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Yelp Open-Sources Latest in Data Pipeline Project, Data Pipeline Client Library
Yelp open sources latest component in its data pipeline initiative, a python-based data pipeline client library.
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Microservices and Stream Processing Architecture at Zalando Using Apache Flink
Javier Lopez and Mihail Vieru spoke at Reactive Summit 2016 Conference about cloud-based data integration and distribution platform used for stream processing in business intelligence use cases. Their solution is based on technologies such as Flink, Kafka and Elasticsearch.
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Stream Processing and Lambda Architecture Challenges
Lambda architecture has been a popular solution that combines batch and stream processing. Kartik Paramasivam at LinkedIn wrote about how his team addressed stream processing and Lambda architecture challenges using Apache Samza for data processing. The challenges described are the late arrival of events and the processing of duplicated messages.
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Jay Kreps on Distributed Stream Processing with Apache Kafka and Kafka Streams
Apache Kafka and Kafka Streams frameworks help with developing stream-centric architectures and distributed stream processing applications. Jay Kreps, CEO of Confluent, gave the keynote presentation on stream processing and microservices at Reactive Summit 2016 Conference last week.
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Reactive Summit 2016 Conference: Reactive Microservices and Staging Data Pipelines
Reactive microservices, data center scale operating system (DCOS), and staging reactive data pipelines were the highlighted topics at Reactive Summit 2016 Conference held this week. InfoQ team attended the conference and this post is a summary of the first day's events at the conference.
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Confluent Announces Kafka for the Enterprise with Multi-Datacenter Replication
Confluent Enterprise latest version supports multi-datacenter replication, automatic data balancing, and cloud migration capability. Confluent, provider of the Apache Kafka based streaming platform, announced last week the new features for Confluent Enterprise, to help build streaming data pipelines and develop stream processing applications.
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Neha Narkhede: Large-Scale Stream Processing with Apache Kafka
In her presentation "Large-Scale Stream Processing with Apache Kafka" at QCon New York 2016, Neha Narkhede introduces Kafka Streams, a new feature of Kafka for processing streaming data. According to Narkhede stream processing has become popular because unbounded datasets can be found in many places. It is no longer a niche problem like, for example, machine learning.
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LinkedIn Details Production Kafka Debugging and Best Practices
LinkedIn’s Joel Koshy details their Kafka usage, debugging and monitoring two production incidents in using the core Kafka infrastructure concepts, semantics and behavioral patterns to plan for and detect similar problems in the future.
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Architecting Scalable, Dynamic Systems when Eventual Consistency Won’t Work
Architecting a scalable and dynamic system without caching is explained by Peter Morgan, head of engineering for the sports betting company William Hill. The values of the bets on sporting events change constantly. No data can be cached; all system values must be current. Distributed Erlang processes model domain objects which instantly recalculate system values based on data streams from Kafka.
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Microsoft Expands Azure Machine Learning and Real Time Analytics Offering
Microsoft recently announced new machine learning capabilities for Microsoft Azure platform. Developers can also create their own web services and publish them to Azure Marketplace. Microsoft also announced availability of Apache Storm for Azure. Azure Stream Analytics, Data Factory and Event Hubs for Azure were all announced in the past few weeks by Microsoft. In this article we explore moreabout
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Apache Kafka - A Different Kind of Messaging System
Apache has released Kafka 0.8, the first major release of Kafka since becoming an Apache Software Foundation top level project. Apache Kafka is publish-subscribe messaging implemented as a distributed commit log, suitable for both offline and online message consumption. It is a messaging system initially developed at LinkedIn for collecting and delivering high volumes of event and log data.