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In this post, you learn how Smartsheet built a Real-time Dynamic Filtering (RDF) system on Amazon Managed Service for Apache Flink, cutting messaging costs by over $40,000 per mont...
In this post, we explore how Buildkite uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink to power Test Engine’s streaming-firs...
Streaming systems usually fail in one of two ways: Loudly, when infrastructure breaks Quietly, when one bad record keeps replaying until the pipeline is effectively dead T...
Learn how to develop lightweight, high-performance stream processing applications in Java with contemporary frameworks
I've been running Kafka and Spark Structured Streaming together in production for about five years. Not in demo environments or proof-of-concept projects. In systems processing ins...
it's always something you know
This post establishes a reusable pattern for operational workloads that genuinely move the needle: fraud detection...
In this post, we demonstrate how to build a production-ready IoT device monitoring system using Spark 4.0’s transformWithState API on Amazon EMR Serverless. This example showcases...
Why Query Optimization Matters A Spark query written by a human and a Spark query executed by the engine are often very different things. The gap between them — the optimization —...
Apache Spark is an open-source data processing framework for large volumes of data from multiple sources. Spark is used in distributed computing for processing machine learning app...
Apache Spark is one of the most powerful tools in the data and AI engineering world. It helps process massive datasets and is widely used across industries, irrespective of cloud p...
Apache Kafka has become the de facto standard for real-time data streaming and event-driven architectures. But simply adopting Kafka isn’t…Continue reading on Medium »
A Practical Walkthrough Most distributed systems eventually hit a wall with their messaging layer, whether it's Kafka's tight coupling between compute and storage, RabbitMQ's limit...
Schema proliferation builds slowly and gets expensive fast. One schema per event type feels right until there are ten tables, union queries spanning all of them, and a single field...
Why Delta Lake? Apache Parquet on cloud storage was a great first step for data lakes — but it left engineers dealing with a painful set of problems in production: No ACID transa...
In the gaming industry, every millisecond counts. To drive in-game personalization,...
Kafka and Temporal address different failure boundaries, and resilient distributed systems often need both rather than one as a substitute for the other. Kafka is built to move ord...
Core Features pg-cdc is not just replication. pg-cdc streams Postgres Write Ahead Logs(WAL) out of production Postgres into typed, immutable, time-travelable Iceberg tables on S3...
Discover how data engineers ship real-time data pipelines affordably using Snowpipe Streaming and Snowflake CoCo. Start streaming in minutes.
From foundational ETL and analytics to the frontier of generative AI, Apache Spark serves as the architectural backbone for global data processing. However, as data volumes scale,...
The data (r)evolution started with structured data, most strategic decisions and dashboards rely on it. Long before databases and…Continue reading on Medium »
LONDON — Data streaming provider Confluent today announced new capabilities in Confluent Intelligence and Confluent Cloud that streamline how real-time, artificial intelligence (AI...
Google created MapReduce more than 20 years ago to solve the scaling problems in data processing that the then young company was running into. The AI era that we are in now demands...
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