To build resilient systems, you need to see how they break. Reading Kafka documentation on fault tolerance helps, but intentionally introducing network chaos shows how the underlying mechanics actually behave.

The fragility hidden in that decision took longer to surface than it should have because it never showed up as a framework bug. It showed up as other people's schema changes.

Event sourcing flips that model on its head. Instead of storing current state, you store the sequence of events that produced it, and you derive state on demand by replaying the log. History isn't a side effect you bolt on afterward; it is the write path.

Introduced in 2000, SQLite quickly became a popular choice for database-related tasks. Whether you used it in an introductory database module, built a local data pipeline, or managed local metadata in production, SQLite has long been the gold standard for a zero-configuration database, with more than 25 years of widespread use.

The origin was narrow: streaming pipelines fail. A downstream consumer has an outage, a schema changes, a job crashes partway through, and, let's say, Kafka partitions are already gone due to max retention; when that happens, someone needs a way to re-publish a window of historical data without working with the source system to republish. We built a small tool to do exactly that: point it at a backup table (that held raw events), give it a date range, and it would replay that data back to a Kafka topic.

A batch Spark job on YARN can show memory nearly full and CPU half idle at the same time, and you pay for both. Memory looks full because YARN reports the memory allocated to containers, not the memory in use. CPU looks idle because the executors that fit on each node don't add up to the node's cores. The cluster is billed for every vCPU and uses a fraction of them.

Background: How Spark Sizes Itself on YARN

Spark requests one YARN container per executor [3]. The container size is the executor heap (spark.executor.memory) plus off-heap overhead (spark.executor.memoryOverhead), which defaults to 10% of executor memory with a 384 MiB minimum [1]. The heap divides into a small reserved area, a unified region shared by execution and storage (60% of the usable heap by default, set by spark.memory.fraction), and the remainder for user data structures [2]. YARN also rounds each request up to its allocation increment, so real fits can differ slightly from paper math [4].

That gap is why we built message search for Kafdrop at a hackathon. The setup was familiar from my day job: a platform moving data between pipeline stages over a pub/sub API, with no way to ask where a message went when something breaks. Kafdrop showed partitions and offsets. It couldn't search inside the messages.

A report takes twenty seconds to open. A pipeline that normally finishes before breakfast is still running. Someone sees the capacity above 100% and proposes the obvious fix: move from F64 to F128.

That might be the right decision. It should not be the first one.

Every enterprise with factories, stores, vehicles, hospitals, or devices faces the same architectural question. How does data move between the edge and the data center or cloud, in both directions, reliably, securely, and at acceptable cost? The vendor landscape answers with dozens of overlapping options: MQTT platforms, hyperscaler services, event streaming, event mesh, messaging fabrics, delivery platforms, and protocol proxies. This article structures the option space by traffic pattern instead of by product category, maps the vendor categories onto those patterns, and closes with a decision framework you can apply to your own architecture.

Why the Two Platforms Don't Share Data Natively

Delta Lake and Apache Iceberg are both open table formats, but they track a table's files, schema, and change history differently. Delta Lake is Databricks' native format. Snowflake has strong native support for Iceberg. A table written as Delta Lake isn't something Snowflake can open directly, and a table written as Iceberg by some other engine isn't something Databricks reads as Delta.

1. Create a new index.
2. Copy the rows that belong in search.
3. Replay anything that changed during the copy.
4. Verify the result.
5. Move the search alias.

The difficult part is deciding where the bulk copy ends and the replay begins.

"Store the event ID. Before processing, check if it exists. If it does, skip. Done."

]]> Thu, 22 Oct 2026 16:00:00 GMT https://dzone.com/articles/3682446 Ramesh Yara https://dzone.com/articles/migrate-from-cc-to-rust Embedded and IoT devices operate in environments where every kilobyte of memory and every millisecond of response time matters. Yet most of them still rely on C or C++, languages powerful enough for low-level control but notorious for memory-safety flaws that can crash systems or expose them to security vulnerabilities. The issue is so widespread that around 70% of Microsoft’s and Google’s reported vulnerabilities stem from memory-safety issues. For connected devices running critical operations, such instability isn’t an option. However, a structured C++ to Rust migration addresses the root cause by replacing unsafe memory handling with compile-time guarantees and predictable real-time performance.

Embedded environments leave little room for trial and error; therefore, experience and a clear roadmap make a big difference. For this purpose, companies often work with teams skilled in Rust development services and familiar with embedded software development. With the right expertise, you can introduce new Rust components gradually and safely without interruptions to the existing IoT ecosystem.

