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Measuring the importance of data quality to causal AI success

Dynatrace

Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. It starts with implementing data governance practices, which set standards and policies for data use and management in areas such as quality, security, compliance, storage, stewardship, and integration.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage. An additional implication of a lenient sampling policy is the need for scalable stream processing and storage infrastructure fleets to handle increased data volume. Storage: don’t break the bank!

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MySQL Key Performance Indicators (KPI) With PMM

Percona

Number of slow queries recorded Select types, sorts, locks, and total questions against a database Command counters and handlers used by queries give an overall traffic summary Along with this, PMM also comes with Query Analytics giving much detailed information about queries getting executed.

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The Importance of Selecting the Proper Azure VM Size

SQL Performance

IT professionals are familiar with scoping the size of VMs with regards to vCPU, memory, and storage capacity. Memory optimized – High memory-to-CPU ratio, relational database servers, medium to large caches, and in-memory analytics. Storage optimized – High disk throughput and IO. Premium storage support. Generation.

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

PMM2 uses VictoriaMetrics (VM) as its metrics storage engine. Please note that the focus of these tests was around standard metrics gathering and display, we’ll use a future blog post to benchmark some of the more intensive query analytics (QAN) performance numbers.

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HammerDB v4.0 New Features Pt1: TPROC-C & TPROC-H

HammerDB

The HammerDB TPROC-C workload by design intended as CPU and memory intensive workload derived from TPC-C – so that we get to benchmark at maximum CPU performance at a much smaller database footprint. more transactions than system B in the fully audited benchmark then the HammerDB result was also 1.5X I.e. if system A generated 1.5X

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Lerner?—?using RL agents for test case scheduling

The Netflix TechBlog

Netflix engineers run a series of tests and benchmarks to validate the device across multiple dimensions including compatibility of the device with the Netflix SDK, device performance, audio-video playback quality, license handling, encryption and security. It could help us design and implement more targeted reward functions.

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