Introduction

Data streaming is now considered a critical business requirement in every industry. We are living in a world of interconnected systems and devices, continuously generating ‘events’ that are causing data volumes to grow at lightning speeds. Not being reactive to real-time data is no longer an option for businesses that wish to remain competitive. As published in the ‘2023 state of streaming’, it’s estimated that 76% of organisations that exploit streaming data reap 2x to 5x in returns, acknowledging it as a significant business enabler.

Data streaming spreads like wildfire. One successful project sparks interest and use cases amongst other teams, keen to improve the latency of their decision-making and not be left behind. At the same time, as streaming spreads across different teams and new business units, this is when growing pains begin to arise.

Challenges in Scaling Data Streaming

Access Management & Data Security

Streaming data adoption means handling PII and PCI data. Regulation forces you to know how this data is used and by whom. Not to mention you must encrypt your data at rest, but this isn’t supported out of the box with the de facto streaming technology, Apache Kafka. Your developers need tooling to troubleshoot and debug (yes, even in production!), so access and permissions must be managed securely for both applications and humans.

Onboarding New Projects & Teams

When data streaming becomes the backbone for data movement in your business, it has a knock-on effect on internal processes as it can spawn many new projects. Onboarding these projects requires the creation of new resources that are commonly served by a central platform team. This team can swiftly become a bottleneck, unable to serve the demands of the developers and the business in reasonable time frames.

Business Continuity & Resilience

The pinnacle of streaming is real-time decision-making. This means serving use cases on the critical path of a company’s business. With such power also comes great responsibility, as the consequence of a production incident can be financial, repute, or a severely impacted customer experience. Streaming applications and pipelines need robust testing, and teams need proactive alerts when anomalies occur.

Facilitating Developer Autonomy with Organizational Control

Based on Conduktor’s experience helping over 15,000 businesses on their data streaming journey, this is the secret sauce for scaling adoption securely and efficiently. Taking from data mesh principles, a self-serve data platform drives efficient processes, while federated governance and security controls allow an organization to enforce over-arching rules.

The result: developers have the freedom to innovate quickly, create and collaborate, while platform teams are comfortable knowing that safeguards are in place to mitigate risk.

About Conduktor

Conduktor is on a mission to transform how businesses interact with real-time data. Our software, built for organizations using Apache Kafka as data infrastructure, enhances security, improves data governance, and simplifies complex architectures. By applying Data Mesh principles, Conduktor accelerates real-time data adoption and usage to drive impactful business outcomes.

We equip platform teams with tools that offer flexibility in enforcing organizational standards and rules, ensuring tailored and efficient data management solutions.

Contact us to discuss your use cases. We are Kafka experts and want to build out-of-the-box and innovative solutions for enterprises using Apache Kafka, so we are very interested in your feedback.