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Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Uber Engineering

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs. References.

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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges. Performance.

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Experiences with approximating queries in Microsoft’s production big-data clusters

The Morning Paper

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., Microsoft’s big data clusters have 10s of thousands of machines, and are used by thousands of users to run some pretty complex queries. Five queries improve substantially on both latency and total compute hours.

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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

It provides a good read on the availability and latency ranges under different production conditions. The upstream service calls the existing and new replacement services concurrently to minimize any latency increase on the production path. Logging is selective to cases where the old and new responses do not match.

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Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., on end-to-end latency) and less than 0.15% on throughput. This tracing system is similar to Dapper and Zipkin and records per-microservice latencies and number of outstanding requests. ASPLOS’19.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Upon further profiling, we found that most of the latency came from the candidate generated step (i.e.,

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