]]> Thu, 22 Oct 2026 13:00:00 GMT https://dzone.com/articles/3683604 Andrew Davies https://dzone.com/articles/kestra-enterprise-workflow-orchestration Kestra is an open-source workflow orchestration platform that unifies IT scheduling, data pipelines, business processes, infrastructure automation, and AI agents under one declarative, event-driven control plane. As enterprises modernize legacy systems and adopt agentic AI, unified enterprise workflow orchestration is becoming increasingly important. Here is why I joined Kestra as Global Field CTO and why I believe the orchestration market is approaching a turning point.

Orchestration is the connective tissue of the modern enterprise, and right now nobody owns it. That is the gap I am joining Kestra to help close.

]]> Wed, 21 Oct 2026 14:30:00 GMT https://dzone.com/articles/3687378 Kai Wähner https://dzone.com/articles/hybrid-enterprise-data-refresh If you are a data engineer, you have probably come across this situation where you have to decide between two approaches for refreshing enterprise data. One approach focuses more on speed and less on compute, while the other focuses more on reliability and data correctness. Usually, when we talk about data refresh, the two options that come to mind are incremental load and full load, and the question becomes which one we should choose.

In my opinion, this is not necessarily a choice where we have to pick one approach over the other. Both approaches have advantages and disadvantages, and rather than relying on one approach, I believe we can combine them and use each where it makes the most sense.

]]> Thu, 15 Oct 2026 14:00:00 GMT https://dzone.com/articles/3673263 Pratham Yadav https://dzone.com/articles/consumer-lag-recovery A Kafka recovery often looks successful before it actually is.

The replay job exits. Consumer lag drops. Error rate returns to normal. The dashboard is green enough for everyone to want the incident closed.

]]> Tue, 13 Oct 2026 14:00:00 GMT https://dzone.com/articles/3682556 Ishan Shah https://dzone.com/articles/kafka-with-spring-boot Kafka is a distributed event streaming platform that enables real-time data ingestion, storage, and processing of continuous event streams. To do that, Kafka has a complex internal structure. In this part, I will try to cover an overview of the full internal structure.

Overview

Kafka uses a publish-subscribe model where producers produce events and consumers consume messages. Kafka producers publish events to the broker, and consumers consume the events from the broker. In the meantime, the broker has a different layer of architecture that allows Kafka to be an event streaming platform. The producer, broker, and consumer-related metadata are managed by the Kafka controller, which is elected by ZooKeeper or KRaft. 

]]> Mon, 12 Oct 2026 16:00:00 GMT https://dzone.com/articles/3664523 Sanjoy Kumer Deb https://dzone.com/articles/modern-elt-dbt-databricks A few years back, I sat through a production incident review where the root cause turned out to be a single stored procedure nobody on the team had touched in four years. The original author had left the company. Nobody knew why a particular join filtered out cancelled orders, only that removing it broke three downstream reports. That afternoon summed up, for me, everything wrong with how a lot of organizations still build their data pipelines.

Legacy ETL was never really about "extract, transform, load" as much as it was about hiding logic somewhere convenient at the time and hoping the convenience lasted. It usually didn't.

]]> Mon, 12 Oct 2026 12:00:00 GMT https://dzone.com/articles/3676617 SUNILKUMAR REDDY Eraganeni https://dzone.com/articles/news-openai-chatgpt-codex-watermark-emea-eu ChatGPT responses in Europe may soon carry a signal users cannot see.

OpenAI is rolling out invisible text watermarks for eligible ChatGPT and Codex output in the European Union across all plans. The provenance system is designed to help identify AI-generated text as transparency requirements under the EU AI Act take effect.

]]> Wed, 07 Oct 2026 20:40:41 GMT https://dzone.com/articles/3697480 Liz Ticong https://dzone.com/articles/java-iotdb-time-series Apache IoTDB is well-suited for environments where time-series data from connected devices and industrial systems is generated continuously and requires efficient querying. Typical use cases include monitoring temperature, pressure, vibration, energy usage, machine status, and device telemetry. This approach also applies to manufacturing, smart infrastructure, fleet monitoring, utilities, and edge computing.

IoTDB stands out for its focus on large-scale time-series workloads from devices and industrial systems. It is purpose-built for high-frequency data ingestion, historical analysis, and time-based queries. This makes it ideal for applications that require insight into both the current state and historical trends of devices or processes.

]]> Tue, 06 Oct 2026 16:00:12 GMT https://dzone.com/articles/3688327 Otavio Santana