The rapid proliferation and increased volume of data across industries has magnified the need for organizations to have a solid strategy in place for processing and managing real-time data. Improving overall data capabilities enables teams to operate more efficiently, and emerging technologies have even created a smoother pathway for bringing real-time data closer to business users, which plays a critical role in effective decision-making. By Miguel Garcia.
In this article you will learn:
- Beyond the architecture
- Data challenges
- Performance
- Security and compliance
- Architecture patterns
- Stream-to-stream
- Batch-to-stream
- Stream-to-batch
- Lambda architecture
- Kappa architecture
- Streaming architecture
- Reference Architecture: Streaming architecture with Apache Kafka and Apache Druid
… and more. There are several key data architecture patterns — each one with its strengths, limitations, and use cases. The architecture we choose plays a pivotal role in determining performance, adaptability, and business impacts. However, the knowledge of these architectures alone won’t guarantee success because there are other factors to consider, such as adequate training, team size, cost, or even organizational culture, that may affect decision-making. Good read!
[Read More]