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In the era of big data and bigger AI, businesses are relying more on the importance of real-time data processing and analytics. Streaming data is a powerful paradigm for handling continuous, unbounded streams of data in real time. However, despite benefits like reduced latency, improved responsiveness, and the ability to make data-driven decisions on the fly, many companies are still reluctant to fully embrace this technology. In this article, we’ll look at the five main technical reasons why companies have been hesitant to adopt data streaming and explore solutions to these challenges.
Complexity and Infrastructure Requirements
One of the main technical hurdles with streaming data is the inherent complexity and infrastructure requirements of this neverending flow of information. Streaming data calls for more complicated infrastructure because it isn’t easy to just divide yesterday’s data from today’s data when the data doesn’t stop.
Streaming data systems must be designed to handle unbounded, continuous data streams. This requires a fundamentally different architecture compared to traditional batch processing systems. Companies need to invest in scalable, fault-tolerant, and distributed infrastructure to reliably streaming their data. Complex systems like Apache Kafka can be challenging for companies that don’t have the necessary expertise and resources needed to manage these systems. At the same time, streaming data systems must operate 24/7, driving significant operational costs for compute, storage and networking.
It’s important to remember that Apache Kafka has been around for more than a decade. Some of the newer Kafka-compatible alternatives have advanced both the capabilities and the developer experience. Look for a streaming data platform that removes some of the complexity of these distributed systems, with simpler deployment and day two operations.
Challenges with High-Velocity, High-Volume Data Sources
Another significant technical challenge companies face when adopting streaming data is handling high-velocity, high-volume data sources like IoT data logs, telemetry and clickstream data. Not only is there a constant stream of data, these are very fast data sources that call for low-latency processing. This requires having an always-on scalable infrastructure capable of ingesting and processing millions of events per second, and a distributed architecture that can scale to accommodate the volume and velocity.
To ensure your streaming data platform is up to the task of your use cases, be sure to benchmark performance and review what other adopters have said about limits of scale, partitions, availability, and more.
Difficulty Staffing Streaming Data Experts
The shortage of specialized talent is another significant technical barrier to adopting streaming data. In fact, surveys show that the No. 1 barrier to streaming data adoption is maintaining the in-house expertise to monitor and manage these systems. It’s a niche market, with unique skills, so you can’t just put an ad on LinkedIn and hire someone in a few days.
Streaming data requires a deep understanding of distributed systems, data structures, and algorithms, as well as expertise in optional stream processing frameworks and downstream analytics systems. Developers must be proficient in writing event-driven applications that support low latency and data durability. Moreover, troubleshooting issues in production, as well as fine-tuning infrastructure settings, demand additional proficiency from developers and operations teams.
To address the talent gap, companies can invest in training and upskilling their existing workforce, partnering with educational institutions to develop specialized curricula, or turn to a fully managed cloud service offering to reduce the operational overhead of maintaining a streaming data platform.
Integration with Legacy Systems
Integrating streaming data with existing legacy systems is another significant technical challenge. Many organizations have invested heavily in their existing data infrastructure, with a mix of relational databases, data warehouses and batch-processing systems. Integrating these legacy systems with streaming data platforms requires building custom connectors, handling schema evolution and ensuring data consistency and compatibility across different systems. This can be a complex and time-consuming process, requiring significant engineering effort and expertise.
To streamline the integration process, companies can leverage pre-built connectors and integration frameworks, which provide a pluggable architecture for connecting various data sources and sinks to streaming data platforms. Any platform with a schema registry will help ensure data compatibility and seamless schema evolution, reducing the complexity of managing data contracts between different systems.
Data Security and Compliance Concerns
Data security and compliance are critical technical concerns for companies considering the adoption of streaming data. Enforcing controls like role-based access control policies on streaming data adds an additional wrinkle to the data management process.
Streaming data often contains sensitive information, such as personally identifiable information (PII), financial data, or health records, which must be protected in accordance with various regulations and industry standards, such as GDPR, HIPAA, or PCI-DSS. Companies must implement robust security measures, including encryption, access control, and data masking, to ensure the confidentiality, integrity, and availability of their streaming data. Additionally, they must have proper auditing and monitoring mechanisms in place to detect and respond to security incidents and demonstrate compliance with relevant regulations.
Despite the complexity of managing and maintaining these systems, many companies still choose to self-host their streaming data platform in order to maintain data sovereignty – especially in highly regulated industries and geographies. However, a new approach to Platform as a Service (PaaS) architecture has emerged that enables companies to enjoy the convenience of a fully managed service while keeping all their data exclusively in their own virtual private networks (VPCs). The Bring Your Own Cloud, or BYOC, deployment model enables full data sovereignty while also leaving the work of monitoring and managing streaming data clusters to the vendor experts.
Additionally, it makes sense to look for solutions with built-in support for encryption, authentication, and authorization, ensuring that data is protected both in transit and at rest. Robust monitoring and logging capabilities can provide visibility into a system’s operations and enable companies to detect anomalies and demonstrate compliance with audit requirements.
Conclusion
Streaming data offers significant potential for companies to gain real-time insights, improve operational efficiency and drive innovation. However, companies looking to gain these benefits must overcome several challenges to successfully leverage this technology.
These challenges include complexity and infrastructure requirements, handling high-velocity, high-volume data sources, difficulty finding specialized talent, integration with legacy systems, and data security and compliance concerns. However, new technologies have emerged in this space to help overcome these challenges, with modern streaming data platforms that provide a simplified, scalable and more secure solution for building and deploying streaming data pipelines.
As the volume, velocity, and variety of data continue to grow, streaming data will become an increasingly critical capability for businesses across industries. By understanding the technical challenges associated with this technology, and considering the diverse ecosystem of tools, platforms, and organizational dynamics, companies can position themselves to thrive in the data-driven economy of the future.