Stream Processing: Datenströme im Echtzeit-Analyzer

Stream Processing: Analyze data streams in real time

With stream processing, data can be processed directly and without intermediate storage - and thus avoid large data collections. After their creation, the data from the data source are combined into streams and forwarded to the recipient. With stream processing, companies are able to create fast data analyzes and add new insights to statistics.

stream processing provides

Real-time data analysis
Dealing with large data streams
Processing of data from different sources

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Stream processing: more productivity with open source technologies

With stream processing, insights into workflows and processes can be gained and reactions to events can be triggered within a few seconds. To enable companies to process large amounts of data immediately, other technologies are used in stream processing, including Apache Kafka, Apache Flink and ksqlDB.

Apache Kafka

Apache Kafka is an important basis for stream processing. The open source software not only allows data streams to be stored but also processed via a distributed streaming platform.

Apache Flink

Apache Flink enables data stream processing and real-time insight processing. For this purpose, the framework can be used on the most important platforms.

ksqlDB

ksqlDB is a streaming engine specially developed for Apache Kafka and supports a wide variety of stream processing functions such as filtering, transformation, windowing and aggregation.

Our stream processing services

As a confluent partner for Apache Kafka and Apache Flink, we support companies in gaining real-time insights from data streams and in processing and using them in a meaningful way.

Kafka workshop

With the popular open source software, data streams can be scaled, stored and processed via a distributed streaming platform.

IT consulting

Complex and (un)limited data streams can be processed in all common cluster environments via Flink.

Direct implementation

We take matters into our own hands instead of beating around the bush. In this way, we find the fastest way to the ideal solution and create lean ones processes.

Stream Processing and Big Data

In many industries, large amounts of data are collected every day, such as in the insurance industry, in banking or in e-commerce. And the number is growing all the time. Methods such as stream processing are required to make the information generated by big data tangible and to recognize patterns and trends. As a result, companies are able to make better use of their data and use the insights and analyzes gained from it profitably. The flexible approach to stream processing also enables them to adapt better to new requirements and adapt and align their IT infrastructures accordingly.

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Real-time insights? Stream processing is the solution

Stream processing involves working with data that is in motion. This has the advantage that, for example, analysis results can be viewed in a very short time and the information is available for further processing. Other benefits include:

Real-time processing

With stream processing, data is processed in real time as it arrives, while with batch processing, the data must first be stored.

Less data movement

Data is processed where it is generated and does not need to be moved between processing and storage tiers.

Easy scaling

Stream processing is highly scalable and suitable for large amounts of data, since high volumes and speeds can be handled well.

High fault tolerance

In the event of disruptions and failures of individual processing units, stream processing systems remain active due to their high fault tolerance.

Better data quality

Data can be validated and normalized in a timely manner using stream processing systems. This significantly increases the quality of the data.

Better cost efficiency

Because the data is processed without intermediate storage during stream processing, no large storage capacity is required.

Frequently Asked Questions

What is stream processing?

Stream processing is an alternative method of data processing. In contrast to batch processing, data is processed in real time and without intermediate storage. This avoids large accumulations of data, which can be particularly helpful in the area of big data. When companies depend on processing data as quickly as possible, batch processing quickly reaches its limits. In the case of analysis results, for example, which become less meaningful due to the delay. In addition, the resource requirements for storing data increase as more data is generated. Stream processing circumvents this problem.

How does stream processing work?

With stream processing, data is forwarded and processed with minimal delay immediately after it is created. A data stream is generated from a data source, which consists of a large number of individual data items in a specific format. It is received by a recipient and processed further. A forwarded data stream can trigger certain actions, such as updating data analyzes and statistics or creating new data streams.

How is the data processed in stream processing?

When it comes to processing, a distinction is made between native streaming and micro batching. With native streaming, a data stream is processed directly, while with micro batching, smaller units of data streams are collected in order to then process them further. The advantage of native streaming is further processing in real time and without intermediate storage.

What open source platforms do you recommend?

Due to our expertise and as a confluent partner, we are happy to recommend our customers to use the streaming platform Apache Kafka and Flink.