programming

23 Sep 2026

DZone Java Zone

How to Build an AI Agent to Generate Selenium WebDriver Tests in Java: A Practical Guide for Test Automation Engineers

Artificial intelligence is transforming software testing by enabling faster test creation, smarter execution, and more efficient quality assurance processes. With AI agents, we can generate test cases and scripts, run tests, and produce detailed reports with minimal manual effort.

AI agents are software systems that leverage artificial intelligence to achieve goals and perform tasks on behalf of users. They can think through problems, plan actions, and remember things, while also making decisions on their own and improving over time.

23 Sep 2026 4:00pm GMT

22 Sep 2026

DZone Java Zone

Valkey: Bringing Key-Value Databases to Enterprise Java

Enterprise applications commonly face multiple data challenges. Some data requires transactional integrity and relationships, while other data prioritizes fast, predictable access. Sessions, counters, rate limits, temporary state, often-accessed objects, and coordination data may not benefit from the complexity of a relational model. In these cases, a key-value database's simplicity becomes an architectural advantage.

This simplicity is especially valuable in distributed and cloud-native systems, where latency, throughput, plus scalability directly shape user experience and infrastructure costs. A key-value database offers a focused approach: identify data by a key and retrieve or update it efficiently. The challenge is selecting a technology that delivers this performance while meeting the operational maturity, ecosystem support, and governance standards required for enterprise applications.

22 Sep 2026 12:00pm GMT

15 Sep 2026

DZone Java Zone

dbt Meets Apache Flink: One Workflow for Data Engineers

Data engineers managing batch SQL pipelines on Snowflake, BigQuery, and increasingly Databricks, and streaming pipelines on Apache Flink face a familiar problem: two toolchains, two skill sets, two CI/CD pipelines.dbt is now extending into stream processing. This post explains what that means in practice, why it matters for data engineering teams, and what a concrete implementation looks like with Apache Flink on Confluent Cloud.

Data Streaming Meets the Lakehouse

Data lakes promised to solve the enterprise data problem. The reality has been messier. Batch pipelines produce stale information, and analytical workloads run hours after the business event occurred. By the time a query runs, the window for action is often already closed.

15 Sep 2026 12:00pm GMT