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Causal Consistency for Large Neo4j Clusters
Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems.
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Further Together: Curated Pairing Culture @Pivotal
Neha Batra presents her experience with pair programming at Pivotal Labs. They pair program eight hours/day every workday and help enable other companies to practice it with them.
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Performance and Search
Dan Luu discusses how to estimate performance using back of the envelope calculations that can be done in minutes or hours, even for applications that take months or years to implement.
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Real-Time Recommendations Using Spark Streaming
Elliot Chow discusses the data pipeline that they built with Kafka, Spark Streaming, and Cassandra to process Netflix user activities in real time for the Trending Now row.
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SQL Server on Linux: Will it Perform or Not?
Slava Oks talks about SQL Server’s history, high-level architecture and dives into core of I/O Manager, Memory Manager, and Scheduler. Topics include lessons learned and experiences behind the scenes.
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Automating Chaos Experiments in Production
Ali Basiri discusses the motivation behind ChAP (Chaos Automation Platform), how they implemented it, and how Netflix service teams are using it to identify systemic weaknesses.
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Applying Failure Testing Research @Netflix
Kolton Andrus and Peter Alvaro present how a “big idea” -- lineage-driven fault injection -- evolved from a theoretical model into an automated failure testing service at Netflix.
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Winston: Helping Netflix Engineers Sleep at Night
Sayli Karmarkar discusses Winston, a monitoring and remediation platform built for Netflix engineers.
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Fundamentals of Stream Processing with Apache Beam
Frances Perry and Tyler Akidau discuss Apache Beam, out-of-order stream processing, and how Beam’s tools for reasoning simplify complex tasks.
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Petabytes Scale Analytics Infrastructure @Netflix
Tom Gianos and Dan Weeks discuss Netflix' overall big data platform architecture, focusing on Storage and Orchestration, and how they use Parquet on AWS S3 as their data warehouse storage layer.
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Streaming Live Data and the Hadoop Ecosystem
Oleg Zhurakousky discusses the Hadoop ecosystem – Hadoop, HDFS, Yarn-, and how projects such as Hive, Atlas, NiFi interact and integrate to support the variety of data used for analytics.
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Spring with ApacheNiFi
Oleg Zhurakousky provides a quick introduction to Apache NiFi, demonstrates its core features while concentrating on WHY/WHERE and HOW of integrating with Spring.
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