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Automating Safety

In today’s software development landscape, test automation plays a pivotal role in ensuring the reliability and stability of applications. However, automating tests, especially when dealing with complex environments, introduces a set of challenges, one of which is the accidental invocation of production profiles. In this blog post, we’ll explore the potential pitfalls of this scenario and discuss how adhering to the principles of the 12-factor app and embracing chaos engineering can help prevent these accidents.

The Perils of Accidental Production Profile Invocation

Accidentally invoking a production profile during the testing phase can have catastrophic consequences. Production profiles are configured to work with real, live data and systems, making them highly sensitive to any changes. Here are some common scenarios that highlight the risks:

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Rust and Scylla DB for Big Data

Do you ever wonder about a solution that you know or you wrote is the best solution, and nothing can beat that in the years to come? Well, it’s not quite how it works in the ever-evolving IT industry, especially when it comes to big data processing. From the days of Apache Spark and the evolution of Cassandra 3 to 4, the landscape has witnessed rapid changes. However, a new player has entered the scene that promises to dominate the arena with its unprecedented performance and benchmark results. Enter ScyllaDB, a rising star that has redefined the standards of big data processing.

The Evolution of Big Data Processing

To appreciate the significance of ScyllaDB, it’s essential to delve into the origins of big data processing. The journey began with the need to handle vast amounts of data efficiently. Over time, various solutions emerged, each addressing specific challenges. From the pioneering days of Hadoop to the distributed architecture of Apache Cassandra, the industry witnessed a remarkable evolution. Yet, each solution presented its own set of trade-offs, highlighting the continuous quest for the perfect balance between performance, consistency, and scalability.  You can check here at the official website for benchmarks and comparisons with Cassandra and Dynamo DB.

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Ways To Stop and Resume Your Kafka Producer/Consumer at Run-Time

Imagine you are running a Kafka cluster, and suddenly you need to perform maintenance on one of your Kafka clients or producers. What do you do? In this blog, we will explore how to stop and resume a Kafka client or producer at runtime using the Java client API.

Kafka has become an indispensable building block for streaming data pipelines due to its high throughput, fault tolerance, and scalability, which make it an excellent option for processing large volumes of data in real time. Additionally, it offers the significant advantage of supporting several programming languages, including Java, Python, Kotlin, Rust, and others.

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Think Reactive and Native With Quarkus, Kotlin, and GraphQL

Introduction

In today’s world of software development, the terms “native” and “reactive” have gained significant popularity, becoming crucial considerations for developers, architects, and businesses alike.

Regardless of whether you’re building front-end applications or back-end systems, the native and reactive approach has become a crucial component of modern software development’s non-functional requirements. This blog aims to explore the significance of these two concepts and why they have become so essential.

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Spice Up Your ‘CI/CD Process’ With Automation Using Cucumber, Selenium, and Kotlin

Continuous Integration & Continuous Deployment

Imagine you are working on a two weeks sprint, and your agile teams have different assignments that can only be tested through Integration Tests, which may take numerous builds in shorter periods. I still remember that a Build Engineer yelled at me for asking for three builds within an hour. With the power of CI/CD, 60% of this problem is solved with Rapid releases with just a click of the mouse or build hooks.

Problem Statement

Your PL/SQL Developer has optimized an SQL Query which works like a charm, now, he wanted to tweak the Stored Procedure furthermore, and he wanted a Quality Engineer to do the regression testing.