Streaming Data Dilemma:
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 brazil whatsapp number data 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.