prohibitively expensive. Tools and services like Decodable have made real-time ETL and stream processing accessible and affordable for businesses of all sizes with features like object storage for state, native ARM hardware support, change data capture, efficient resource management, and tons of processing engine optimizations. That said, not all real-time data platform offerings provide the same degree of capabilities, features, and functionality. Download our new ebook, Decoding the Top 4 Real-Time Data Platforms Powered by Apache Flink, to understand the pros and cons of each.

Myth #5: Real-Time Data Is Only for Large Enterprises

Real-time data processing is not reserved exclusively for tech giants or the Fortune 500. Businesses of all sizes can gain a competitive edge and enhance their customer experiences, regardless of the scope of their needs, scale, or budget. These capabilities can build on your existing data infrastructure and skill set to drive new opportunities, with solutions that can be fully hosted or used to control data processing entirely within your own cloud environment.

Myth #6: Real-Time Data Is Limited to Specific Use Cases

Real-time data processing isn't limited to fraud detection, logistics, or supply chain management, although it has a clear role to play in those areas. From package tracking to gaming leaderboards, real-time applications are becoming the norm. Customers expect instant access to information, whether it's their bank account balance or the status of their delivery. Modern platforms like Decodable support stateful processing functionality that includes joins, aggregation, and pattern matching, in addition to transformation and connectivity.

Your business likely has numerous real-time use cases that could unlock new revenue opportunities and enhance customer satisfaction. Whether it's optimizing inventory management, personalizing customer experiences, or improving operational efficiency, real-time data processing ensures you have the right information, in the right format, at the right time to achieve your goals.

The Time for Real-Time Is Now

While real-time processing may have once seemed daunting and expensive, the landscape has shifted. Tools like Decodable have made real-time capabilities accessible to businesses of all sizes. And just as the cloud was once a foreign concept, real-time data is becoming the new standard.

Join our upcoming tech talk Decoding the Top 4 Real-Time Data Platforms Powered by Apache Flink on June 18, 12pm EST as Eric Sammer, Founder and CEO, Decodable explores the different offerings available for running Flink in the cloud, how managed solutions simplify its complexities, and how that enables organizations to focus on innovation while dedicated experts manage the infrastructure.

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Eric Sammer

Eric Sammer is a data analytics industry veteran who has started two companies, Rocana (acquired by Splunk in 2017), and Decodable. He is an author, engineer, and leader on a mission to help companies move and transform data to achieve new and useful business results. Eric is a speaker on topics including data engineering, ML/AI, real-time data processing, entrepreneurship, and open source. He has spoken at events including the RTA Summit and Current, on podcasts with Software Engineering Daily and Sam Ramji, and has appeared in various industry publications.

Less than a decade ago, it was common to hear "We don’t do cloud. We don’t need it." Back then, on-premises infrastructure was the default. But as time marched on, the landscape shifted, and today cloud-hosted infrastructure is the backbone of nearly every major enterprise. The advantages of embracing the cloud—agility, scalability, cost savings—became too compelling to ignore. 

Now, we find ourselves at the precipice of a similar transformation with real-time data. Just as there were naysayers about the cloud, there are those who may proclaim, "We don’t do real-time. We don’t need it." But much like it did for the cloud, the tide is turning, and the benefits of real-time data are becoming increasingly apparent. However, despite numerous public examples across every major industry, several myths about real-time data processing remain. Let's debunk these misconceptions and explore why real-time data is not only achievable but essential for modern businesses.

Myth #1: Real-Time Data Is Just for Constantly Ticking Graphs

When people hear "real-time," they often envision graphs in a dashboard being updated once per second or typing indicators in a chat application. In actual practice, real-time data is more about ensuring data and analytics align with reality, delivering accurate and up-to-date analytics, training machine learning models, and providing data to customer-facing services.

Real-time data movement is fast becoming the default way data is captured, processed, and delivered, supporting both online and offline use cases. This approach allows organizations to power all applications and services with the same data, delivering intuitive customer experiences that meet their expectations and respond to the real world.

