Data-intensive applications : Top 10 features of data-intensive applications

In the last decade we have seen many interesting developments in databases, in distributed systems, and in the ways we build applications on top of them. There are various driving forces for these developments:

•  Internet companies such as Google, Microsoft, Amazon, Facebook, LinkedIn, Netflix, and Twitter are handling huge volumes of data and traffic, forcing them to create new tools that enable them to efficiently handle such scale.

•  Businesses need to be agile, test hypotheses cheaply, and respond quickly to new market insights by keeping development cycles short and data models flexible.

•  Free and open source software has become very successful and is now preferred to commercial or bespoke in-house software in many environments.

•  CPU clock speeds are barely increasing, but multi-core processors are standard, and networks are getting faster. This means parallelism is only going to increase.

•  Even if you work on a small team, you can now build systems that are distributed across many machines and even multiple geographic regions, thanks to infrastructure as a service (IaaS) such as Amazon Web Services.

•  Many services are now expected to be highly available; extended downtime due to outages or maintenance is becoming increasingly unacceptable.

Data-intensive applications are pushing the boundaries of what is possible by making use of these technological developments. We call an application data-intensive if data is its primary challenge—the quantity of data, the complexity of data, or the speed at which it is changing—as opposed to compute-intensive, where CPU cycles are the bottleneck. 

Image reference - Designing Data-Intensive Applications by Martin Kleppmann

Here are top 10 features of data-intensive applications:

1. Data Management: The ability to efficiently manage and store large datasets is a critical feature of data-intensive applications. This feature allows organizations to scale up their data processing capabilities and handle large volumes of data.

2. Data Visualization: Data visualization tools are essential for data-intensive applications as they provide a way for users to analyze and make sense of complex data sets. These tools often utilize dashboards, graphs, and charts to represent data in an easily digestible format.

3. Machine Learning: Incorporating machine learning capabilities into data-intensive applications can enable automated decision-making processes and improve the accuracy and speed of data analysis.

4. Data Mining: Data mining is a crucial feature that involves searching for patterns and relationships in large datasets. It helps users identify trends, anomalies, and other insights that might not be evident at first glance.

5. Real-time data processing: With the rise of fast-paced industries, real-time data processing provides users with the ability to analyze data, issue alerts, and perform actions in real-time.

6. Data Security: Data-intensive applications often contain sensitive information, so strong data security measures are critical. These measures can include user authentication, encryption, data masking, access control, and monitoring.

7. Analytics: Advanced analytics features allow users to apply statistical and machine learning techniques to large datasets. These features help users gain insights and predictions on future trends from the data.

8. Scalability: As the volume of data increases, data-intensive applications must be able to handle large and complex data sets. Scalability features ensure that the application can sustainably manage the growing demand without latency or system failures.

9. Integration with third-party tools: Seamless integration with other applications and services is essential for data-intensive applications. Integration allows users to extract data from various sources, such as social media feeds, web portals, or other applications.

10. Support for Open-source frameworks: Open-source frameworks such as Apache Hadoop, Spark, Kafka, and others support processing large datasets. Many data-intensive applications support these frameworks and leverage their processing capabilities to deliver powerful features.