The four best use cases for data streaming

The four best use cases for data streaming
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Confluent calls the four best use cases for data streaming

Von Martin Hensel

Real -time analyzes of data play an important role across all areas for companies.If it is about the real -time processing of data in motion, Apache Kafka is de facto standard according to Confluent.With four use cases, the company shows essential operational scenarios.

Company on the subject

Confluent Germany GmbH

Kai Waehner, Field CTO of Confluent, has compiled the four most important applications for data streaming.They show how companies will bring their processes for data processing and provision to a new level in the current year.

Event-based real-time data processing

When it comes to building a new IT infrastructure, many IT departments still use the proven Lambda architecture today.It consists of two parallel layers: the batch and the speed layer.This enables it to process parts of large amounts of data in real time.The Lambda architecture is associated with some restrictions: On the one hand, this is a very complex architecture, since batch and streaming processing require different code bases that require maintenance and synchronization.In addition, the use of three layers means that data must be processed several times, which leads to higher operating effort and increasing costs.

Die vier besten Use Cases für Daten-Streaming

Companies are therefore increasingly switching to a Kappa architecture: it is also able to manage the real-time processing of large amounts of data.However, Kappa uses only one code based on a code base and scalable infrastructure, in which the applications can process data both in both batch and streaming processes.Since Kappa is an event-driven architecture, the event streaming platform Apache Kafka can be inserted excellently as the central nervous system of architecture.

Hyperpersonalized omnichannel

Apache Kafka will play an increasingly important role in terms of customer satisfaction both online and offline communication channels.The flexible and highly scalable infrastructure forms the basis for real -time processing, integration and correlation of data in motion.In addition, thanks to the decoupling of systems, various corporate areas can access all Kafka clusters and thus all generated data-whether the data in the partner's production warehouse, the regional logistics center, or in the store can be processed and saved.

In the retail area, various use cases are conceivable from product production to marketing and sales to aftersales.During production, customers can observe the status of the manufacturing and delivery process, for example, and know exactly when they can expect their purchase.The marketing department is able to correlate online and offline customers in real time.Machine-Learning models can be used to dine and train efficiently with data to recommend more suitable products and services or to optimize the maintenance process of products ("Predictive maintenance").

Kafka connects Cloud, Multi-Region and Edge

Not only the workforces of companies work in view of the developments in recent years: business areas and IT infrastructures also extend today over various IT environments as well as over several regions and continents.This increasingly creates a need for technologies that combine different systems and localities effectively and cost-effective and enable real-time data provision.For example, more and more companies are relying on hybrids or multi-cloud environments.

Apache Kafka guarantees the processing and integration of data in motion regardless of the provider in such environments.In some other cases, for example, a Kafka cluster for national cooperation is no longer sufficient.With the help of replication, locally processed data can be transferred to several clusters so that distributed business areas can access them.In addition, Apache Kafka can also be deployed in its own data center or even outside "at the edge", for example in a factory or in a vehicle.Here the same open, flexible and scalable architecture is guaranteed as in a cloud or on-premium environment.This benefits, among other things, retail, mobile phone providers, local and long-distance transport as well as smart manufacturing companies.

Cyber security in real time

According to the IBM, companies needed an average of 228 days in 2020 to identify a cyber attack - a much too long period in which personal or corporate -critical data, privacy and the security of all systems are at stake.But how does Apache Kafka fit the topic of cyber security when it comes to an event streaming platform and not a pure cybersecurity tool?

The response to cybercriminal activities and anomalies is all about recognizing them quickly ("Mean Time to Detect") and acting just as quickly ("Mean Time to Response").As a result, companies have to drastically shorten these periods of time.For this, data from all areas of the company must be integrated, processed, analyzed, correlated and transported across the entire company.Data in motion is therefore an important part of cyber security strategies.Apache Kafka ensures the processing, correlation and integration of this data in a scalable and highly available manner in order to be able to quickly identify and remedy anomalies.Therefore, event streaming often forms the basis for building a modern, scalable cyber architecture and serves as a combination with specific security solutions.

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