Articles, tutorials, and insights about data integration, event processing, and building scalable workflows.

A practical comparison of stream processing approaches — covering latency, operational complexity, and the team fit that actually determines the right choice

The difference between near-real-time and actually-real-time, and why the gap costs more than you think

The real reasons production data pipelines break in the middle of the night — and the engineering practices that prevent it

A data architect at a Fortune 500 once told me they spent 14 months migrating to Kafka — then quietly switched half the pipelines back to batch. Here's why that keeps happening, and what I'd do instead.

You've got Airflow running. Your team knows it. The DAGs work. So why are you suddenly hearing 'we need real-time' — and what do you actually do about it?

How Local Data Filtering Can Cut Your Cloud Bill by 90% — And What Yours Could Look Like

Why real-time data matters, what makes migration hard, and how to think about the transition — whether you choose layline.io or another path

From invisible scaling to invisible invoices—why engineering teams are ditching FaaS for persistent, predictable data engines.

At layline.io, we've harnessed the robust capabilities of Apache Pekko to bring you a comprehensive low-code event-processing platform. With our solution, you can leverage the full potential of Apache Pekko without writing a single line of code.

In an age where data rules supreme, managing and orchestrating the vast sea of information has become the backbone of numerous businesses and industries.

layline.io selected one of the most influential IT-Startups in Hamburg, Germany by EU Startup News.
The hospitality industry has seen significant technological advancements in recent years. This includes data integration software being one of the most critical tools that the industry can leverage to optimize their operations.

In today's fast-paced world, every business is looking for better ways to process large volumes of data. Traditional data processing methods have their limitations, which is why companies are increasingly exploring event-based asynchronous data processing.

How does layline.io compare to Kafka? This is a question which we hear from time to time. We wonder why.

ReST interfaces are popular and abundant. We show you how to configure Http-Client requests within layline.io using Yahoo Finance as an example.

It's hard to understand what's actually happening in complex processing scenarios. layline.io helps by providing probing tools to get insights into inner workings of complex Workflows at runtime.

ASN.1 is still a popular data format. Learn how easy it is to configure any ASN.1 format in layline.io.

Dealing with complex data formats and changes can be daunting. Learn how layline.io tackles this challenge using a configurable grammar language.

Showcase on how to read data from a structured file, map record data, and output the data Kafka cloud.

The traditional Microservices model on Kubernetes/Docker has some disadvantages which result in overly complex management and resource consumption. We explain the background and how layline.io can help.

You may be missing out if you are data-driven only. In fact, every business is data-driven. But you should ask yourself what that really means, and whether it is sufficient for you tomorrow.

For some years now Microservices and Service-oriented architectures have been all the rage. But there are downsides. Can they be overcome?

How to deal with data pressure in non-stop message-driven solutions and ensure non-stop uptime under load.
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