Delta often performs better for Spark workloads, while Iceberg tends to be stronger for a multi-engine environment. The right choice depends on your platform use case.
When you don't need a real Kafka cluster yet, a lightweight substitute can remove a lot of friction. Use a portable mock environment and wire it into your app.
NB-IoT, Cat-M, and now RedCap all surface the same mass attach problem at scale. Here's what it is, why RedCap doesn't escape it, and what to fix before activation day.
Apache Beam turns ten. From Google's 2015 Dataflow paper to 4 trillion daily events at LinkedIn — what it got right, where it falls short, and what comes next.
Decouple heavy processing with Spring Boot, Kafka, and WebSockets: AI consumers analyze events asynchronously, while WebSockets deliver real-time insights to users.
Classify requests (dashboards vs exploration/jobs), cap and prioritize concurrency, and fall back to cache/rollups so critical dashboards stay responsive during spikes.
CI/CD-driven modernization of data platforms, improving release speed, observability, and reliability through automation, parallelization, and job-level telemetry.
Learn to transform Spring Boot REST APIs into an event-driven architecture by utilizing Kafka, RabbitMQ, or NATS to enhance scalability, resilience, and responsiveness.
Retry transient failures, route poison messages to a DLQ, and deduplicate with a DB table three layers that turn a fragile Kafka consumer into a fault tolerant one.
Platform turning complex smart meter data into usable, real-time insights via APIs — enabling scalable analytics, efficiency, and smarter energy decisions.
Learn how agentic data pipelines go beyond big data to power modern AI workloads with autonomous decision-making, real-time adaptability, and intelligent data.
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