InfoQ Homepage Streaming Content on InfoQ
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How to Use Open Source Prometheus to Monitor Applications at Scale
In this article, the author discusses how to collect metrics and achieve anomaly detection from streaming data using Prometheus, Apache Kafka and Apache Cassandra technologies.
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Real-Time Data Processing Using Redis Streams and Apache Spark Structured Streaming
Structured Streaming, introduced with Apache Spark 2.0, delivers a SQL-like interface for streaming data. Redis Streams enables Redis to consume, hold and distribute streaming data between multiple producers and consumers. In this article, author Roshan Kumar walks us through how to process streaming data in real time using Redis and Apache Spark Streaming technologies.
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Increasing the Quality of Patient Care through Stream Processing
Today’s healthcare technology landscape is disaggregated and siloed. Physicians analyse patient data streams from different systems without much correlation. Even though health-tech domain is mature and rich with data, the value of it is not directed towards increasing the quality of patient care. This article presents a stream processing solution in which streams are co-related.
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Scaling a Distributed Stream Processor in a Containerized Environment
The article presents our experience of scaling a distributed stream processor in Kubernetes. The stream processor should provide support for maintaining the optimal level of parallelism. However, adding more resources incurs additional cost and also it does not guarantee performance improvements. Instead, the stream processor should identify the level of resource requirement and scale accordingly.
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Apache Kafka: Ten Best Practices to Optimize Your Deployment
Author Ben Bromhead discusses the latest Kafka best practices for developers to manage the data streaming platform more effectively. Best practices include log configuration, proper hardware usage, Zookeeper configuration, replication factor, and partition count.
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Democratizing Stream Processing with Apache Kafka® and KSQL - Part 2
In this article, author Robin Moffatt shows how to use Apache Kafka and KSQL to build data integration and processing applications with the help of an e-commerce sample application. Three use cases discussed: customer operations, operational dashboard, and ad-hoc analytics.
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A Critique of Resizable Hash Tables: Riak Core & Random Slicing
This fall, Wallaroo Labs will be releasing a large new feature set to our distributed data stream processing framework, Wallaroo. One of the new features requires a size-adjustable, distributed data structure to support growing & shrinking of compute clusters. It might be a good idea to use a distributed hash table to support the new feature, but what distributed hash algorithm should we choose?
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How to Choose a Stream Processor for Your App
Stream Processors are software platforms that enable users to respond to incoming data streams faster compared to traditional batch data processing. In this article, the authors discuss the stream processor reference architecture, key features required by most streaming applications and optional features that can be selected based on specific use cases.
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Democratizing Stream Processing with Apache Kafka and KSQL - Part 1
In this article, author Michael Noll discusses the stream processing with KSQL, the streaming SQL engine for Apache Kafka. Topics covered include challenges of stateful stream processing and how KSQL addresses them, and how KSQL helps to bridge the world of streams and databases through streams and tables.
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Migrating Batch ETL to Stream Processing: A Netflix Case Study with Kafka and Flink
At QCon New York, Shriya Arora presented “Personalising Netflix with Streaming Datasets” and discussed the trials and tribulations of a recent migration of a Netflix data processing job from the traditional approach of batch-style ETL to stream processing using Apache Flink.
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Exploring the Fundamentals of Stream Processing with the Dataflow Model and Apache Beam
At QCon San Francisco 2016, Frances Perry and Tyler Akidau presented “Fundamentals of Stream Processing with Apache Beam”, and discussed Google's Dataflow model and associated implementation of Apache Beam.
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Is Batch ETL Dead, and is Apache Kafka the Future of Data Processing?
At QCon San Francisco 2016, Neha Narkhede presented “ETL is Dead; Long Live Streams”, and discussed the changing landscape of enterprise data processing. A core premise of the talk was that the open source Apache Kafka streaming platform can provide a flexible and uniform framework that supports modern requirements for data transformation and processing.