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Apache Doris for Log and Time Series Data Analysis

DZone

As NetEase expands its business horizons, the logs and time series data it receives explode, and problems like surging storage costs and declining stability come. As NetEase's pick among all big data components for platform upgrades, Apache Doris fits into both scenarios and brings much faster query performance.

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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. Performance.

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A guide to Autonomous Performance Optimization

Dynatrace

In my recent Performance Clinic with Stefano Doni , CTO & Co-Founder of Akamas , I made the statement, “Application development and release cycles today are measured in days, instead of months. Increase in environment complexity and increased frequency in delivery requires a novel approach to performance optimization.

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What is software automation? Optimize the software lifecycle with intelligent automation

Dynatrace

Software analytics offers the ability to gain and share insights from data emitted by software systems and related operational processes to develop higher-quality software faster while operating it efficiently and securely. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI.

Software 181
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Spark-Radiant: Apache Spark Performance and Cost Optimizer

DZone

Spark-Radiant is Apache Spark Performance and Cost Optimizer. Spark-Radiant will help optimize performance and cost considering catalyst optimizer rules, enhance auto-scaling in Spark, collect important metrics related to a Spark job, Bloom filter index in Spark, etc. Spark-Radiant is now available and ready to use.

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Auto-Diagnosis and Remediation in Netflix Data Platform

The Netflix TechBlog

This blog will explore these two systems and how they perform auto-diagnosis and remediation across our Big Data Platform and Real-time infrastructure. This has led to a dramatic reduction in the time it takes to detect issues in hardware or bugs in recently rolled out data platform software.

Big Data 238
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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., Finally, we show that Seer can identify application level design bugs, and provide insights on how to better architect microservices to achieve predictable performance. ASPLOS’19.