fidzu : Java

Batch processing remains vital because many business operations aren't suited to interactive requests. Tasks such as recalculating prices, reconciling transactions, migrating records, generating reports, processing invoices, reclassifying customers, or applying rules across millions of records may require considerable time. Handling these as standard requests leads to fragile systems, increased user wait times, frequent timeouts, challenging retries, and possible data inconsistencies.

A batch model handles large workloads predictably, incrementally, and with control over progress and recovery. Rather than processing a massive operation as a single loop, batch processing uses jobs, steps, chunks, checkpoints, filtering, and restartability. This approach separates long-running data tasks from the user experience while delivering a structured execution model. In this article, we will focus on Jakarta Batch and examine its sustained relevance for modern enterprise applications.

29 Sep 2026 2:00pm GMT

Multi-step flows are common in UX, including onboarding, checkout, account setup, approval processes, configuration wizards, and administrative tasks. These require users to move through multiple screens while continuing a consistent working state. The challenge is to keep this state active for the duration of the interaction, but not beyond. Request scope is too short, while session scope often extends longer than the business process needs.

Jakarta Faces handles this with @FlowScoped, which manages state based on the lifecycle of a flow instead of a single page or the entire session. This article uses a customer segmentation application to demonstrate how a flow can guide users through configuration, preview, and confirmation, while maintaining state across each step. This approach creates a cleaner model for wizard-style UX: the scope begins when the user enters the flow, persists during navigation, and ends upon exit.

29 Sep 2026 12:00pm GMT

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

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

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

API testing is an essential part of modern software development. While sending requests and receiving responses is straightforward, the real value of API automation comes from response verification. A test is meaningful only when it validates that the API returns the correct data, structure, status codes, and business rules.

In Java-based API automation, REST Assured combined with Hamcrest Matchers provides a clean and expressive way to verify API responses. These matchers help testers write readable assertions that validate numbers, strings, arrays, JSON objects, and collections with minimal code.

11 Sep 2026 7:00pm GMT

Running Apache Flink on a mainframe sounds odd at first. A modern stream processing engine on a platform most people call legacy? But take a closer look. It is not only possible. It might be a smart move for some of the largest financial institutions in the world. This post explores why some enterprises want Apache Flink on the mainframe, how it could work, and whether it is a brilliant innovation or a technical detour.

Disclaimer: The views and opinions expressed in this blog are strictly my own and do not necessarily reflect the official policy or position of my employer.

10 Sep 2026 7:00pm GMT

If you ask Java developers about the concept of 'Sneaky Throws,' I am almost sure there will be a couple of opinions that are quite differently expressed, but similar in their meaning. Some will sum it up as being able to throw checked exceptions without declaring them explicitly; others will amend that it means writing functional-style code (lambdas) and being allowed to call methods that throw checked exceptions.

Most probably, it will be surely mentioned that there's a Lombok annotation called exactly @SneakyThrows that solves the problem immediately when put on a method. Last but not least, to outline it in a more pragmatic manner, the concept allows tricking the Java compiler into treating checked exceptions as runtime exceptions.

10 Sep 2026 6:00pm GMT

In a previous article, Working with Spreadsheets in Java: A Practical Overview, we walked through the common scenarios where Java applications need to interact with spreadsheets and the categories of tools available for the job. One of the factors mentioned there was support for modern Excel formulas - a topic that deserves more space than a single bullet point.

Java applications interact with Excel more often than most teams plan for: file uploads from finance, calculation logic authored in a workbook, reporting exports back to business users. The files these users produce today are not the same as the files they produced five years ago. Excel 365 and Excel 2021 introduced a new formula model, and workbooks authored in those versions routinely use it. Depending on which library you use, those formulas may evaluate correctly, fail silently with stale cached values, or throw exceptions at recalculation time.

09 Sep 2026 2:00pm GMT

A pull request arrives. A few hundred lines of Java implementing the new discount rule: tiered thresholds, a regional exception, something about loyalty tiers that nobody can quite explain. It compiles. The tests pass. An LLM wrote it in about forty seconds.

Now: who reviews it?

04 Sep 2026 2:00pm GMT

Every Java developer who runs services on Kubernetes has watched this scene play out. Traffic spikes, the autoscaler adds a pod, and then everyone waits. The container is running in two seconds. The application is not ready for another twelve seconds. During those ten seconds, your existing pods absorb the extra load, latency climbs, and if things are bad enough, the autoscaler panics and adds even more pods that are also not ready.

I spent years treating Spring Boot startup time as a fact of life, the way you treat weather. Then I found out the JVM has had a fix for a big chunk of it since Java 12; it works beautifully inside Docker, and almost nobody bakes it into their images. It is called Class Data Sharing, CDS for short, and this article shows you how to make your Docker build do the work

03 Sep 2026 6:00pm GMT

Any input/output operation, be it accessing a file, handling an HTTP request, or a database connection, is based on 3 fundamental system concepts - file descriptors, kernel memory, and heap size.

This article discusses how modern languages help developers handle behind-the-scenes file descriptor, kernel memory, and heap management. These three concepts are major bottlenecks for scaling.

03 Sep 2026 4:00pm GMT

"...premature optimization is the root of all evil…"

Donald Ervin Knuth

28 Aug 2026 5:00pm GMT

In a chapter of The SingleStore Cookbook, there is a complete sentiment analysis pipeline using Rust compiled to WebAssembly and loaded directly into SingleStore via its Code Engine. The result was clean: one CLI command to deploy, sentiment scoring running inside the database engine alongside the data and a full stock-price-plus-headlines analytical pipeline built on top of it.

Can we do the same thing in Neo4j?

27 Aug 2026 2:00pm GMT

Goose - the open-source, Rust-based AI developer agent from Block (donated to the Linux Foundation's Agentic AI Foundation) - interacts natively with your local development environment via the Model Context Protocol (MCP). In this tutorial, you will learn how to build stateless, cloud-native Java microservices using Quarkus LangChain4j and expose them as governed MCP extensions that Goose can discover and run seamlessly.

Autonomous AI coding agents like Goose go far beyond simple code autocompletion. Built in Rust for speed and portability, Goose runs on your local machine, inspects files, runs terminal commands, and uses tools over MCP to automate complex engineering tasks.

26 Aug 2026 6:00pm GMT

Java Meets the Spreadsheet

Apache POI has been the standard Java library for reading and writing Excel files for over twenty years. It handles the majority of everyday spreadsheet tasks well. But a growing category of real-world Excel files now contains formulas that POI's evaluator cannot execute at all.

This is one of several situations Java developers hit when working with spreadsheets that are not obvious until you are already in production. Business users produce, share, and reason about data in spreadsheets. Finance teams model in Excel. Operations teams track inventory in Excel. Analysts hand deliverables to engineering as .xlsx files. Java applications end up interacting with all of it: back-office services accept Excel uploads, pricing engines run calculations that were originally authored in a workbook, reporting tools export data in a format the recipient can open in Excel without formatting problems.

26 Aug 2026 3:00pm GMT