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Unlocking Real-Time Insights: Overcoming the Challenges of Streaming Data

Streaming data is essential to create a living data stack where everything updates automatically, but there can be multiple layers of complexity to work through.

Use of streaming data architectures is growing as businesses look for real-time insights and automation that can keep up with business today. Apache estimates that over 80 percent of Fortune 100 companies use Apache Kafka. As a data infrastructure engineer and evangelist, I've seen firsthand how companies struggle with the complexity of managing streaming data systems. Although the benefits of real-time insights are clear, the path to successful adoption can be challenging. In this article, I'll share some key strategies for simplifying the streaming data journey.

For Further Reading:

Enabling the Real-Time Enterprise with Data Streaming

Streaming Data and Message Queuing in the Enterprise

The Benefits of Streaming Data Are Contagious

Generative AI and emerging real-time technologies such as retrieval-augmented generation (RAG) promise to make real-time data streaming and event monitoring even more important. However, many companies find themselves still grappling with the complexity of managing these systems.

Streaming data is essential to create a living data stack where everything updates automatically, but there can be multiple layers of complexity to work through -- from standing up distributed systems to modeling data for reuse across an organization.

Adoption Challenges

First, it's essential to recognize that moving from batch processing to real-time streaming requires a significant mindset shift. Developers must grapple with new concepts such as the publish-subscribe model and learn to design for distributed systems. Traditional databases follow a request-response paradigm, but with streaming, you're constantly sending data to a topic and then subscribing to read from that topic. Architecting for the high-availability and fault-tolerance needs of a distributed system also requires a different approach.

To navigate this learning curve, it’s crucial to invest in education and training. Companies should encourage developers to experiment with streaming concepts in a safe, local environment. Look for tools that abstract away some of the underlying complexity. For example, some streaming platforms allow you to get started with a single command rather than requiring multiple dependencies. The easier it is for developers to test and explore, the faster they'll be able to build real-world applications.

When it comes to deploying streaming systems, there are a few key considerations. Some companies choose to build in-house expertise around open source tools such as Apache Kafka. This can provide a high degree of control, but the operational overhead can be significant and require a staff of trained experts to build and maintain the system. Managed services offer an alternative by providing a fully hosted streaming platform. However, it's important to be thoughtful about data custody and security. Choose services that give you transparency and control over where your data resides to ensure you adhere to global privacy regulations.

Bring Your Own Cloud

An emerging approach aims to provide the best of both worlds by allowing companies to leverage managed services within their own cloud environments. A “bring your own cloud” approach allows developers to leverage managed services while continuing to deploy to their own cloud environments. This gives a company the flexibility to choose a cloud provider and geographic regions while benefiting from expert support in managing the streaming platform.

Regardless of the deployment model, data governance is essential. Companies should develop clear policies about data access, security, and retention. Implement processes to ensure that data is being used ethically and in compliance with relevant regulations. By putting these guardrails in place from the outset, a company can avoid costly missteps later.

Before embracing streaming, start with a clear vision of how real-time data aligns with your business goals. Real-time data architectures enable businesses to make decisions based on up-to-the-minute information rather than yesterday's batch analytics. The use cases include retail giants optimizing inventory based on real-time shelf sensors, national restaurant chains balancing ingredient supply with online orders, and financial institutions detecting fraudulent transactions as they occur. Companies experimenting with streaming can start with a pay-as-you-go service that allows them to adopt a managed streaming platform while reducing up-front infrastructure and staffing costs.

Look to the Future

Looking forward, applications for streaming data will only expand. In the realm of AI and machine learning, streaming will play a critical role in coordinating data between multiple models to deliver faster, more contextual results. For AI and ML use cases, companies need both historical data from a data lake or warehouse to train models and real-time data from the stream to make in-the-moment decisions.

As the world moves towards a living data stack, streaming will play a huge role in coordinating data between multiple models to deliver faster contextual results.

The streaming data revolution is here, and it's transforming the way businesses operate. By enabling real-time insights and automation, streaming architectures are helping companies make faster, more sophisticated decisions and stay competitive in an increasingly dynamic marketplace. Although the complexity of these systems can be daunting, the strategies outlined here can help simplify the journey.

By investing in education, choosing the right deployment model, and starting with a clear business vision, your company can harness the power of streaming data without getting bogged down in the operational details. As the applications for streaming grow from real-time analytics to generative AI, companies that embrace this technology strategically will be well-positioned for success.

About the Author

Christina Lin is currently the director of developer advocacy at Redpanda, a streaming data platform. Christina has over 20 years of experience in software development and is an advocate for making innovative solutions down to earth and easily accessible for everyone.

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