Myth #2: Real-Time Data Means Ripping Out Your Data Warehouse

Real-time data pipelines complement your existing infrastructure, enabling continuous processing between systems. This ensures that your analytics accurately reflect the present reality without requiring a fundamentally different skill set or data platform architecture. The value of data warehouses, analytical processing systems, and other repositories remains intact and is further enhanced by allowing a new class of analytics and workloads. You can choose to do as little or as much ingest-time processing of data as necessary for your use case, and can continue to do batch processing in these systems where that makes sense.

Myth #3: Real-Time Data Comes with Complex Reconciliation Processes

Real-time streaming systems come with well-defined data processing guarantees, such as exactly-once or at-least-once processing, minimizing or eliminating the need for cumbersome reconciliation processes. Each event is processed automatically and accurately, maintaining data integrity by design—even in the case of failures.

In contrast, batch processing environments often require reconciliation processes that “fix up” data to ensure quality and consistency. This reconciliation process involves multiple steps, including collecting, matching, comparing, and resolving discrepancies between datasets. While some discrepancies can be resolved automatically based on predefined rules, others may require manual intervention, especially in cases of complex errors that require human judgment.

Overall, modern real-time streaming systems streamline data processing by handling potential duplicates or failures automatically, while batch processing environments may require more extensive reconciliation efforts to maintain data integrity.

Myth #4: Real-Time Data Is Expensive and Complex

Contrary to popular belief, real-time data is no longer prohibitively expensive. Tools and services like Decodable have made real-time ETL and stream processing accessible and affordable for businesses of all sizes with features like object storage for state, native ARM hardware support, change data capture, efficient resource management, and tons of processing engine optimizations. That said, not all real-time data platform offerings provide the same degree of capabilities, features, and functionality. Download our new ebook, Decoding the Top 4 Real-Time Data Platforms Powered by Apache Flink, to understand the pros and cons of each.

Myth #5: Real-Time Data Is Only for Large Enterprises

Real-time data processing is not reserved exclusively for tech giants or the Fortune 500. Businesses of all sizes can gain a competitive edge and enhance their customer experiences, regardless of the scope of their needs, scale, or budget. These capabilities can build on your existing data infrastructure and skill set to drive new opportunities, with solutions that can be fully hosted or used to control data processing entirely within your own cloud environment.

Myth #6: Real-Time Data Is Limited to Specific Use Cases

Real-time data processing isn't limited to fraud detection, logistics, or supply chain management, although it has a clear role to play in those areas. From package tracking to gaming leaderboards, real-time applications are becoming the norm. Customers expect instant access to information, whether it's their bank account balance or the status of their delivery. Modern platforms like Decodable support stateful processing functionality that includes joins, aggregation, and pattern matching, in addition to transformation and connectivity.

Your business likely has numerous real-time use cases that could unlock new revenue opportunities and enhance customer satisfaction. Whether it's optimizing inventory management, personalizing customer experiences, or improving operational efficiency, real-time data processing ensures you have the right information, in the right format, at the right time to achieve your goals.

The Time for Real-Time Is Now

While real-time processing may have once seemed daunting and expensive, the landscape has shifted. Tools like Decodable have made real-time capabilities accessible to businesses of all sizes. And just as the cloud was once a foreign concept, real-time data is becoming the new standard.

Join our upcoming tech talk Decoding the Top 4 Real-Time Data Platforms Powered by Apache Flink on June 18, 12pm EST as Eric Sammer, Founder and CEO, Decodable explores the different offerings available for running Flink in the cloud, how managed solutions simplify its complexities, and how that enables organizations to focus on innovation while dedicated experts manage the infrastructure.

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Simply enter your email address here and we'll send you the next issue as soon as it's published—and nothing else, we promise!

Eric Sammer

Eric Sammer is a data analytics industry veteran who has started two companies, Rocana (acquired by Splunk in 2017), and Decodable. He is an author, engineer, and leader on a mission to help companies move and transform data to achieve new and useful business results. Eric is a speaker on topics including data engineering, ML/AI, real-time data processing, entrepreneurship, and open source. He has spoken at events including the RTA Summit and Current, on podcasts with Software Engineering Daily and Sam Ramji, and has appeared in various industry publications.

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